Chapter 4: IEEE 802.15.4 Based Wireless Sensor Network Design for Smart Grid Communications
PERFORMANCE ANALYSIS OF IEEE 802.15.4 BASED WIRELESS ...
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PERFORMANCE ANALYSIS OF IEEE 802.15.4
BASED WIRELESS SENSOR NETWORKS
Sumudu Wijetunge
A thesis submitted for the degree of
Doctor of Philosophy in Engineering
SCHOOL OF COMPUTING, ENGINEERING AND MATHEMATICS
UNIVERSITY OF WESTERN SYDNEY
AUSTRALIA
November 2013
c©Sumudu Wijetunge, 2013
To my Parents and my beloved Wife
DECLARATION
Date: November 2013
Author: Sumudu Wijetunge
Title: PERFORMANCE ANALYSIS OF IEEE 802.15.4 BASED
WIRELESS SENSOR NETWORKS
Degree: Ph.D.
I certify that the work presented in this thesis is, to the best of my knowledgeand belief, original, except as acknowledged in the text, and that the material has notbeen submitted, either in full or in part, for a degree at this or any other institution.
I certify that I have complied with the rules, requirements, procedures and policy
relating to my higher degree research award of the University of Western Sydney.
Author's Signature
ACKNOWLEDGEMENTS
It is with great pleasure that I express my deepest gratitude to my princi-
pal supervisor, Dr Ranjith Liyanapathirana, for his continuous guidance, advice,
encouragement and support. Without his excellent supervision, this dissertation
would not have been completed.
I extend my sincere gratitude to my co-supervisor, Dr. Upul Gunawardana,
who has been a source of generosity, insight and inspiration for all my eorts
during the candidature. I owe my research achievements to his expert guidance.
I would like to thank to my co-supervisor, Dr. Qi Cheng for his support
and valuable advice. My sincere thanks also go to Dr. Xinqun Zhu for giving
insight about structural health monitoring systems and to Dr. Ravi Ranasinghe
for helping to set up the computer simulation platform.
I gratefully acknowledge the University of Western Sydney for granting me the
UWS International Postgraduate Research Scholarship, which was the primary
source of funding for this research. I also appreciate the travel support given
by the School of Computing, Engineering and Mathematics for my attending
national and international conferences.
I am thankful to all technical, administrative and academic sta of School of
Computing, Engineering and Mathematics who directly or indirectly helped me
during my candidature. My gratitude also goes to all my research colleagues for
their support, encouragement and friendship.
I am always grateful to my beloved parents and sisters for their love and
constant support throughout my life. Finally, I cannot express my thanks enough
to my loving wife, Dr. Pushpika Wijesinghe, who has been the reason for all my
success during last six years. Her understanding throughout these years has
meant more than I could ever imagine.
ABSTRACT
This thesis investigates performance of the IEEE 802.15.4 standard in the
context of wireless sensor networks (WSNs) deployed in pervasive monitoring.
IEEE 802.15.4 has been widely acknowledged as the standard physical (PHY)
and medium access control (MAC) layer specications for WSNs due to its simple
protocol stack and low power operation. Mathematical models and computer
simulations were devised to analyse the IEEE 802.15.4 MAC protocol and to
improve its performance under dierent applications and operational conditions.
First, the beacon-enabled mode of the protocol with acknowledgments (ACKs)
was analysed using a discrete time Markov chain (DTMC). The proposed model
provides a generalised platform to evaluate the impact of dierent network and
MAC layer parameters and erroneous channel conditions on the performance of
the protocol. Second, performance of the non-beacon-enabled mode of the pro-
tocol was investigated both with and without ACKs. The outcomes of these
analyses were then used to compare and contrast the performance of two opera-
tional modes. Third, impact of the presence of hidden nodes on the performance
of IEEE 802.15.4 based networks was examined. Using a DTMC model and net-
work simulations, it was shown that the throughput of the protocol is severely
reduced due to increased transmission failures caused by hidden nodes. Finally,
a new hybrid MAC mechanism was proposed to improve the performance of the
IEEE 802.15.4 standard in the context of recently emerged hybrid monitoring ap-
plications. Simulation based experiments show that the proposed hybrid protocol
not only outperforms the standard protocol, but also provides a reliable, energy
ecient and delay limited transmission mechanism for hybrid monitoring WSNs.
This thesis has provided a platform for further studies on the performance
analysis and application specic tailoring of the IEEE 802.15.4 standard in the
context of WSNs.
Contents
Acknowledgement iii
Abstract iv
Contents v
Abbreviations xi
Notation xiv
List of Figures xvii
List of Tables xxi
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Major Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Thesis Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 MAC Protocols for WSNs 12
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Wireless MAC Protocols . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Fixed Assignment Mechanism . . . . . . . . . . . . . . . . 13
2.2.2 Demand Assignment Mechanism . . . . . . . . . . . . . . . 14
CONTENTS vi
2.2.3 Random Access Mechanism . . . . . . . . . . . . . . . . . 14
2.3 MAC Protocols for WSNs . . . . . . . . . . . . . . . . . . . . . . 16
2.3.1 Energy Wastage in MAC Layer . . . . . . . . . . . . . . . 16
2.3.2 Schedule based Protocols . . . . . . . . . . . . . . . . . . . 17
2.3.3 Contention based Protocols . . . . . . . . . . . . . . . . . 18
2.4 Overview of IEEE 802.15.4 Standard . . . . . . . . . . . . . . . . 19
2.4.1 Device Types and Network Topologies . . . . . . . . . . . 20
2.4.2 PHY Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.4.3 MAC Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4.3.1 Beacon-Enabled Mode . . . . . . . . . . . . . . . 24
2.4.3.2 Non-beacon-Enabled Mode . . . . . . . . . . . . 28
2.5 Performance Evaluation of MAC Protocols . . . . . . . . . . . . . 30
2.5.1 Analytical Models . . . . . . . . . . . . . . . . . . . . . . . 30
2.5.2 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3 Analysis of Beacon-enabled IEEE 802.15.4 MAC Protocol with
ACK Transmission 35
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.2.1 Approximations . . . . . . . . . . . . . . . . . . . . . . . . 39
3.3 Analytical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3.1 Node State Model . . . . . . . . . . . . . . . . . . . . . . . 40
3.3.2 Channel State Model . . . . . . . . . . . . . . . . . . . . . 44
3.4 Simplied Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.4.1 Node State Model . . . . . . . . . . . . . . . . . . . . . . . 48
3.4.2 Channel State Model . . . . . . . . . . . . . . . . . . . . . 49
3.5 Extended Model for Networks with Erroneous Channels . . . . . . 50
3.5.1 Channel Error Model . . . . . . . . . . . . . . . . . . . . . 51
CONTENTS vii
3.5.2 Node State Model . . . . . . . . . . . . . . . . . . . . . . . 52
3.5.3 Modied Channel State Model . . . . . . . . . . . . . . . . 52
3.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . 54
3.6.1 Aggregate Network Throughput . . . . . . . . . . . . . . . 54
3.6.2 Average Power Consumption . . . . . . . . . . . . . . . . . 55
3.6.3 Data Transmission Reliability . . . . . . . . . . . . . . . . 57
3.7 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 59
3.7.1 Validation of Analysis . . . . . . . . . . . . . . . . . . . . 59
3.7.2 Eects of Network Parameters . . . . . . . . . . . . . . . . 62
3.7.3 Eects of MAC-Layer Parameters . . . . . . . . . . . . . . 65
3.7.4 Eects of Channel Errors . . . . . . . . . . . . . . . . . . . 70
3.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4 Analysis of Non-beacon-enabled IEEE 802.15.4 MAC Protocol 73
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.2 Collision of Transmissions . . . . . . . . . . . . . . . . . . . . . . 75
4.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.3.1 Approximations . . . . . . . . . . . . . . . . . . . . . . . . 79
4.4 Analytical Model without ACKs . . . . . . . . . . . . . . . . . . . 81
4.4.1 Node State Model . . . . . . . . . . . . . . . . . . . . . . . 81
4.4.2 Channel State Model . . . . . . . . . . . . . . . . . . . . . 84
4.5 Analytical Model with ACKs . . . . . . . . . . . . . . . . . . . . 88
4.5.1 Node State Model . . . . . . . . . . . . . . . . . . . . . . . 88
4.5.2 Channel State Model . . . . . . . . . . . . . . . . . . . . . 91
4.6 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . 94
4.6.1 Aggregate Network Throughput . . . . . . . . . . . . . . . 95
4.6.2 Average Power Consumption . . . . . . . . . . . . . . . . . 96
4.6.3 Data Transmission Reliability . . . . . . . . . . . . . . . . 97
4.7 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 98
CONTENTS viii
4.7.1 Validation of Analysis . . . . . . . . . . . . . . . . . . . . 99
4.7.2 Impact of Network and MAC-layer Parameters . . . . . . . 105
4.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5 Throughput Analysis of IEEE 802.15.4 MAC Protocol in the
Presence of Hidden Nodes 111
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.2.1 Node Grouping . . . . . . . . . . . . . . . . . . . . . . . . 115
5.3 Network Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.3.1 Analysis of Individual Groups . . . . . . . . . . . . . . . . 116
5.3.2 Analysis of the Common Channel . . . . . . . . . . . . . . 118
5.4 Simplied Analysis for Networks with Uniform Groups . . . . . . 124
5.5 Throughput Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.6 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 125
5.6.1 Validation of Analytical Results . . . . . . . . . . . . . . . 126
5.6.2 Impact of Dierent Network Parameters on Throughput . 127
5.6.3 Approximating Throughput of Generic Networks . . . . . 131
5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
6 IEEE 802.15.4 based MAC Protocol for Hybrid MonitoringWSNs139
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
6.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
6.2.1 QoS Requirements of Hybrid Monitoring Application . . . 143
6.3 New MAC Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . 144
6.3.1 MAC Mechanism for LTPM Data Transmission . . . . . . 144
6.3.2 MAC Mechanism for ED Data Transmission . . . . . . . . 147
6.3.2.1 Improving Data Transmission Reliability of net-
works with synchronised trac . . . . . . . . . . 148
6.4 Hybrid Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
CONTENTS ix
6.4.1 Initial Phase . . . . . . . . . . . . . . . . . . . . . . . . . . 152
6.4.2 Steady Phase . . . . . . . . . . . . . . . . . . . . . . . . . 157
6.5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 160
6.5.1 Experiment 1 - Performance Evaluation of the DTS Mech-
anism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
6.5.2 Experiment 2 - Performance Evaluation of the Randomly-
delayed CSMA/CA Mechanism . . . . . . . . . . . . . . . 164
6.5.3 Experiment 3 - Performance Evaluation of the Hybrid Pro-
tocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
6.5.4 Experiment 4 - Network Scalability of the Hybrid Protocol 173
6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
7 Conclusion 178
7.1 Summary and Conclusion . . . . . . . . . . . . . . . . . . . . . . 178
7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
References 182
Appendices 204
A Steady State Transition Equations of Node Model DTMCs 204
A.1 Beacon-enabled IEEE 802.15.4 - Analytical Model . . . . . . . . . 204
A.2 Beacon-enabled IEEE 802.15.4 - Simplied Model . . . . . . . . . 206
A.3 Non-beacon-enabled IEEE 802.15.4 with ACK Transmission . . . 207
B Fractions of Time Spent by a Node in Dierent Transceiver Ac-
tivities 208
B.1 Analysis of Beacon-enabled IEEE 802.15.4 . . . . . . . . . . . . . 208
B.2 Analysis of Non-beacon-enabled IEEE 802.15.4 . . . . . . . . . . . 209
C Modications to ns-2.34 Simulator 211
CONTENTS x
C.1 Modications to CCA Procedure . . . . . . . . . . . . . . . . . . 211
C.2 Implementation of Hybrid Protocol . . . . . . . . . . . . . . . . . 213
D Supportive Calculations and Algorithm Descriptions 217
D.1 Quantifying δ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
D.2 Algorithms for Networks Deployed Only in LTPM Applications . 220
D.3 Computation of Initialising-Parameters . . . . . . . . . . . . . . . 221
Abbreviations
ns-2 network simulator - 2
ACK acknowledgment
B-MAC Berkeley MAC
BPSK binary phase-shift keying
CAP contention access period
CBR continuous bit rate
CCA clear channel assessment
CDMA code division multiple access
CFP contention free period
COTS commercial-o-the-shelf
CSMA carrier sense multiple access
CSMA/CA carrier sense multiple access/collision avoidance
CSMA/CD carrier sense multiple access/collision detection
CTS clear to send
DCF distributed coordination function
DEE-MAC dynamic energy ecient MAC
DLL data link layer
DSSS direct sequence spread spectrum
DTMC discrete time Markov chain
ABBREVIATIONS xii
DTS dedicated time slots
ED event detection
EPA equilibrium point analysis
FDMA frequency division multiple access
FFD full-function device
GTS guaranteed time slot
HUA hybrid unied-slot access
IFS interframe spacing
LEACH low energy adaptive clustering hierarchy
LLC logical link control
LQI link quality indication
LTPM long-term periodic monitoring
MAC medium access control
MACA multiple access with collision avoidance
MANET mobile ad-hoc network
MEMS micro electromechanical sensors
MH-MAC mobility adaptive hybrid MAC
O-QPSK oest quadrature phase-shift keying
OSI open systems interconnection
PAMAS power aware multi access with signalling
PHY physical
PMAC pattern MAC
PSSS parallel sequence spread spectrum
QoS quality of service
RF radio frequency
RFD reduced-function device
ABBREVIATIONS xiii
RTS request to send
RX-to-TX receive to transmit
S-MAC sensor-MAC
SHM structural health monitoring
SMACS self organising MAC for sensor networks
SSCS service specic convergence sublayer
T-MAC timeout MAC
TDMA time division multiple access
TRAMA trac adaptive medium access
TUA tagged user analysis
WBAN wireless body area network
WLAN wireless local area network
WPAN wireless personal area network
WSN wireless sensor network
Z-MAC zebra MAC
Notation
BEy backo exponent of the yth backo stage
BI beacon interval
BImin−nwk minimum BI value the network can operate
BInwk beacon interval of the network
BO beacon order
Dcsma maximum delay caused by CSMA/CA mechanism
Dmax upper bound of the delay in ED data transmission
Drnd maximum value of the random delay
EDrate event detection rate
K number of non-overlapping groups in the network
L data frame length in backo slots
Lack ACK frame length in backo slots
Led ED data frame length in backo slots
Lltpm LTPM data frame length in backo slots
N number of nodes in the network
Nmax−ltpm maximum number of nodes allowed to associate with the network when
only LTPM application exists
Nmax maximum number of nodes allowed to associate with the network
Pdiscard probability of frame discarding
Perr−data probability of having channel errors during data transmission
NOTATION xv
R reliability of data transmission (or reliability factor)
S aggregate network throughput
SD superframe duration
SO superframe order
Tm monitoring period for a given LTPM instance
Tcycle periodicity of LTPM instances
Tdts DTS length
Tinit initial value of the countdown timer
Trpt reporting cycle of LTPM data
Yav average power consumption of a node
α probability of none of the node begin data transmission
β probability of only one node begins data transmission
δ proportional constant of Drnd
η frame delivery ratio
γ probability of none of the remaining nodes begin data transmission
S fraction of time the channel spent in successful data transmission
λ frame arrival rate
N natural numbers
π(statei) long term proportion of transitions into statei
ρ frame discard ratio
σ probability of more than one node begin data transmission
x maximum number of transmission attempts
y maximum number of backing o stages
d delay constraint in ED data transmission
havg average number of hidden-nodes-per-node of the network
m number of LTPM data frames generated in a node during Tm period
NOTATION xvi
nj number of nodes in Group j
ndts number of DTSs required for a node to transmit LTPM data during Trpt
nltpm number of LTPM data frames transmitted within the DTS
p frame arrival probability
pci|i conditional probability of the channel being idle in the next backo slot
given that it is idle in the current backo slot
pny geomatircal parameter that represents the number of backo slots a node
dwells in the yth backo stage
pnt|ii conditional probability of any node begins transmission given that the
channel has been idle for two consecutive backo slots
pci steady state probability of channel idleness
pnt steady state probability of a node begins transmission
pncca steady state probability of a node begins CCA
q probability of receiving ACK after a data transmission
sdack starting delay of ACK transmission
tack waiting time for a ACK frame
tbcn beacon duration
tltpm transmission duration of a single LTPM data frame (including IFS)
vb transition probability from bad channel state to good channel state
vg transition probability from good channel state to bad channel state
List of Figures
2.1 IEEE 802.15.4 standard within OSI seven-layer model. . . . . . . 20
2.2 IEEE 802.15.4 network topologies: (a) Star (b) Peer-to-peer (c)
Cluster-tree, a complex network based on peer-to-peer topology. . 21
2.3 IEEE 802.15.4 MAC protocol: Channel access mechanisms [62]. . 23
2.4 IEEE 802.15.4 superframe structure. . . . . . . . . . . . . . . . . 25
2.5 IEEE 802.15.4 slotted CSMA/CA mechanism. . . . . . . . . . . . 26
2.6 IEEE 802.15.4 unslotted CSMA/CA mechanism. . . . . . . . . . . 29
3.1 3D-DTMC model of node. Probability pci and pci|i are denoted by
u and v, respectively. . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2 Discrete-time Markov chain model of channel. . . . . . . . . . . . 45
3.3 Discrete-time Markov chain for node in simplied model. . . . . . 49
3.4 Gilbert-Elliot channel error model. . . . . . . . . . . . . . . . . . 51
3.5 DTMC model for burst error channel. . . . . . . . . . . . . . . . . 53
3.6 Energy states and transitions of CC2420 transceiver [126][149]. . . 56
3.7 Performance of beacon-enabled IEEE 802.15.4 networks with and
without ACK transmission (N = 10 and L = 10 backo slots). . . 60
3.8 Eects of frame length L on the performance of beacon-enabled
IEEE 802.15.4 networks (when N = 10). . . . . . . . . . . . . . . 63
3.9 Eects of number of nodesN on the performance of beacon-enabled
IEEE 802.15.4 networks (when L = 10 backo slots). . . . . . . . 64
LIST OF FIGURES xviii
3.10 Eects of macMaxFrameRetries on the performance of beacon-
enabled IEEE 802.15.4 networks. . . . . . . . . . . . . . . . . . . 66
3.11 Eects of macMaxCSMABackos on the performance of beacon-
enabled IEEE 802.15.4 networks. . . . . . . . . . . . . . . . . . . 67
3.12 Eects of the backo window length on the performance of beacon-
enabled IEEE 802.15.4 networks. . . . . . . . . . . . . . . . . . . 69
3.13 Eects of channel errors on the performance of beacon-enabled
IEEE 802.15.4 networks (N = 10 and L = 10 backo slots). . . . . 71
4.1 Collision of transmissions: slotted CSMA/CA vs. unslotted CSMA/CA. 76
4.2 Timing of starting the same event in dierent protocols. . . . . . 80
4.3 DTMC model for node without ACKs. The steady state probabil-
ity pci is denoted by a . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.4 DTMC model for channel without ACKs. . . . . . . . . . . . . . . 85
4.5 DTMC model for node with ACKs. The steady state probability
pci is denoted by a . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.6 DTMC model for channel with ACKs. . . . . . . . . . . . . . . . 91
4.7 Behaviour of basic model probabilities (N = 10 and L = 10 backos).100
4.8 Performance of IEEE 802.15.4 based networks (N = 10 and L =
10): (a) Aggregate network throughput S (b) Average power con-
sumption per node Yav. . . . . . . . . . . . . . . . . . . . . . . . . 102
4.9 Performance of IEEE 802.15.4 based networks (N = 10 and L =
10): (a) Frame discard ratio ρ (b) Frame delivery ratio η. . . . . . 103
4.10 Number of collisions in slotted and unslotted protocols. . . . . . . 104
4.11 Eects of frame length L on the performance of non-beacon-enabled
IEEE 802.15.4 networks without ACKs (N = 10). . . . . . . . . . 106
4.12 Eects of number of nodes N on the performance of non-beacon-
enabled IEEE 802.15.4 networks without ACKs (L = 10 backo
slots). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
LIST OF FIGURES xix
4.13 Eects ofmacMaxFrameRetries on the performance of non-beacon-
enabled IEEE 802.15.4 networks with ACKs. . . . . . . . . . . . . 108
4.14 Eects ofmacMaxCSMABackos on the performance of non-beacon-
enabled IEEE 802.15.4 networks with ACKs. . . . . . . . . . . . . 109
4.15 Eects of the backo window length on the performance of non-
beacon-enabled IEEE 802.15.4 networks with ACKs. . . . . . . . 109
5.1 Hidden node problem in a single-hop star-topology network. . . . 112
5.2 Example network [4,6,8] with node grouping (K = 3). . . . . . . . 115
5.3 DTMC model for the common channel. . . . . . . . . . . . . . . . 119
5.4 Transition from SUCCL to SUCCL−1 state. . . . . . . . . . . . . . 120
5.5 Normalised aggregate network throughput S of dierent networks
with hidden nodes. . . . . . . . . . . . . . . . . . . . . . . . . . . 126
5.6 Normalised aggregate network throughput S for varying network
parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
5.7 Normalised aggregate network throughput S for [4,8,12] network
when dierent groups generate frames at dierent rates. . . . . . . 130
5.8 [8,8] network and its relaxed node grouping congurations. . . . . 132
5.9 [8,8,8] network and its relaxed node grouping congurations. . . . 133
5.10 Normalised aggregate network throughput S of networks with re-
laxed node grouping. . . . . . . . . . . . . . . . . . . . . . . . . . 134
5.11 Dierent congurations of 16-node-network (N = 16) with dier-
ent average number of hidden nodes havg. . . . . . . . . . . . . . . 135
5.12 Dierent congurations of 24-node-network (N = 24) with dier-
ent average number of hidden nodes havg. . . . . . . . . . . . . . . 136
5.13 S of dierent network congurations with varying havg: (a) 16-
node-network (N = 16) and (b) 24-node-network (N = 24). . . . . 136
5.14 Validation of proposed technique. . . . . . . . . . . . . . . . . . . 137
6.1 Hybrid monitoring scenario. . . . . . . . . . . . . . . . . . . . . . 143
LIST OF FIGURES xx
6.2 DTS mechanism. . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
6.3 Hybrid MAC mechanism. . . . . . . . . . . . . . . . . . . . . . . . 151
6.4 SuperBeacon payload. . . . . . . . . . . . . . . . . . . . . . . . . 156
6.5 Reliability in LTPM data transmission. . . . . . . . . . . . . . . . 163
6.6 Power consumption in LTPM data transmission. . . . . . . . . . . 164
6.7 Performance of the randomly-delayed CSMA/CA mechanism in
ED data transmission: (a) reliability R (b) maximum delay. . . . 166
6.8 Data transmission reliability of hybrid protocol: (a) LTPM data
transmission with varying EDrate (b) ED data transmission with
varying m. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
6.9 Power consumption of hybrid protocol (m = 2400). . . . . . . . . 171
6.10 Maximum delay in ED data transmission. . . . . . . . . . . . . . 172
6.11 Network scalability of hybrid protocol (in terms of maximum num-
ber of nodes allowed to form the network Nmax). . . . . . . . . . . 175
C.1 Timing of CCA procedure in dierent scenarios. . . . . . . . . . . 212
D.1 Normalised dierence in data transmission reliability ∆R with
varying δ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
List of Tables
2.1 IEEE 802.15.4 PHY layer: Frequency bands and data rates. . . . 22
2.2 IEEE 802.15.4 MAC-layer parameters. . . . . . . . . . . . . . . . 27
3.1 Relationships between model variables and MAC-layer parameters. 43
3.2 States of the CC2420 transceiver: Beacon-enabled mode. . . . . . 56
3.3 MAC-layer parameter values for dierent investigations. . . . . . . 66
4.1 Node states and their dwell times. . . . . . . . . . . . . . . . . . . 82
4.2 States of the CC2420 transceiver: Non-beacon-enabled mode. . . . 96
5.1 Probabilities αj, βj, A, B, C and E for the network shown in
Figure 5.2 when L = 10. . . . . . . . . . . . . . . . . . . . . . . . 118
6.1 Simulation parameters. . . . . . . . . . . . . . . . . . . . . . . . . 161
6.2 Sensor measurements and monitoring application requirements. . 167
6.3 DTS-scheduling parameters Tdts, ndts, and BInwk (when Trpt = 30
min.). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
B.1 Fractions of time spent by a node in dierent transceiver activities
in beacon-enabled networks. . . . . . . . . . . . . . . . . . . . . 209
B.2 Fractions of time spent by a node in dierent transceiver activities
in non-beacon enabled networks. . . . . . . . . . . . . . . . . . . 209
C.1 Impelemetation of hybrid protocol in ns-2 . . . . . . . . . . . . . 214
Chapter 1
Introduction
Sensing, observing and controlling the neighbouring environment has been con-
sidered as one of the key requirements of humans from the beginning of mankind.
Ever increasing demand of this requirement has led to the development of ad-
vanced sensor-actuators to sense and control our surroundings. However, the ex-
istence of complex monitoring applications that cannot be addressed by a single
sophisticated sensor-actuator (e.g., battle eld monitoring, environmental moni-
toring and structural health monitoring) demands a new paradigm for automated
sensing and controlling. With recent advances in electronic and computer engi-
neering, a technology known as Sensor Networks has emerged to form networks
of sensors to fulll the requirements posed by complex monitoring applications
[1].
A sensor network is a group of transducers that monitor and record conditions
of various physical phenomena such as temperature, pressure, wind speed, and
chemical concentration in a given environment. The basic building block of a
sensor network is the sensor node, which comprises of several micro electrome-
chanical sensors (MEMS), a micro computer and a transceiver [2]-[4]. To perform
as a network, sensor nodes should be able to communicate in addition to their
primary tasks of sensing and computing. Currently, most of the sensor networks
utilise existing wired technologies to form the communication network. However,
1. INTRODUCTION 2
manipulating a wired network is cumbersome and inherently associated with the
hassles of installation and maintenance apart from the cost of cabling. On the
other hand, wireless networks can be easily deployed and maintained regardless
of the scale of the network and are cost eective compared with wired networks
[4][5]. These advantages along with the recent technological advances and ever
growing list of potential applications [6]-[9] have paved the way to build sensor
networks that communicate wirelessly.
Wireless operation, despite being the prime strength of wireless sensor net-
works (WSNs), poses new challenges: Latency and security of communication,
fair access to the medium, eective routing and scalability of the network are a
few among others. Moreover, wireless sensor nodes have to rely completely on
built-in energy sources as they cannot be fed externally due to their untethered
nature. Making the situation worse, replacing or renewing the self contained
energy sources in sensor nodes has been generally considered impractical or too
costly [2]-[4]. Therefore, one of the major challenges in WSNs is the scarcity of
energy used for sensing, processing (computation) and communication.
Among these three major functions in wireless sensor nodes, communication
has been considered the largest energy consumer [10]-[13]. According to Karl and
Willig [14] the ratio of energy consumption for communication to computation of
a single bit is about 190. Therefore, energy ecient communication is essential
to achieve the overall energy eciency in WSNs. The obvious solution to achieve
the energy eciency in communication is improving the energy characteristics of
the underlying radio. Apart from that, introducing ecient data transmission
and reception mechanisms to a given radio may also enhance the energy per-
formance of a wireless sensor node signicantly [15]. Thus, the medium access
control (MAC) protocol which manages the data transceiver operation is a vital
contributor for energy ecient operation in WSNs.
In general, the MAC protocols in traditional wireless networks such as wire-
less local area networks (WLANs) and mobile ad-hoc networks (MANETs) focus
1. INTRODUCTION 3
on delivering high channel throughput (i.e., channel utilisation), low latency in
transmissions and fairness in medium access; however, have little or no consider-
ation for energy conservation [10]. In contrast, WSNs require protocols that can
provide the best performance with the minimum energy consumption [11]-[17].
Due to this fundamental dierence and the unique characteristics of WSNs includ-
ing scalability of network and limited resources, the MAC protocols designed for
traditional wireless networks are inapt in the context of WSNs [18]-[21]. There-
fore, there exists a requirement for developing an improved set of MAC protocols
in order to harness the full potential of WSN technology.
Following the introduction of the well-known sensor-MAC (S-MAC) [19], a
wide range of MAC protocols that meet the unique requirements of WSNs has
been developed; WiseMAC [22], DMAC[23], Sift [21] and trac adaptive medium
access (TRAMA) [12] are a few to name. Each of these MAC protocols is spe-
cially tailored for a certain set of WSN applications to deliver their optimum
performance. The application-specic nature of these protocols hinders the inter-
operability among them, and hence creates a huge barrier to the successful com-
mercial launching of WSN technology [24][25]. This raises the necessity of a
standard for the MAC protocols used in WSNs [26].
Due to its simplicity and low power operation, the IEEE 802.15.4 standard
[27], which denes the physical (PHY) and MAC layer specications for low
power, low-data-rate wireless personal area networks (WPANs), has been widely
acknowledged as the state-of-the-art standard for MAC protocols in WSNs. This
standard has already been implemented in most of the commercial sensor nodes
including MicaZ [28], IMote2 [29] and TelosB [30]. It also serves as the basis for
almost all commercial WSNs standards including Zigbee [31], ISA 100.11a [32]
and WirelessHART[33], acclaiming itself as the standard for the PHY/MAC layer
protocols in WSNs.
The IEEE 802.15.4 MAC protocol operates in two dierent modes: the beacon-
enabled mode and the non-beacon-enabled mode. The beacon-enabled mode is
1. INTRODUCTION 4
utilised to deploy synchronised sensor networks, while the non-beacon-enabled
mode is used in non-synchronised sensor networks. Whether it is synchronised or
non-synchronised, performance of a sensor network highly depends on the per-
formance of the underlying MAC protocol. Performance of the IEEE 802.15.4
MAC protocol is generally evaluated using mathematical modelling, computer
simulations and real world experiments. Outcomes of such analyses aid devel-
opers to predict the behaviour of a particular WSN before the deployment, and
hence to minimise time and cost associated with the design and development
process. Furthermore, the insight gained from these investigations would provide
design guidelines for researchers and developers to deploy better WSNs in future.
The aim of this thesis is to develop a platform for such analyses (modelling and
computer simulations) on the performance of the IEEE 802.15.4 MAC protocol.
1.1 Motivation
Performance of the IEEE 802.15.4 MAC protocol has been evaluated by analyt-
ical methods, simulations and experiments. The reliability in data transmission
has not been considered as a critical measure in most of the existing analyses,
and therefore, the protocol has been analysed by overlooking the MAC level ac-
knowledgment (ACK) and frame retransmission. However, existence of reliability
critical WSN applications such as military survivance [7] and chemical agent de-
tection [34] questions the applicability of the existing analyses that overlook ACK
frame transmission. Thus, the lack of knowledge of the performance of the IEEE
802.15.4 MAC protocol with ACK frame transmission under both operational
modes (i.e., the beacon-enabled and non-beacon-enabled) provides a strong mo-
tivation for the subject of this thesis.
Due to its interesting features such as the superframe structure and guaran-
teed time slots (GTSs), the beacon-enabled mode has attracted the attention of
the research community over the non-beacon-enabled mode. Consequently, most
1. INTRODUCTION 5
of the existing analyses are focused on the beacon-enabled mode of the protocol.
However, the non-beacon-enabled mode is equally deployed in practical WSNs
as its counterpart with beacons, and it is the only mode that can facilitate the
decentralised communication occurring in many event monitoring WSNs. Hence,
analysing the non-beacon-enabled mode is an important contribution to the com-
plete performance evaluation of the IEEE 802.15.4 MAC protocol.
Generally, WSNs operate under harsh environments and over a considerably
large geographical area [1][3]. Thus, highly error-prone channels and the presence
of hidden nodes are familiar scenarios for practical WSN deployments. However,
most of the existing analyses appear to overlook these important physical and
system level considerations associated with WSNs and derive performance of
the protocol by assuming ideal conditions. Therefore, lack of studies on the
performance of the IEEE 802.15.4 MAC protocol under non-ideal operational
conditions strongly motivates to carry out this study.
WSNs are deployed mainly in two dierent monitoring scenarios: periodic
monitoring and event detection [7]. In general, a given WSN serves only for one
monitoring scenario but not for both; however, emerging applications with hybrid
monitoring (e.g., structural health monitoring (SHM) [35]) demand WSNs that
can support both monitoring scenarios simultaneously. Given that the IEEE
802.15.4 MAC protocol in many current WSNs has been adjusted for the un-
derlying monitoring application [36]-[38], it would be interesting to modify the
protocol to meet the unique data transmission requirements of emerging hybrid
monitoring WSNs.
Thus, the main research objective of this thesis is to investigate the per-
formance of the IEEE 802.15.4 MAC protocol deployed in dierent WSNs that
operate under real-world conditions and applications.
1. INTRODUCTION 6
1.2 Major Contributions
The work reported in this thesis has resulted in several contributions to the eld
of analysis and design of MAC protocols in the context of WSNs.
The major contributions are as follows:
• A comprehensive review of existing MAC protocols for WSNs and per-
formance evaluation techniques for communications protocols is presented.
MAC layer specications of the IEEE 802.15.4 standard is identied as the
state-of-the-art MAC protocol for WSNs. Markov Chain based analytical
models and network simulator - 2 (ns-2) based simulations are recognised
as potential performance evaluation techniques for the MAC protocols used
in WSNs.
• Performance of the beacon-enabled mode of the IEEE 802.15.4 MAC with
ACK frame transmission is evaluated using a discrete time Markov chain
(DTMC) based model. An extension to the proposed model is presented
to analyse the protocol under non-ideal channel conditions. Generality of
the proposed model is exploited to investigate the impact of dierent net-
work and MAC-layer parameters on the performance of the protocol. The
proposed models are validated using extensive ns-2 simulations.
• Performance of the non-beacon-enabled mode of the IEEE 802.15.4 MAC
protocol is investigated. A Markov chain based analysis is presented to anal-
yse the non-beacon-enabled IEEE 802.15.4 protocol without ACK frame
transmissions. The analysis is then extended to evaluate the performance
of the protocol with ACK frame transmission. Analytical results obtained
from these models and the models proposed for the beacon-enabled mode
are used to compare and contrast the performance of two operation modes
of the IEEE 802.15.4 MAC protocol.
1. INTRODUCTION 7
• Impact of the presence of hidden nodes on the performance of the beacon-
enabled mode of the IEEE 802.15.4 MAC protocol is investigated. A DTMC
based analytical model is proposed to model the wireless channel seen by the
common receiver, and consequently, to derive the aggregate throughput of
the network. Analytical results and simulations reveal a signicant impact
of average number of hidden nodes on the throughput performance of a
given network.
• IEEE 802.15.4 compliant two new MAC mechanisms are proposed to meet
the quality of service (QoS) requirements of dierent monitoring scenarios.
By carefully merging these mechanisms, a hybrid MAC protocol is devel-
oped to transmit data eciently in hybrid monitoring WSNs. The new
hybrid protocol is implemented on ns-2 simulation platform. Simulation
results show that the proposed hybrid protocol outperforms the standard
IEEE 802.15.4 MAC in terms of reliability and energy eciency, while sat-
isfying the stringent delay requirements in data transmission.
1.3 Publications
The following collection of papers, which has been published in, accepted by or
submitted to peer-reviewed journals or conferences, presents the contribution of
this thesis.
1. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `Performance
Analysis of IEEE 802.15.4 MAC Protocol with ACK Frame Transmission',
Accepted for publication in the Wireless Personal Communications, ISSN:
1572-834X (electronic version).
2. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `Throughput Anal-
ysis of IEEE 802.15.4 MAC Protocol in the Presence of Hidden Nodes',
1. INTRODUCTION 8
Accepted for publication in Wireless Networks, ISSN: 1572-8196 (electronic
version).
3. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `Performance
Analysis of Non-Beacon-Enabled IEEE 802.15.4 MAC Protocol', Submitted
to Wireless Personal Communications, ISSN: 1572-834X (electronic ver-
sion).
4. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `An IEEE 802.15.4
based MAC Protocol for WSNs deployed in Hybrid Monitoring Applica-
tions', Submitted to International Journal of Wireless Information Net-
works, ISSN: 1572-8129 (electronic version).
5. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `An IEEE 802.15.4
based hybrid MAC Protocol for Hybrid Monitoring WSNs', in Proceedings
of the 38th IEEE Conference on Local Computer Networks (LCN), Oct.
2013, Sydney, Australia.
6. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `Throughput Anal-
ysis of Non-Beacon-Enabled IEEE 802.15.4 Networks with Unsaturated
Trac ', in Proceedings of the 12th International Symposium on Communi-
cations and Information Technologies (ISCIT) 2012, Gold Coast, Australia.
7. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `Investigation of
Data Transmission Reliability of IEEE 802.15.4 based Wireless Sensor Net-
works with Synchronised Periodic Data', in Proceedings of the International
Conference on Computer and Information Sciences (ICCIS) 2012, Kuala
Lumpur, Malaysia, pp.619-624.
8. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `Impact of MAC
parameters on the performance of IEEE 802.15.4 MAC protocol with ACK
Frame Transmission', in Proceedings of the Australian Telecommunication
1. INTRODUCTION 9
Networks and Applications Conference (ATNAC), Nov. 2011, Melbourne,
Australia. (Won one of the competitive travel grants)
9. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `Performance
Analysis of IEEE 802.15.4 MAC protocol for WSNs in Burst Error Chan-
nels', in Proceedings of the 11th International Symposium on Communica-
tions and Information Technologies (ISCIT), Oct. 2011, Hangzhou, China,
pp.286-291. (Received the ISCIT2011 Best Paper Award)
10. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `Throughput Anal-
ysis of IEEE 802.15.4 MAC Protocol in the Presence of Hidden Nodes', in
Proceedings of the 11th International Symposium on Communications and
Information Technologies (ISCIT), Oct. 2011, Hangzhou, China, pp.303-
308.
11. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, 'Performance
Analysis of IEEE 802.15.4 MAC protocol for WSNs with ACK Frame Trans-
mission under Unsaturated Trac Conditions', in Proceedings of the Sixth
International Conference on Intelligent Sensors, Sensor Networks and In-
formation Processing (ISSNIP), Dec. 2010 , Brisbane, Australia, pp.55-60.
12. S. Wijetunge, U. Gunawardana, and R. Liyanapathirana, `Wireless Sensor
Networks for Structural Health Monitoring: Considerations for communica-
tion protocol design', in Proceedings of the IEEE 17th International Confer-
ence on Telecommunications (ICT), Apr. 2010, Doha, Qatar, pp.694-699.
1.4 Thesis Organisation
The remainder of the thesis is organised as follows:
In Chapter 2, the requirement of having energy ecient MAC protocols for
WSNs is elaborated by discussing the potential sources of energy wastage in
1. INTRODUCTION 10
communications. A comprehensive review that leads to a classication of existing
MAC protocols is presented. The IEEE 802.15.4 standard, which species the
PHY and MAC layer specications for low-power, low-data-rate applications,
is identied as the state-of-the-art MAC protocol for WSNs. An overview of
the IEEE 802.15.4 MAC protocol is presented by specifying its two dierent
operational modes: beacon-enabled mode and non-beacon-enabled mode. The
literature of modelling wireless MAC protocols is reviewed, and the Markov chain
analysis is recognised as one of the potential analytical technique to model the
IEEE 802.15.4 MAC protocol. Further, a comprehensive summary of existing
computer simulators used for MAC protocol simulations in the context of WSNs
is provided.
Chapter 3 presents a DTMC based analysis to model the beacon-enabled
mode of the IEEE 802.15.4 MAC protocol with ACK frame transmissions. Per-
formance of the protocol is evaluated in terms of the network throughput, power
consumption, and reliability in data transmission. By using few approximations,
the proposed analysis is simplied to a mathematically less complex model that
can predict the performance of the protocol with an acceptable accuracy. Impact
of dierent network parameters including number of nodes and frame length, and
the MAC layer parameters including number of frame retries and backo window
length are investigated using the proposed models. The analysis is then extended
to evaluate performance of the protocol under erroneous channel conditions. Pre-
dictions of all the proposed analyses are validated using ns-2 simulations.
In Chapter 4, a Markov chain based analysis is presented to model the non-
beacon-enabled mode of the IEEE 802.15.4 MAC protocol. After illustrating the
possible contention scenarios associated with the non-beacon-enabled mode, a
mathematical model is developed for the protocol without ACK frames. Then,
by carefully integrating the ACK frame transmission and the frame retransmis-
sion mechanism, the proposed model is extended to evaluate performance of the
protocol with ACK frames. Similar to Chapter 3, generality of the proposed anal-
1. INTRODUCTION 11
yses is exploited to investigate the impact of dierent network and MAC-layer
parameters on the performance of the protocol. Validity of the proposed analyses
are proven using extensive ns-2 simulations.
Chapter 5 develops an analytical model to investigate the impact of the pres-
ence of hidden nodes on the performance of the IEEE 802.15.4 MAC protocol. In
a network with hidden nodes, the common channel seen by the receiver is mod-
eled using a DTMC based on the node grouping assumption. The DTMC is then
solved, and the aggregate throughput of the network is obtained. Consequently,
the impact of dierent network parameters including number of groups, nodes per
group, frame arrival rate and frame length on the performance of the protocol
is discussed. Experimental results show that the aggregate network throughput
of a given network depends on the average value of the number of hidden nodes
per node of that network. Based on this nding, a mechanism is proposed to ap-
proximate the throughput performance of networks that do not satisfy the node
grouping condition.
In Chapter 6, a new hybrid MAC mechanism is presented to improve the
performance of IEEE 802.15.4 based WSNs deployed in hybrid monitoring ap-
plications. The new protocol combines two dierent medium access techniques
without altering the basic architecture of the standard IEEE 802.15.4 MAC proto-
col. The performance of the proposed protocol is evaluated via ns-2 simulations.
A WSN deployed in a SHM system is considered as a case study to discuss the
improvements achieved by the proposed protocol in terms of reliability, delay, and
energy eciency in data transmission.
Chapter 7 concludes the thesis by presenting a summary of the investigations,
research outcomes, and recommendations for future research.
Chapter 2
MAC Protocols for WSNs
2.1 Introduction
This chapter reviews MAC protocols and their performance evaluation techniques
in the context of WSNs. An overview of basic wireless medium access mechanisms
is followed by a discussion on possible energy losses at the MAC layer in WSNs.
Based on how MAC protocols address the energy wastage, a broader classica-
tion of MAC protocols for WSNs is presented. The IEEE 802.15.4 standard is
identied as the state-of-the-art MAC protocol for WSNs and, it is summarised
by elaborating its dierent medium access mechanisms. An overview of the an-
alytical techniques that have been used to evaluate the performance of MAC
protocols is presented with a special attention to Markov chain based analyses.
Finally, the simulation platforms used to model MAC protocols in WSNs are dis-
cussed by comparing their ability to simulate the IEEE 802.15.4 MAC protocol.
2.2 Wireless MAC Protocols
Communication in wireless networks is generally achieved using a `common trans-
mission medium1', which is shared by all network nodes. The shared medium1Also referred to as the channel.
2. MAC PROTOCOLS FOR WSNS 13
access in wireless networks has to be regulated properly while satisfying the per-
formance requirements of underlying applications. This responsibility is carried
out by the MAC layer protocol of wireless nodes. The MAC layer represents the
lower half of the data link layer (DLL) of the open systems interconnection (OSI)
model. It operates directly on top of the PHY layer, thereby having full control
over the physical medium. The main functions of the MAC layer protocols are
to decide when a node accesses the shared medium and to resolve any potential
medium access conicts between competing nodes.
The medium access conicts, which are widely known as `collisions of transmis-
sions', can be minimised by exchanging some amount of controlling information
among the nodes. Exchanging these overhead information generally occurs over
the same common channel reducing the available channel resources for useful data
transmission. On the other hand, reducing overhead information would increase
collisions in data transmissions, and consequently it wastes the available channel
resources. The trade-o between the maximum utilisation of the available chan-
nel resources and the overheads required to achieve it has been at the basis of
most of the Wireless MAC protocols [7]. To address this trade-o, wireless MAC
protocols may follow one of the three fundamental channel access mechanisms :
xed assignment, demand assignment, and random access [39].
2.2.1 Fixed Assignment Mechanism
In the xed assignment mechanism, each node is allocated a predetermined xed
amount of channel resources with the cost of large overheads. Each node uses its
allocated resources exclusively without the risk of collisions. In general, a central-
node should exist with this mechanism to coordinate the resource allocation to
other nodes. The protocols based on the xed assignment are more ecient un-
der evenly distributed trac conditions (i.e., when each node has same amount
of data to transmit). However, they waste channel resources in uneven trac
2. MAC PROTOCOLS FOR WSNS 14
conditions by allocating resources to the idle nodes too (i.e., nodes without data
to transmit). Some of the classic protocols that comply with the xed assignment
mechanism are time division multiple access (TDMA), frequency division mul-
tiple access (FDMA), and code division multiple access (CDMA), which assign
channel resources in the form of time slots, frequency bands and channel codes,
respectively.
2.2.2 Demand Assignment Mechanism
In the demand assignment mechanism, channel resources are assigned temporar-
ily to nodes on their demand. Thus, this mechanism provides better performance
in networks with uneven trac conditions by dynamically assigning channel re-
sources only to active nodes. However, the dynamic resource allocation generates
more overheads, and hence reduces available channel resources further. The pro-
tocols based on polling schemes [40] and token passing [41] pursue the demand
assignment mechanism.
2.2.3 Random Access Mechanism
In contrast to the aforementioned mechanisms, the random access mechanism
does not particularly assign channel resources to nodes. Thus, all nodes must
contend to access the common medium in a random manner. Therefore, the
nodes in this mechanism are uncoordinated, and hence the channel access is fully
distributed. Compared to the other two mechanisms, the random access mech-
anism utilises little or no overheads to regulate the channel access. Therefore,
it inevitably causes collisions in data transmission. However, various techniques
have been proposed with this mechanism to reduce the number of collisions and
to recover from such collisions. For example, ALOHA protocol [42][43], which is
one of the rst protocols complied with random access, resolves collisions by de-
ploying a positive acknowledgement technique along with an exponential backing
2. MAC PROTOCOLS FOR WSNS 15
o retransmission mechanism.
The carrier sense multiple access (CSMA) protocol [44] enhances the collision
mitigation techniques proposed in ALOHA by sensing the shared medium (i.e.,
verifying the absence of other trac) before transmitting. Sensing the shared
medium - widely known as `carrier sensing' - reduces the possibility of simultane-
ous transmissions, and hence minimises the number of collisions in the medium.
Carrier sensing in CSMA is implemented in two versions: non-persistent and
p-persistent. In non-persistent CSMA, a node is allowed to transmit data imme-
diately if the medium is sensed idle. Otherwise, the node backs o (i.e., delays
the transmission) randomly. At the end of the back o period, the node senses
the medium again and repeats the same mechanism. On the other hand, in p-
persistent CSMA1 a node senses the medium continuously. When the medium
becomes idle the node either transmits the data with a probability p or delays
the transmission with a probability (1− p).
An extended version of CSMA known as carrier sense multiple access/collision
avoidance (CSMA/CA) adds further measures to limit the number of collisions oc-
curred in wireless networks, in which collision detection is not possible. CSMA/CA
protocol may optionally utilise request to send (RTS) and clear to send (CTS)
control messages to inform neighbouring nodes about the oncoming data trans-
mission, and thereby, it can reserve the wireless medium for each data transmis-
sion. Further improvements to the CSMA and CSMA/CA protocols have been
proposed in the IEEE 802.11 MAC protocol [45], multiple access with collision
avoidance (MACA) protocol [46] and its variants [47][48].
These three fundamental mechanisms (i.e., xed assignment, demand assign-
ment and random access) form the basis for MAC protocols designed for all most
all wireless networks including WSNs.1When p = 1, the protocol is specically categorised as 1-persistent CSMA.
2. MAC PROTOCOLS FOR WSNS 16
2.3 MAC Protocols for WSNs
In general, a WSN represents a network of battery powered sensor nodes that
are deployed in a large physical environment to monitor a certain phenomenon
cooperatively. The data collected at each sensor node are forwarded wirelessly
to a central server to generate useful information [2]-[4]. This challenging nature
poses several unique requirements for designing MAC protocols to WSNs. First,
these protocols should be able to adapt to network changes - in terms of topology,
size and density - without incurring signicant overheads. Next, the scalability of
MAC protocol is vital for many WSNs as most of them contain tens to hundreds
of sensor nodes. Then, dierent trac patterns (e.g., periodic trac, spontaneous
trac) generated by underlying WSN applications should be supported by these
protocols without undergoing signicant modications. Furthermore, these MAC
protocols should be simple enough to be implemented using limited memory and
computing capabilities of sensor nodes. Above all of these requirements, the
energy eciency in operation should be achieved by these protocols to conserve
limited energy resources in sensor nodes [9][10][14][49].
2.3.1 Energy Wastage in MAC Layer
To achieve the energy ecient communication in WSNs, sensor nodes should min-
imise the energy wasted at the MAC layer due to collisions, overhearing, protocol
overheads and idle listening [11][19]. Collision of transmissions inicts corrupted
receptions at the receiver-node, and hence it wastes energy at both transmitter
and receiver nodes. Energy consumption may extend further upon subsequent
retransmissions of the collided frames. The broadcast nature of wireless medium
creates the overhearing problem by making wireless nodes to pick up frames that
are not destined to them. Overhearing unnecessary trac can be a dominant
factor of the energy wastage when node density is high and trac load is heavy
[19]. Protocol overheads are induced by exchanging control and synchronisation
2. MAC PROTOCOLS FOR WSNS 17
information among competing nodes. Excessive transmitting/receiving of over-
head information may result in signicant energy consumption [11]. Nodes waste
energy on idle listening when they are listening to receive possible trac (data or
control messages) that is not sent. Even though idle listening consumes less en-
ergy than data transmission or reception, it is the main source of energy wastage
when nodes operate over a long period [19].
Based on how they eliminate/minimise energy losses and full other design
requirements, the MAC protocols for WSNs can be categorised in to two major
groups: schedule based protocols and contention based protocols.
2.3.2 Schedule based Protocols
In schedule based protocols, all sensor nodes follow a common schedule to access
the channel to avoid the energy losses occurred mainly due to collisions. There-
fore, these protocols are inherently based on the xed or demand assignment
mechanisms. TDMA scheme is the preferred mechanism for most of these proto-
cols, since limited resources at sensor nodes may not permit employing complex
radio transceivers required for FDMA or CDMA systems. In TDMA scheme, a
node transmits/receives only during its allocated time slot. It turns the radio
transceivers o at all the other times, and thereby avoids the idle listening and
overhearing implicitly. Therefore, schedule based protocols successfully eliminate
most of the energy losses in wireless communications and increase the life time
of the network.
However, the xed/demand assignment nature of these protocols creates some
shortcomings. First and foremost, a signicant amount of protocol overheads is
required in schedule based protocols to establish and maintain the common sched-
ule. Then, these protocols have to maintain a network-wide strict time synchro-
nisation, which is again achieved by additional signalling overheads, to deliver
a collision-free communication. Poor scalability is another issue with schedule
2. MAC PROTOCOLS FOR WSNS 18
based protocols as it is inecient or impractical to deploy a common schedule
in large networks. Moreover, schedule based protocols do not easily adapt to
network changes, since such adaptations demand complex rearrangements to the
common schedule.
TRAMA [12], low energy adaptive clustering hierarchy (LEACH) [50], dy-
namic energy ecient MAC (DEE-MAC) [51], pattern MAC (PMAC) [52], self
organising MAC for sensor networks (SMACS) [53] and EMACS [54] are some of
the prominent schedule based MAC protocols proposed for WSNs.
2.3.3 Contention based Protocols
In contention based protocols, the channel access is regulated not by a common
schedule but the contention that each node has with its neighbouring nodes to
access the common channel. Thus, these protocols are essentially based on the
random access mechanism. Most of the contention based protocols prefer CSMA
scheme over other random access techniques due to its simplicity. The absence
of a common schedule in these protocols minimises the energy wasted due to
protocol overheads. However, their random nature of channel access leads to
an excessive amount of energy losses in the form of collisions, idle listening and
overhearing. To mitigate these energy losses, many contention based protocols
provide various techniques. For example, random backing o and channel sensing
are generally performed to reduce collisions in transmission, while low duty cycles
with periodic listening [19] and preamble sampling [22] are employed to minimise
the idle listening and overhearing.
Despite their comparatively high energy consumption, the contention based
protocols show better adaptability for network changes and trac variations.
Furthermore, these protocols do not require a strict time synchronisation and
are highly scalable for large sensor networks due to their decentralised operation.
S-MAC [19], timeout MAC (T-MAC) [20], Berkeley MAC (B-MAC) [55], power
2. MAC PROTOCOLS FOR WSNS 19
aware multi access with signalling (PAMAS) [56] and WiseMAC [22] are a few
representative contention based MAC protocols for WSNs.
By combining the strengths and eliminating the weaknesses of both schedule
based and contention based categories, a new class of MAC protocols known as
hybrid protocols has been emerged recently. The IEEE 802.15.4 MAC protocol
[27] is one of these hybrid protocols1 that has a contention based component to
achieve a exible and low complex operation and a schedule based component to
provide a higher quality of service (QoS). More Importantly, the introduction of
the IEEE 802.15.4 protocol has accelerated the commercial deployment of WSNs
by setting up a standard for WSNs MAC protocols [60].
2.4 Overview of IEEE 802.15.4 Standard
The IEEE 802.15.4 standard [27] denes the PHY and MAC layer specica-
tions for Low-Rate WPANs, which are generally low cost, low power, short range
communication networks. The PHY layer of an IEEE 802.15.4 complied device
contains a radio frequency (RF) transceiver along with its low-level control mech-
anism. On the other hand, the MAC sublayer of the device provides a regulating
mechanism to access the physical layer for all communication. Figure 2.1 illus-
trates these layers within the OSI seven-layer model.
The MAC sublayer, which represents the lower half of the data link layer
(DLL), also provides services to the logical link control (LLC) sublayer2 (i.e., the
upper half of DLL) via a service specic convergence sublayer (SSCS) as shown
in Figure 2.1. The LLC sublayer oers a direct interface to upper layer protocols
by shielding them form the underlying PHY and MAC protocols. Functionalities
of the LLC sublayer and other upper layers, which range from the network layer
to application layer, are beyond the scope of the IEEE 802.15.4 standard.1Zebra MAC (Z-MAC) (Z-MAC) [57], Funneling MAC [58] and mobility adaptive hybrid
MAC (MH-MAC) [59] are a few other hybrid MAC protocols proposed for WSNs.2An IEEE 802.2 Type 1 LLC is intended to use with IEEE 802.15.4 devices.
2. MAC PROTOCOLS FOR WSNS 20
Figure 2.1: IEEE 802.15.4 standard within OSI seven-layer model.
Even though the IEEE 802.15.4 standard was originally proposed for WPANs,
the close similarities between the characteristics of WPANs and WSNs make it
a perfect candidate for the PHY/MAC layer protocols in WSNs1. Therefore,
following sections present the most relevant characteristics of the IEEE 802.15.4
standard in the context of WSNs: device types and network topologies, PHY
layer, and MAC layer.
2.4.1 Device Types and Network Topologies
The IEEE 802.15.4 standard denes two classes of physical devices: full-function
devices (FFDs) and reduced-function devices (RFDs). A FFD is equipped with
adequate resources to handle all the functionalities specied by the standard.
Conversely, a RFD has less resources and carries only a reduced set of protocol
functionalities. Based on these physical device types, the standard denes three
logical device types as follows:
• network coordinator: an FFD responsible for network establishment and
control,1Thus, the `LR-WPAN devices' and `wireless sensor nodes' will be used interchangeably
hereafter.
2. MAC PROTOCOLS FOR WSNS 21
Figure 2.2: IEEE 802.15.4 network topologies: (a) Star (b) Peer-to-peer (c)Cluster-tree, a complex network based on peer-to-peer topology.
• router node: an FFD that relays messages to other nodes,
• end node: an RFD or FFD that communicates only with its parent node,
i.e., the network coordinator or a router node.
Depending on application requirements and logical types of network nodes,
an IEEE 802.15.4 network may operate either of two network topologies: star
topology or peer-to-peer topology (Figure 2.2). In both of these topologies, the
network coordinator initialises the network and administers the association of
other nodes to the network. Communication in star topology networks is con-
trolled centrally by the network coordinator i.e., all nodes communicate directly
with the network coordinator, and the communication among nodes occurs only
through the network coordinator. Conversely, peer-to-peer topology networks ex-
hibit a decentralised communication, in which any node may communicate with
any other nodes in its radio range. In contrast to the star topology, the network
coordinator in the peer-to-peer topology is signicant only at network initialisa-
tion and association procedures; and it does not play a key role in handling the
data ows. Therefore, complex network structures with multi-hop communica-
tion such as cluster-tree topology (Figure 2.2 (c)) can be developed by extending
the peer-to-peer topology.
2. MAC PROTOCOLS FOR WSNS 22
2.4.2 PHY Layer
The physical layer is primarily responsible for data transmission and reception
using a certain physical radio channel according to a specic modulation and
spreading technique. For this purpose, the IEEE 802.15.4 standard adopts a wide
band physical layer with direct sequence spread spectrum (DSSS) technique. The
standard species three dierent frequency bands: the 868 MHz band - available
in Europe, the 915 MHz band - available in North America, and the 2400 MHz
ISM band - available worldwide. The channelisation of these frequency bands
are as follows: a single channel (Channel 0) operates in the 868 MHz band, 10
channels (Channels 1-10) are dened in the 915 MHz band with a channel spacing
of 2 MHz, and 16 channels (Channels 11-26) operate in the 2.4 GHz band with
a channel spacing of 5 MHz. The major features of each frequency band are
summarised in Table 2.11.
Apart from the data transmission and reception, the IEEE 802.15.4 PHY
layer is responsible for the activation and deactivation of the radio transceiver,
channel frequency selection, energy detection within the current channel, link
quality indication (LQI) for received frames, and clear channel assessment (CCA).
Among these tasks, CCA is particularly important to the MAC layer to acquire
the knowledge of the current state of the medium: busy or idle. CCA reports a
Table 2.1: IEEE 802.15.4 PHY layer: Frequency bands and data rates.
PHY Frequncey Data Parameterslayer band Modulation Bit rate Symbol rate Symbols(MHz) (MHz) (kb/s) (ksymbol/s)
868 868868.6 BPSK† 20 20 Binary915 902928 BPSK 40 40 Binary2450 24002483.5 O-QPSK‡ 250 62.5 16-ary† binary phase-shift keying (BPSK)‡ oest quadrature phase-shift keying (O-QPSK)
1It is worth mentioning that the 2006 revision of the standard [61] has proposed new mod-ulation schemes along with the parallel sequence spread spectrum (PSSS) technique for the 868MHz and 915 MHz bands allowing them to achieve maximum data rate of 250 kbps.
2. MAC PROTOCOLS FOR WSNS 23
busy medium to the MAC layer by detecting either any energy above the energy
detection threshold or a signal with the modulation and spreading characteristics
of the IEEE 802.15.4 standard. Otherwise, it reports an idle medium to the MAC
layer.
2.4.3 MAC Layer
The MAC layer is mainly responsible for beacon management, network associ-
ation and disassociation, channel access via CSMA/CA mechanism, guaranteed
time slot (GTS) management, ACK frame delivery, frame validation, and se-
cure communication. Among all of them, this thesis concentrates only on the
CSMA/CA mechanism, GTSs, and ACK frame delivery, which relate directly to
the channel access procedures. In terms of channel access, the IEEE 802.15.4
MAC protocol can operate in two dierent modes: beacon-enabled mode and
non-beacon-enabled mode. In the beacon-enabled mode, special control mes-
sages known as beacons are periodically transmitted by the network coordinator
for synchronisation and association purposes. On the other hand, the periodic
beacons do not exist in the non-beacon-enabled mode; however the network coor-
dinator may transmit unicast beacon messages upon nodes' requests. Figure 2.3
Figure 2.3: IEEE 802.15.4 MAC protocol: Channel access mechanisms [62].
2. MAC PROTOCOLS FOR WSNS 24
illustrates these two operational modes along with their associated channel ac-
cess mechanisms1: slotted CSMA/CA, slotted CSMA/CA + GTSs, and unslotted
CSMA/CA; and provides the outline for the following discussion.
2.4.3.1 Beacon-Enabled Mode
In the beacon-enabled mode, all nodes follow a common superframe structure
initiated by the network coordinator. The superframe, which is bounded by two
consecutive beacons, consists of an active period and an optional inactive period
as shown in Figure 2.4. While all communications occur during the active period,
nodes' transceivers are allowed to sleep in the inactive period. The length of the
superframe (beacon interval, BI) and the length of its active period (superframe
duration, SD) are dened as
BI = aBaseSuperframeDuration × 2BO
SD = aBaseSuperframeDuration × 2SO,(2.1)
where aBaseSuperframeDuration = 960 symbols. The parameters BO and SO
represent the beacon order and superframe order respectively, and they are in
the range 0 6 SO 6 BO 6 14. When SO = BO ⇒ SD = BI, and then
the superframe is always active. The active period of the superframe is divided
into 16 equal superframe slots as depicted in Figure 2.4. Each superframe slot is
further divided into several smaller slots that are equal to aUnitBackoPeriods
(= 20 symbols) in length. These smaller slots form the basic time period of the
CSMA mechanism, and they are simply referred to as the backo slots throughout
this dissertation. The active period consists of a beacon, a contention access
period (CAP), and an optional contention free period (CFP). Nodes that want to
communicate during the CAP compete with other nodes by following the slotted1Each of these access mechanism supports an optional positive ACK scheme. In this ACK
scheme, if the destination node has not received a given data frame successfully, that frame isnot acknowledged. If the originating node has not received the corresponding ACK after a datatransmission, it assumes an unsuccessful transmission and retransmits the data frame.
2. MAC PROTOCOLS FOR WSNS 25
Figure 2.4: IEEE 802.15.4 superframe structure.
CSMA/CA mechanism1, while in the CFP nodes access the channel using GTSs.
Slotted CSMA/CA Mechanism: The primary channel access mechanism of
the beacon-enabled mode is the slotted CSMA/CA. In this mechanism, all nodes
are time synchronised with the network coordinator by aligning respective back-
o slot boundaries, in particular to the start of the beacon transmission. Fur-
thermore, all channel access procedures (e.g., backing o, channel sensing and
transmitting) should begin at the boundaries of backo slots.
When the MAC layer of a node receives a data frame to transmit, the IEEE
802.15.4 slotted CSMA/CA mechanism (Figure 2.5) begins to operate as follows:
First, the variables NRT, NB, BE and CW, which represent the retransmission
counter, backo stage counter, backo exponent and contention window length,
are initiated to their respective default values (0, 0, macMinBE (default value 3)
and 2). Then, the node locates the next backo slot boundary and waits for a ran-
dom number of backo slots chosen uniformly between 0 and 2BE−1 before sens-
ing the channel. This random backing o reduces the possibility of collision with
transmissions of other contending nodes. After the backo phase, the node senses
the channel (i.e., performs a CCA) for CW times consecutively. If the channel
1Except the channel access for beacons and ACK frame transmissions.
2. MAC PROTOCOLS FOR WSNS 26
Figure 2.5: IEEE 802.15.4 slotted CSMA/CA mechanism.
has been found idle, the channel access is considered a success and transmission
begins. Otherwise, the node increases NB and BE by one and moves to the next
random backo stage. Once BE reaches the maximum value of backo exponent
(macMaxBE ; default value 5), it is frozen. The above process repeats until NB
reaches the maximum number of backo stages (macMaxCSMABackos ; default
value 4) where an access failure is declared to the upper layer.
If the ACK scheme is not enable, the slotted CSMA/CA mechanism ter-
minates after transmitting the data frame. Otherwise, the slotted CSMA/CA
2. MAC PROTOCOLS FOR WSNS 27
Table 2.2: IEEE 802.15.4 MAC-layer parameters.
MAC layer Range Default Related MACparameter value layer variables
macMaxBE 3 8 5 BEmacMinBE 0 macMaxBE 3 BEmacMaxCSMABackos 0 5 4 NBmacMaxFrameRetries 0 7 3 NRT
mechanism continues and the node waits for the corresponding ACK frame to
be received. If the node does not receive the corresponding ACK frame within
a macAckWaitDuration1 period, it assumes a frame transmission failure. Then,
it increases NRT by one and starts to retransmit the data frame. During the re-
transmission phase, the node retries the aforementioned channel access procedure
up to the maximum value of retransmission attempts (macMaxFrameRetries ; de-
fault value 3). After macMaxFrameRetries transmission failures, the node drops
the data frame after declaring a transmission failure. Possible range and default
values of the MAC-layer parameters described in this section are listed in Table
2.2.
Channel Access using GTSs: In the beacon-enabled mode, a node that re-
quires a guaranteed QoS can access the channel during the optional CFP, which
(if it presents) locates at the end of the CAP as shown in Figure 2.4. The CFP is
comprised of GTSs allocated by the network coordinator to the requested nodes.
A GTS is a portion of the superframe that is dedicated exclusively to a given
node. The network coordinator can allocate up to seven GTSs at a time and
each GTS may occupy more than one superframe slot. Communication within a
GTS, which is exclusively limited to its allocated node, starts immediately after1This is the maximum waiting time specied in the standard. It comprised of the starting
delay sdack of ACK transmission and the length of the ACK frame Lack. The former variesbetween aTurnaroundTime (i.e., 12 symbols) and aUnitBackoPeriod + aTurnaroundTime
(i.e., 32 symbols) depending on the time at which the last symbol of the data frame receivedat the receiver-node. The length of ACK frames is 22 symbols in 2.4GHz PHY layer.
2. MAC PROTOCOLS FOR WSNS 28
the GTS boundary without following the CSMA/CA mechanism. Thus, the GTS
mechanism creates a scheduled based access scheme within the superframe using
a TDMA like mechanism.
2.4.3.2 Non-beacon-Enabled Mode
IEEE 802.15.4 networks that do not comply with the superframe structure operate
in the non-beacon-enabled mode by setting BO = SO = 15. Due to the absence
of periodic beacons, nodes are not synchronised in non-beacon-enabled networks,
and hence all transmissions except ACKs follow the unslotted CSMA/CA mech-
anism to access the channel. Moreover, GTSs are not permitted in this mode of
the protocol.
Unslotted CSMA/CA Mechanism: The IEEE 802.15.4 unslotted CSMA/CA
can be considered as a simpler version of the slotted CSMA/CA mechanism.
Apart from the absence of periodic beacons and its consequences (i.e., absence of
network wide synchronisation, superframe structure), the unslotted CSMA/CA
diers from its slotted counterpart in the following aspects:
• The evolution of time in the slotted mechanism is discrete where the discrete
time unit is equal to a single backo slot (= 20 symbol duration); i.e., events
such as back o, CCA, and frame transmission occur only at the boundaries
of backo slots. On the other hand, the evolution of time in the unslotted
mechanism is continuous; i.e., an event may occur immediately after the
previous one.
• In the slotted CSMA/CA, a node performs two consecutive CCAs (at the
boundaries of back-to-back backo slots) to assess the channel idleness,
while in the unslotted CSMA/CA, only a single CCA is performed.
Except these dierences the operation of the the IEEE 802.15.4 unslotted CSMA/CA
2. MAC PROTOCOLS FOR WSNS 29
Figure 2.6: IEEE 802.15.4 unslotted CSMA/CA mechanism.
mechanism is almost similar to the slotted mechanism, and it is shown in Fig-
ure 2.61.
Next, the performance evaluation of wireless MAC protocols will be discussed,
with special emphasis on the two operational (i.e., beacon-enabled and non-
beacon-enabled) modes of the IEEE 802.15.4 MAC protocol.
1The MAC layer variables and parameters in Figure 2.6 have the same meanings and valuesof those in the slotted mechanism.
2. MAC PROTOCOLS FOR WSNS 30
2.5 Performance Evaluation of MAC Protocols
Performance of a MAC protocol is a central issue in its design, deployment and
conguration. Therefore, the performance evaluation is a major requirement
for a MAC protocol to measure its capability and competency of accomplishing
the required tasks. Furthermore, performance evaluation studies may be useful in
optimising MAC protocols for a given set of system specications. There are three
basic methods to evaluate the performance of a protocol: laboratory experiments,
computer simulations and analytical models. Among them, analytical models and
computer simulations are the focus of this dissertation as laboratory experiments
are exceedingly time consuming and costly1.
2.5.1 Analytical Models
In the context of MAC protocols, analytical modelling is considered as a useful
tool to model the probabilistic behaviour of the protocols. Thus, it has been
widely applied to analyse the MAC protocols with random access mechanisms.
To this end, several techniques have been proposed in the literature [66][67].
A technique known as the S-G analysis was introduced by Abramson [68] to
evaluate the throughput performance S of the ALOHA protocol in terms of the
oered channel trac rate G. This analysis was then extended by Kleinrock and
Tobagi [44] to evaluate the throughput and delay performance of the persistent
and non-persistent CSMA protocols. More recently, the maximum throughput
and channel busyness of CSMA/CA protocols deployed in the IEEE 802.11 and
IEEE 802.15.4 standards have been computed using a generalised S-G analysis
[69]. The S-G analysis usually models the frame transmission characteristics on
the common channel and does not analyse the behaviour of individual nodes in1For instance, a prototype WSN deployment at Golden Gate Bridge in San Francisco USA
has taken approximately 6 months and US$38, 000 to be implemented (i.e., the WSN wascomprised of 64 sensor nodes that cost US$600 each) [63]. Similar prototype WSNs deployed inglacial environment monitoring [64] and heritage building monitoring [65] have been cost £177and US$120 for each sensor node, respectively.
2. MAC PROTOCOLS FOR WSNS 31
detail. Therefore, it generally fails to evaluate the energy performance of a node,
which is vital in the context of WSNs.
On the other hand, the tagged user analysis (TUA) [70] models the behaviour
of an individual node of a given network by using the classical queuing theory.
It evaluates the performance of MAC protocols by considering the interactions
among individual nodes on accessing the common channel. To enable this ap-
proach the TUA assumes that all nodes in the network exhibit equivalent sta-
tistical behaviours. Based on this fundamental assumption, TUAs have been
developed to analyse the throughput and delay performance of many random ac-
cess protocols including slotted ALOHA [70][71], CSMA [72], and IEEE 802.11
CSMA/CA [73] protocols.
A uid-type approximation analysis known as the equilibrium point analysis
(EPA) was proposed by Tasaka [67] to analyze MAC protocols in steady state.
In EPA, it is assumed that the system always works at its equilibrium point so
that the number of nodes in any mode is remained xed. For its analysis, EPA
requires to solve a set of nonlinear equations to obtain the equilibrium point of
the system. Using the solution of equilibrium point, various system performance
metrics are calculated. The EPA was rst applied to analyse the carrier sense
multiple access/collision detection (CSMA/CD) protocol [74]. Recently, it has
been used in evaluating the throughput performance of CSMA/CA in the IEEE
802.11 distributed coordination function (DCF) protocol [75][76].
In contrast to the aforementioned techniques, the Markov chain analysis [77][78]
provides a generalised frame work to analyse the performance of MAC protocols.
Neither an equilibrium point in operation nor an equivalent statistical behaviours
of nodes is necessary to develop a Markov chain. In addition, Markov chains can
comprehensively model the behaviour of an individual node in a given network.
Moreover, the Markov chain analysis has been at the foundation of many other
evaluation techniques including EPA and classical queuing theory [77]. In this an-
alytical approach, a Markovian model [78] is formulated to capture the dynamic
2. MAC PROTOCOLS FOR WSNS 32
behaviour of the system of interest, and then the stationary state probability
distribution of the model is computed. At the end, the system performance is
evaluated using the stationary state probabilities.
Early work on Markov chain analysis of wireless MAC protocols can be found
in [79][80], where the performance of slotted ALOHA and slotted non-persistent
CSMA is evaluated, respectively. Bianchi [81] has developed a two-dimensional
Markov chain to model the CSMA/CA mechanism in the IEEE 802.11 DCF pro-
tocol, and consequently performed a saturated throughput analysis. Following
Bianchi's approach, many Markov chain models have been developed to analyse
the IEEE 802.11 DCF under dierent conditions (e.g., unsaturated trac [82]
and imperfect channel conditions [83]). Pollin et al. [84] have extended Bianchi's
model to evaluate the throughput and energy consumption of IEEE 802.15.4
based star topology networks under both saturated and unsaturated trac con-
ditions. An advanced Markov chain has been presented in [85] by including the
superframe structure of the IEEE 802.15.4 MAC protocol to evaluate the unsatu-
rated throughput of the protocol. Some of the other notable Markov chain based
analyses on the IEEE 802.15.4 MAC protocol can be found in [86]-[89] and more
related work will be presented in Chapter 3, 4 and 5.
2.5.2 Simulations
Simulation based analyses have been used as an alternative to analytical tech-
niques in many large and complex systems, since they can model and analyse such
systems without making restrictive assumptions. In the context of WSNs, various
computer simulators are being employed to model the MAC layer protocols and
to evaluate their performance.
OPNET Modeler [90], which has an object-oriented approach and a hierar-
chical modelling environment, is a well known commercial simulation tool for
network protocol modelling. It provides a wireless suite to model and simulate
2. MAC PROTOCOLS FOR WSNS 33
various aspects of a wireless network including MAC protocols. Several WSNs'
MAC protocols such as PMAC [52], OD-MAC[91], and A-MAC [92] have been
already simulated and validated using OPNET Modeler. The IEEE 802.15.4
MAC protocol is readily implemented in OPNET, and this implementation has
been used in [93] to study the performance of slotted CSMA/CA mechanism of
the protocol. However, the IEEE 802.15.4 implementation in OPNET does not
contain energy models [94], and hence it fails to simulate the energy performance
of the protocol. QualNet [95] is another commercial simulator1 that has a par-
allel discrete-event simulation approach to model wired and wireless networks.
Similar to OPNET Modeler, QualNet includes an IEEE 802.15.4 MAC protocol
implementation [96] and also supports simulating of many other MAC protocols
in WSNs [97][98].
Compared with OPNET and QualNet simulators, OMNeT++ [99] provides
a free, object-oriented, discrete network simulation framework that can be used
to model and evaluate MAC protocols in WSNs [100]-[102]. Because of its open
platform, OMNeT++ has been extensively exploited to develop many simulators
for WSNs. Among them, MiXiM [103] supports the non-beacon-enabled mode
of the IEEE 802.15.4 protocol, while INET framework [104] includes the beacon-
enabled mode. However, none of the OMNeT++ based simulators contain a
unied implementation of the IEEE 802.15.4 MAC protocol that includes both
operational modes.
In contrast, the IEEE 802.15.4 implementation in ns-2 [105][106] supports
both operational modes of the protocol and also includes simulation models
for energy consumption. ns-2 provides an open simulation platform for object-
oriented discrete event simulations, and hence it is widely used in academia to
model, validate and evaluate communication protocols. The IEEE 802.15.4 MAC
protocol was rst implemented in ns-2 by Zheng and Lee [107][108] to study
the performance of the protocol comprehensively. A similar simulation based1Both OPNET and QualNet are freely available for academic research with less features.
2. MAC PROTOCOLS FOR WSNS 34
study is presented in [109], where ns-2 simulations were used to investigate some
throughput-energy-delay tradeos of the protocol. Subsequent improvements to
the initial ns-2 implementation of the IEEE 802.15.4 protocol [107] are proposed
in [110][111]. Apart from the IEEE 802.15.4, many other WSN's MAC protocols
have been modelled and evaluated in ns-2 successfully [19][91][112].
Comprehensive reviews on simulation environments for WSNs can be found
in [113]-[115].
2.6 Conclusion
All wireless MAC protocols are based on one of three basic access mechanisms:
xed assignment, demand assignment and random access. In WSNs, these mech-
anisms are used in dierent ways to minimise the energy wasted at the MAC
layer. The IEEE 802.15.4 MAC protocol provides a better solution by combining
the features of demand assignment and random access mechanisms. Performance
of MAC protocols, which critically aects the overall performance of a given net-
work, can be evaluated by either analytical models or simulations. The Markov
chain analysis is preferred to evaluate the IEEE 802.15.4 MAC protocol analyt-
ically, while ns-2 can simulate the protocol comprehensively. As a whole, this
chapter provides a foundation for the concepts and techniques presented in the
subsequent chapters.
Chapter 3
Analysis of Beacon-enabled IEEE
802.15.4 MAC Protocol with ACK
Transmission
3.1 Introduction
With the introduction of wireless sensor networks (WSNs), there has been an in-
creasing demand for low-power, low-data-rate wireless communication standards.
The IEEE 802.15.4 standard [61] meets that demand by dening the PHY and
MAC layer specications for such communications. As mentioned in Chapter 2,
the IEEE 802.15.4 MAC protocol can operate in two dierent modes: beacon-
enabled mode and non-beacon-enabled mode. Out of these modes, the beacon-
enabled mode can be utilised to maintain the network-wide coordination required
for synchronised monitoring WSNs [116][117]. The beacon-enabled mode also fa-
cilitates several power saving features including BatteryLifeExtension and duty
cycling [61][108], which are vital in achieving overall energy eciency. Further-
more, the IEEE 802.15.4 standard includes a MAC-level retransmission mecha-
nism with acknowledgements (ACKs) to improve data transmission reliability in
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 36
reliability-critical-WSN applications such as military surveillance, wildre detec-
tion and explosive agents tracking [7][34]. Moreover, most of the existing IEEE
802.15.4 based networks operate under unsaturated trac conditions as the stan-
dard has been specically proposed for low-data-rate applications. Therefore,
to understand the performance of real-world WSNs, it is necessary to analyse
the beacon-enabled IEEE 802.15.4 MAC protocol with ACK transmission under
unsaturated trac conditions.
The performance of the beacon-enabled IEEE 802.15.4 MAC protocol has been
already evaluated by means of experiments [118]-[120], simulations [107][109][121]
and analytical models [87]-[135]. Most of the earlier analytical models [87][122],
which were inspired by the seminal work of Bianchi [81], have described the
beacon-enabled IEEE 802.15.4 protocol under saturated trac, despite the fact
that such trac conditions are rarely seen in WSNs. Among these saturated
analyses, only a few have considered ACK transmission and frame retransmission
[86][123]-[125]. Recently, dierent approaches to Bianchi's model have been taken
to analyse the protocol with unsaturated trac [88][126]-[135] ; however, most of
them have overlooked ACK transmission [88][126]-[130].
One of the early attempts to analyse the protocol with acknowledgements
and retransmissions under unsaturated trac conditions can be found in [133].
However, the analytical model presented in [133] has not been validated, and later
it has been shown that it does not match the simulation results [87]. Taking a
dierent approach, Lee et al. [132] have proposed a renewal theory based model to
analyse the protocol with ACK transmission under unsaturated trac. Yet, they
have not derived energy/power related performance metrics, which are vital in
the context of WSNs. Furthermore, they have assumed saturated trac to derive
the throughput performance. The enhanced Markov chain presented in [131]
accurately models the protocol with ACK and unsaturated trac. However, it too
has overlooked the energy/power related performance metrics of the protocol. On
the other hand, Park et al. [134] have derived the average power consumption of
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 37
a sensor node considering a network with ACK frames under unsaturated trac.
But, they have not discussed the throughput performance of the protocol. The
analysis proposed by Sahoo and Sheu [135] has derived both the throughput and
the energy consumption related performance metrics. However, their analysis has
been validated only for a single network setup. Later, it has been shown that the
analysis in [135] weakly matches with simulations [134].
The majority of the existing analyses of the beacon-enabled IEEE 802.15.4
MAC protocol assume default MAC parameters values, and hence they present
value-specic models. However, the default MAC layer conguration does not
yield the desirable performance for all situations [136][137]. In fact, the litera-
ture shows that there is a signicant impact of MAC-layer parameters includ-
ing macMinBE, macMaxBE, macMaxCSMABackos and macMaxFrameRetries
on system performance [134]-[136, and references there in]. Most of the ex-
isting analytical models that consider the eects of MAC-layer parameters on
system performance only take macMinBE or macMaxCSMABackos or both
[125][129][132][138] into account. On the other hand, the analyses in [131] and
[135] only consider the macMaxFrameRetries parameter. The models that con-
sider all of the above MAC parameters [134] do not derive a comprehensive set of
performance metrics, which covers both throughput and energy/power aspects of
the protocol. Therefore, none of the existing analyses provide a complete picture
of the performance of beacon-enabled IEEE 802.15.4 MAC protocol with ACK
transmission under unsaturated trac conditions.
The key assumption of `ideal (i.e., error free) channel condition' made in all
the aforementioned analyses deters their applicability in practical WSNs, in which
sensor nodes generally communicate through highly error prone wireless channels.
Although the impact of channel errors on the performance of well-known IEEE
802.11 based networks has been thoroughly investigated in the literature [139]
[140], that of the IEEE 802.15.4 based WSNs is still open to be studied. More
recently, Gao et al. [141] have eliminated the ideal channel assumption and anal-
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 38
ysed the IEEE 802.15.4 MAC protocol under burst error channels. However, that
analysis does not take ACK transmission into account. Furthermore, it overlooks
some performance metrics including power consumption and frame delivery ratio,
which are important in reliability critical WSNs. Thus, there is a need to analyse
the protocol with ACK transmission under erroneous real-life channel conditions.
This chapter presents a generalised analytical model based on a three-dimensional
discrete-time Markov chain (3D-DTMC) to analyse the beacon-enabled IEEE
802.15.4 MAC protocol with ACK transmission under unsaturated trac condi-
tions. The proposed analytical model is a comprehensive extension of the analysis
done by Ramachandaran et al. [126] for the same protocol without ACK frames
and retransmissions. In additon, a simplied version of the proposed model is
presented to reduce the complexity of the analysis. Finally, the proposed analyt-
ical model is extended to analyse IEEE 802.15.4 based networks operating under
erroneous channel conditions.
3.2 System Model
The system model considered in this chapter consists of an IEEE 802.15.4 beacon-
enabled, single-hop, star topology network of N sensor nodes and a coordinator
node. The latter acts as a common receiver for all sensor nodes, which lie within
the carrier sensing range of each other. Therefore, all nodes see the same trans-
mission channel with regard to MAC-layer functionalities. The system remains
active throughout the entire beacon interval1, which is lled with CAP. Data
transmission within the system is assumed to be only in the uplink direction
(i.e., from sensor nodes to coordinator). Each successful data transmission will
be followed by an acknowledgement (ACK) from the common receiver. All data
frames are of equal length, and the transmission of a data frame lasts for xed-L1i.e., SO = BO. However, only the coordinator node remains active always. For energy
conservation, sensor nodes power-o their radio unless they are transmitting data frames orreceiving acknowledgements and beacons.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 39
backo slots. Data frames arrive at the nodes according to a Poisson distribution
with an arrival rate of λ frames per frame duration. Thus, the frame arrival prob-
ability per backo slot can be derived as p = λ/L. Buering at the nodes is not
considered. Consequently, a node that already holds a frame will discard further
frame arrivals. Moreover, the capture eect [142][143] is not implemented for
collided frames, i.e., all collided frames are dropped regardless of their received
signal strengths at the common receiver. Unless mentioned otherwise, ideal chan-
nel conditions are assumed. Hence, transmitted data frames can be lost only due
to frame collisions.
Performance of the system of interest (e.g., throughput, power consumption)
largely depends on the amount of data transmitted over a given period of time.
On other hand, transmission of data depends on sensing the channel to be idle.
Therefore, modelling of this system is based on two basic probabilities:
• The probability that the channel is sensed idle in a given backo slot,
• The probability that a node begins data transmission in a given backo
slot.
Since exact computation of these probabilities is analytically complex, some ap-
proximations1 are considered to simplify the analysis.
3.2.1 Approximations
The probability that the channel is sensed idle in a given backo slot is approx-
imated by the steady state probability pci that the channel is idle. Accordingly,
after a random backo each node senses the channel to be idle in the rst2 of the
two channel-sensing backo slots with probability pci . Similarly, the probability
that any node begins data transmission in a given backo slot is approximated
by the steady state probability pnt that a node begins its data transmission.1Similar approximations have been made in [126] and [144].2The channel idleness is not considered to be independent from one sensing backo slot to
the next.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 40
According to the standard, a node should defer any transmission that cannot
be completed within the current beacon interval to the next. It is assumed that
this condition has a negligible eect on the contention process of data transmis-
sion. Therefore, the beacon-enabled IEEE 802.15.4 MAC protocol is approxi-
mated by the slotted CSMA protocol described in Section 2.4.3.1.
3.3 Analytical Model
Based on the system model and approximations above, this section formulates two
DTMCs to model the behaviour of an individual node and the common channel
in the system considered. The two DTMCs are coupled and solved numerically
for the basic model probabilities pnt and pci , which can be then used to analyse
the protocol in a given network.
3.3.1 Node State Model
Consider a node that has a data frame to transmit. In the standpoint of MAC
protocol, this node can be in dierent states depending on its current trans-
mission attempt, backo stage and backo counter value. Values of these pa-
rameters in a given instant depend on the basic model probabilities, and hence
they are not deterministic. Therefore, the transmission attempt, backo stage
and backo counter of a given node are modelled using three stochastic pro-
cesses represented by the random variables x, y and z, respectively, where x ∈
1, 2, ...,macMaxFrameRetries+1, y ∈ 1, 2, ...,macMaxCSMABackos+1 and
z ∈ 1, 2, ..., wy − 11. Based on these stochastic processes, a three-dimensional
DTMC is developed to model the behaviour of an individual node as shown in
Figure 3.1.
A node resides in the IDLE state if it does not have a frame to transmit.
When this node receives a frame to transmit (with probability p), it moves from
1wy − 1 represents the maximum backo window length of the yth backo stage, and it isequal to 2BEy − 1, where BEy denotes the corresponding backo exponent.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 41
Figure 3.1: 3D-DTMC model of node. Probability pci and pci|i are denoted by u
and v, respectively.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 42
the IDLE state to the rst backo stage. A backo stage consists of several
backing o slots and two channel sensing states; each of one backo slot1 in
duration. The number of back o slots that the node resides in the yth backo
stage is uniformly distributed between 0 and wy − 1 (note: if the chosen number
is zero, the node immediately starts to sense the channel without backing o).
However, during the rst backo stage of the rst transmission attempt, the node
has to wait at least three backo slots before sensing the channel due to the node's
transceiver Sleep-to-Idle transition time [126]. The back o slots of all backo
stages are modelled by BOxyz states with suitable values for the variables x, y
and z.
When the rst backing o expires, the node moves to CS110 state. The nota-
tions CSxy0 and CSxy(−1) are used to represent the rst and second channel sensing
states of the yth backo stage in the xth transmission attempt, respectively. If
the channel is found to be idle during the rst sensing, which happens with prob-
ability pci , the node moves to CS11(−1) state. If the node nds the channel to be
idle again, it accesses the channel to transmit the data frame. This happens with
probability pci|i, which is the conditional probability of the channel being idle in
the next backo slot given that it is idle in the current backo slot. On the other
hand, if the channel had been found busy when the node was in either CS110 or
CS11(−1) state, the node moves to the next backo stage and chooses the number
of back o slots randomly. This mechanism is repeated up to y backo stages,
where y = macMaxCSMABackos+1 . The node moves back to IDLE state from
CS1y0 or CS1y(−1) by declaring a channel access failure, if the channel is found to
be busy.
On the contrary, if the node gets access to the channel successfully, it resides
in TX1 state for L backo slots to transmit the data frame. At the end of data
transmission, the node moves to ACK1 state with probability 1 and awaits the1This is the basic time step of the protocol and equals to 20 symbols in duration. In the
2.4 GHz physical layer, this equals 320 µs.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 43
Table 3.1: Relationships between model variables and MAC-layer parameters.
Model variable MAC-layer parameters
x macMaxFrameRetries + 1y macMaxCSMABackos + 1wy 2min((y−1)+macMinBE ,macmaxBE)
corresponding ACK frame for a period of (Lack + sdack) backo slots, where Lack
and sdack denote the ACK frame length and the starting delay of ACK trans-
mission, respectively. If the node has received the ACK frame (with probability
q, which will be determined in Section 3.3.2), it moves to IDLE state. Con-
versely, if the node has not received the ACK frame, it moves to the rst backo
stage of the next transmission attempt. This process repeats until the node
receives the corresponding ACK frame or tries x transmission attempts, where
x = macMaxFrameRetries + 1. If the node has not received the corresponding
ACK frame after x transmission attempts, it terminates the transmission process
and moves back to IDLE state by declaring a transmission failure. The relation-
ships between the model variables x, y and wy, and their respective MAC-layer
parameters are summarised in Table 3.1.
Based on the node state transitions described above, steady state probabilities
of the node model DTMC are derived in Appendix A.1. They are then used to
derive the steady state probability pnt that a node begins data transmission as
follows.
pnt =
steady state probability of node
residing in any CSxy(−1) state
probability that the node transits
from CSxy(−1) to TXx states
=
( ∑xx=1
∑yy=1 π(csxy(−1))
π(idle) + Φ + Ψ + L∑x
x=1 π(txx) + (Lack + sdack)∑x
x=1 π(ackx)
)pci|i,
(3.1)
where Φ =∑x
x=1
∑yy=1
∑wy−1z=1 π(boxyz) and Ψ =
∑xx=1
∑yy=1
∑0z=(−1) π(csxyz). In
(3.1), the steady state probability of node residing in any of the CSxy(−1) states
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 44
is obtained by summing long-term proportions of time that the node resides in
each CSxy(−1) state. Then, the transition probability pci|i can be related to the
other basic model probability - the steady state probability pci that the channel is
idle - as
pci = pci|ipci + pci|b(1− pci), (3.2)
where pci|b is the conditional probability that the channel is idle in the next backo
slot given that it is busy in the current backo slot. The probability pci|b equals2
L+Lack, where L and Lack represent the data frame length and ACK frame length
in backo slots, respectively. Therefore, (3.2) can be rearranged to obtain pci|i as
pci|i =pci − pci|b(1− pci)
pci= 1− 2(1− pci)
(L+ Lack)pci. (3.3)
To nd the basic probability pci in (3.3), the channel state model is developed
next.
3.3.2 Channel State Model
The behaviour of the channel can be modelled using a DTMC as shown in Fig-
ure 3.2. Assume that the channel is idle for two consecutive backo slots, which
is represented by IDLE,IDLE state. The channel remains in this state given
that none of the nodes begin transmission. This happens with probability α
= (1 − pnt|ii)N , where N and pnt|ii are the number of contending nodes and the
probability that any node begins transmission given that the channel has been
idle for two consecutive backo slots, respectively. The conditional probability
pnt|ii can be computed as
pnt|ii =pntpcii
=pntpci|ip
ci
=(L+ Lack)p
nt
(L+ Lack)pci − 2(1− pci). (3.4)
On the other hand, when exactly one node begins transmission and the oth-
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 45
Figure 3.2: Discrete-time Markov chain model of channel.
ers refrain from transmission, the channel moves from IDLE,IDLE to SUCCESS
state. This happens with probability β = Npnt|ii(1 − pnt|ii)N−1. After dwelling
L backo slots in SUCCESS state, the channel progresses to ACK-IDLE state
with probability 1. ACK-IDLE state represents the channel idleness between
data transmission and the following ACK transmission. The channel dwells sdack
backo slots in this state and moves to ACK-TX state, which corresponds to
the subsequent ACK transmission. After residing Lack backo slots in this state,
the channel returns to IDLE,IDLE state through an intermediate IDLE state. If
more than one node begin transmission simultaneously, the channel moves from
IDLE,IDLE to FAILURE state with probability σ = 1 − α − β. Due to the ab-
sence of a collision detection mechanism, the channel dwells in FAILURE state
for the entire frame length (i.e., L backo slots) before returning to IDLE,IDLE
state via the intermediate IDLE state. Moreover, a node that has just transmit-
ted a data frame will receive the corresponding ACK frame if none of the other
nodes have transmitted data simultaneously. This happens with probability q
= (1− pnt|ii)N−1, and it is one of the key probabilities that the node state model
was built upon in Section 3.3.1.
According to the channel behaviour described above, the steady state equa-
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 46
tions and the normalised condition of the channel model DTMC can be expressed
as
πcidle,idle = πcidle + απcidle,idle
πcsuccess = βπcidle,idle
πcack−tx = πcack−idle = πcsuccess (3.5)
πcfailure = (1− α− β)πcidle,idle
πcidle = πcfailure + πcack−tx
πcidle,idle + πcsuccess + πcfailure + πcack−idle + πcack−tx + πcidle = 1. (3.6)
By rearranging the equations above, the long-term proportion of transitions into
each state in the channel model DTMC are computed as
πcidle,idle =1
Ω, πcidle =
(1− α)
Ω, πcfailure =
(1− α− β)
Ω
πcsuccess = πcack−idle = πcack−tx = β/Ω, (3.7)
where Ω = 3 + 2(β − α). Then, the fraction of time spent in each state can be
obtained by taking the actual time spent in each state into account. Therefore,
the probability that the channel remains idle for two consecutive backo slots pcii
is given as
pcii =πcidle,idle
πcidle,idle + Lπcsuccess + Lπcfailure + sdackπcidle−ack + Lackπcack−tx + πcidle
=1
1 + (L+ 1)(1− α) + (Lack + sdack)β. (3.8)
Using (3.3), (3.8) and the expression pci = pcii/pci|i, the basic probability p
ci can be
obtained as
pci =1
L+ Lack + 2
[(L+ Lack)
1 + (L+ 1)(1− α) + (Lack + v)β+ 2
]. (3.9)
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 47
In (3.9), the approximated probability that the channel is sensed idle in a
given backo slot pci is expressed as a function of the approximated probability
that a node begins transmission in a given backo slot pnt through α and β. On
the other hand, in (3.1) pnt is a function of pci through pci|i. Moreover, all steady
state probabilities related to both node state model and channel state model
can be expressed as functions of these two basic model probabilities. Therefore,
(3.1)(3.4), (3.8), (3.9), steady state probabilities of the node model, and the
expressions for α, β and q formulate a consistent system of equations that can
be solved numerically. Numerical solution of this analytical model will be used
to investigate the system performance in Section 3.7.
3.4 Simplied Model
The proposed analysis in Section 3.3 has developed a large state space (specially
in the node state model) to exactly model the protocol's behaviour. However,
the presence of a large number of states complicates the computations associ-
ated with the model. To reduce the computational complexity of the analysis,
a simplied version of the proposed model is developed after making some ad-
ditional approximations. In the simplied model, the uniform distribution that
determines the backo counter value is replaced by a geometric distribution of
the same mean [126]. Therefore, all backing o slots that a node dwells in a
given backo stage can be represented by a single state. Furthermore, the limit
for maximum number of retransmission attempts is ignored, and hence it is as-
sumed that nodes keep retransmitting until they receive the corresponding ACK
frames. This eliminates the 3rd dimension (i.e., dierent transmission attempts)
thus simplifying the model. Finally, the simplied model does not consider the
Sleep-to-Idle transition of node's transceiver to avoid erroneous escalations of
backing os in frame retransmissions in the absence of the 3rd dimension. Apart
from the above additional approximations, all other assumptions and approxima-
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 48
tions drawn in Section 3.3 remain unchanged in the simplied model. Modied
node and channel state models of the simplied analysis will be described next
in detail.
3.4.1 Node State Model
Because of the simplifying approximations made in Section 3.4 above, the be-
haviour of an individual node can be modelled using a more simple DTMC as
shown in Figure 3.3. The node is in IDLE state if it does not have a frame to trans-
mit. When the node receives a frame (with probability p) it starts backing o.
BOy states represent the backo stages of node, where y ∈ 1, 2, ...,macMaxCSMA-
Backos+1. The number of back o slots that the node dwells in the yth backo
stage is geometrically distributed with parameter pny , which also determines the
transition from that backo stage to the corresponding rst carrier sensing stage
[126]. pny can be computed as
1− pnypny
=1
wy
wy−1∑z=0
z (3.10)
by considering its same mean property with the equivalent uniform distribution1.
In Figure 3.3, CSy1, CSy2, TX and ACK represent the `rst and second channel
sensing' (of the yth backo stage), `frame transmitting' and `ACK frame await-
ing/receiving' node states, respectively. Parameters p, q and y have the same
denotations those of the detailed analytical model presented in Section 3.3. The
transition probabilities of the DTMC are also shown in Figure 3.3, and the steady
state equations are derived in Appendix A.2. While the expressions for pci and pci|i
in the simplied model remain unchanged with those of the detailed analytical
1 wy is related to the maximum backo window length of the yth backo stage, and it hasbeen quantied in Table 3.1.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 49
Figure 3.3: Discrete-time Markov chain for node in simplied model.
model, the expression for pnt is changed as follows
pnt =pci|i∑y
y=1 π(csy2)
π(idle) + Lπ(tx) +∑y
y=1 π(boy) +∑y
y=1
∑2z=1 π(csyz) + (Lack + sdack)π(ack)
(3.11)
due to the node model simplications. The denominator of (3.11) can be simpli-
ed to 1 + (L− 1)π(tx) + (Lack + sdack − 1)π(ack), considering the normalisation
condition of the Markov chain.
3.4.2 Channel State Model
Since the additional approximations made in Section 3.4 do not aect the channel
model, the same channel state model depicted in Figure 3.2 is used with the
simplied model. Thus, the expressions for pnt|ii, α, β, q, pcii and pci are the
same as those of Section 3.3.2; however, their values are dierent in the simplied
model due to the changes in the steady state equations of the node model and the
expression for pnt . Similar to the detailed analytical model, a system of equations
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 50
(comprised of (3.3), (3.4), (3.8)(3.11), steady state equations for the node model
and the expressions for α, β and q) can be used to solve the simplied model
numerically.
3.5 Extended Model for Networks with Erroneous
Channels
This section extends the analytical model presented in Section 3.3 to analyse
IEEE 802.15.4 networks operating under burst channel errors. From the stand-
point of MAC protocol, the key dierence between an ideal (i.e., error free)
channel and an erroneous channel is their reasons behind discarding frame trans-
missions. In ideal channel conditions, transmitted data frames are discarded
only due to collisions. If a data frame is transmitted without being collided, the
intended receiver-node successfully receives it, and subsequently the transmitter-
node will receive the corresponding ACK.
On the other hand, in networks experiencing erroneous channel conditions, the
transmitted data frames are discarded either due to collisions or due to channel
errors. Although a data frame is received at the intended receiver-node suc-
cessfully, the corresponding transmitter-node may not receive the ACK due to
channel errors. In such situations, the transmitter-node assumes a failure in data
transmission (link timeout), and consequently it retransmits the data frame until
receiving the corresponding ACK or reaching the maximum possible retransmis-
sion attempts. On the other end of the channel, if the receiver-node receives the
retransmitted frame, it would be recognised as a duplicate frame. Each duplicate
frame will be discarded at the receiver node after resending the corresponding
ACK.
Taking the aforementioned new behaviour into account, an extension to the
analysis in Section 3.3 is proposed. The extended analysis is developed for the
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 51
same system model presented in Section 3.2 excluding the ideal common channel.
Instead, a channel with burst errors is considered as described next.
3.5.1 Channel Error Model
The erroneous channel is modelled using the Gilbert-Elliot error model [141][145].
In contrast to the classicmemoryless binary symmetric channel model, the Gilbert-
Elliot model relates current state of the channel to previous channel conditions.
This enables it to describe burst error patterns in transmission channels.
As depicted in Figure 3.4, the Gilbert-Elliot model represents the channel
using two states: good (G) and bad (B)1. The `good' state is free from errors while
Figure 3.4: Gilbert-Elliot channel error model.
the `bad' state represents the channel with errors. The transition probabilities
from good state to bad state and vice versa are given by vg and vb. Then, the
durations that the channel dwells in the good and bad states can be considered as
exponential random variables with mean v−1g and v−1b , respectively [141]. Thus,
the steady-state probabilities of the channel residing in the good and bad states
- πg and πb - can be given as
πg =vb
vg + vb, πb =
vgvg + vb
.
These steady state probabilities will be used in Section 3.5.3 to derive new prob-
abilities of channel state transitions.1Similar two state Markov models have been used to model the bursty characteristics of
channel errors in [146]-[148].
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 52
Based on this channel error model, node state and channel state models of
the extended analysis are developed next.
3.5.2 Node State Model
Erroneous channel conditions do not aect the behaviour of a transmitter-node
except causing it to retransmit more frames to compensate frames dropped due
to channel errors. In the node state model, those additional retransmissions can
be taken into consideration by recomputing the transition probability q (i.e., the
probability that a node receives the corresponding ACK after a data transmission)
as illustrated in Section 3.5.3. Therefore, the node state model presented in
Section 3.3.1 is used with recomputed q values to model the behaviour of a node
in the extended analysis. In contrast, the presence of channel errors signicantly
aects the behaviour of the common channel, and hence major modications are
required to the channel state model presented in Section 3.3.2. This leads to a
modied channel state model.
3.5.3 Modied Channel State Model
The behaviour of common channel under erroneous conditions is modelled using
a modied DTMC as shown in Figure 3.5. Suppose the channel is in IDLE,IDLE
state at the beginning. If a node begins transmission, the channel moves from
IDLE,IDLE state to SUCCESS state given that no collision has occurred and
the channel is error free for the entire duration of data frame transmission. The
no-collision condition occurs with probability β, while the error free condition is
satised with probability (1−Perr−data) = πge−vgL [141]. If any of these conditions
is not satised, the channel moves to FAILURE state with probability σ = 1 −
α− (1−Perr−data)β. The probabilities α and β have their usual meanings and are
expressed as α = (1 − pnt|ii)N and β = Npnt|ii(1 − pnt|ii)N−1. The rest of the state
transitions of the modied DTMC is similar to those presented in Section 3.3.2.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 53
Figure 3.5: DTMC model for burst error channel.
After transmitting a data frame, a node receives the corresponding ACK frame
if there were no collisions and the channel was error free for the entire duration of
transmission (including both data and ACK frames). This occurs with probability
q = (1− Perr)(1− pnt|ii)N−1, where (1− Perr) = πge−vg(L+v+Lack). This modied q
value should be applied to the node state model in the extended analysis.
In the modied channel state model, the probability that the channel remains
idle for two consecutive backo slots pcii is computed by solving the steady state
equations of the DTMC and by computing the fraction of time spent in each chan-
nel state (similar to the channel model in Section 3.3.2). Thus, the probability
pcii of the extended analysis is given as
pcii =1
1 + (L+ 1)(1− α) + (1− Perr−data)(Lack + sdack)β. (3.12)
Considering (3.3), (3.12) and the expression pci = pcii/pci|i, the probability that
the channel is idle at any given backo slot pci can be obtained as
pci =1
L+ Lack + 2
[(L+ Lack)
1 + (L+ 1)(1− α) + (1− Perr−data)(Lack + sdack)β+ 2
]. (3.13)
In the extended analysis, the expressions for pci|i, pnt and pnt|ii remain same as
those in Section 3.3. However, their values have been changed due to modi-
cations made to pci . Once pci|i, pnt and pnt|ii have been computed, the extended
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 54
analysis formulates a consistent system of equations (which includes (3.1)(3.4),
(3.12), (3.13), steady state equations for the node model, and the expressions for
α, β and q) that can be solved numerically.
3.6 Performance Evaluation
The system performance considered in this chapter are the aggregate network
throughput, average power consumption per node, frame discard ratio and frame
delivery ratio per node. Based on the proposed analytical models, they are derived
as follows.
3.6.1 Aggregate Network Throughput
The aggregate network throughput S can be derived considering the fraction of
time the channel spent in successful data transmission S, which is given as
S =Lπcsuccess
πcidle,idle + Lπcsuccess + Lπcfailure + sdackπcack−idle + Lackπcack−tx + πcidle(3.14)
using the steady state probabilities and the dwell times of each state of channel
models.
For systems with ideal channel conditions, the aggregate network throughput
equals S as there are no duplicate data frame transmissions in the channel. There-
fore, the aggregate network throughput of systems with ideal channel conditions
can be given as
Sideal−channel =Lβ
1 + (L+ 1)(1− α) + (Lack + sdack)β(3.15)
by simplifying (4.38).
On the other hand, for the systems with channel errors, S represents the
fraction of time the channel spent in both original and duplicate data frame
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 55
transmissions. Duplicate frames, which are transmitted due to errors in ACK
frame reception, do not contribute to the aggregate network throughput as they
are rejected by the receiving node's MAC layer. Thus, the aggregate network
throughput of systems with channel errors is computed as
Serror−channel =(1− Perr)Lβ
1 + (L+ 1)(1− α) + (1− Perr−data)Lackβ
=πge−vg(L+Lack)Lβ
1 + (L+ 1)(1− α) + (πge−vgL)Lackβ(3.16)
by eliminating duplicate frames' contribution to S.
In both systems, the aggregate network throughput represents a normalised
value and is dimensionless.
3.6.2 Average Power Consumption
The average power consumption of a node is derived by considering the Chip-
con CC2420 802.15.4 RF transceiver1. Energy characteristics of the CC2420
transceiver are illustrated in Figure 3.6 [126][149], while the relationships be-
tween transceiver states and node's activities in each analytical model are listed
in Table 4.2.
The eect of beacon reception may be ignored during throughput calculations,
since beacons occupy a very small fraction of the time. However, neglecting
beacon receptions may not be justied for power calculations as nodes consume
a considerable amount of energy on beacon reception. Therefore, the beacon
reception time is deducted from the node's dwelling time in IDLE state, and
the power consumption budget is adjusted accordingly. Similar adjustments are1Chipcon CC2420 transceiver, which is widely used in many WSN products including Cross-
bow technology's Micaz motes, is selected as a representative of commercial IEEE 802.15.4 RFtransceivers. However, the proposed analytical model does not depend on this selection. Powerconsumption of any other IEEE 802.15.4 transceiver can be predicted by applying the energycharacteristics (i.e., power consumption at each states and transition power and time betweendierent states) of that transceiver to the analysis appropriately.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 56
Figure 3.6: Energy states and transitions of CC2420 transceiver [126][149].
Table 3.2: States of the CC2420 transceiver: Beacon-enabled mode.
State Description Related model statesAnalytical/Extended† Simplied
Sleep Idling IDLE IDLEIdle Backing o BOxyz BOy
Receive Carrier Sensing CSxyz CSyzReceiving ACK ACKx ACK
Transmit Transmitting TXx TX† Since the analytical model presented in Section 3.3 and the extended modelpresented in Section 3.5 share the same node state model, they are placedunder the same column.
made to the transitions among the transceiver states. Furthermore, the radio
ramp-down times are assumed to be negligible. Based on above assumptions, the
average power consumption of a node Yav can be expressed as
Yav = (pni − pnbcn − pnsi)YSleep + (pnbo − pnir + pnsi)YIdle
+(pncs + pnbcn + pnack + pnir)YRx + pntxYTx.(3.17)
In (3.17), YSleep, YIdle, YRx and YTx are the power expenditures correspond-
ing to the transceiver's Sleep, Idle, Receive and Transmit states (which are
explicitly depicted in Figure 3.6) ; pnbcn denotes the fraction of the time spent in
receiving beacons; pnsi and pnir denote those of transceiver's Sleep to Idle transi-
tion and Idle to Receive transition; pni , pnbo, p
ncs, p
nack and p
ntx represent the fraction
of time spent by a node in idling, backing o, carrier sensing, receiving ACK and
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 57
transmitting, respectively. These parameters are calculated in Appendix B.1 for
each analytical model.
3.6.3 Data Transmission Reliability
The reliability of data transmission is evaluated using two performance metrics:
frame discard ratio ρ and frame delivery ratio η.
Frame discard ratio ρ: This is the ratio between the number of discarded
frames at the MAC layer and the number of frames arrived at the MAC layer to
transmit. Hence, it can be expressed as in (3.18) for a given period of time.
ρ =number of discarded frames
number of successful transmissions + number of discarded frames(3.18)
In the analytical model presented in Section 3.3 and the extended analytical
model presented in Section 3.5, frames are discarded either due to y consecutive
channel access failures or to x consecutive transmission failures. Considering node
state DTMCs of these models, the probability of frame discarding due to channel
access failures Pdiscard−CCA−fails and the probability of frame discarding due to
transmission failures1 Pdiscard−TX−fails at a node in a given backo slot can be
derived as in (3.19) and (3.20), respectively.
Pdiscard−CCA−fails =
∑xx=1
[(1− pci)π(csxy0) + (1− pci|i)π(csxy(−1))
]∑x
x=1 [1 + (L− 1)π(txx) + (Lack + sdack − 1)π(ackx)](3.19)
Pdiscard−TX−fails =(1− q)(Lack + sdack)π(ackx)∑x
x=1 [1 + (L− 1)π(txx) + (Lack + sdack − 1)π(ackx)](3.20)
Pdiscard−Simplified =
[(1− pci)π(csy1) + (1− pci|i)π(csy2)
][1 + (L− 1)π(tx) + (Lack + sdack − 1)π(ack)]
(3.21)
Thus, in these two models, the `total probability of frame discarding' Pdiscard1In the analytical model, transmission failures only occur due to frame collisions (owing to
the ideal channel assumption). In contrast, transmission failures in the extended model occureither due to frame collisions or to channel errors.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 58
at a node in a given backo slot can be given as Pdiscard = Pdiscard−CCA−fails +
Pdiscard−TX−fails.
On the other hand, in the simplied model presented in Section 3.4, frames
are discarded only due to channel access failures as the model assumes limitless
retransmission attempts. Therefore, in this model, the probability of frame dis-
carding at a node in a given backo slot Pdiscard−Simplified can be computed as in
(3.21).
Then, by applying (3.18) for a unit backo slot, ρ can be quantied as
ρ =PFrame−discard
(S/NL) + PFrame−discard, (3.22)
where S, N and L represent the aggregate network throughput, number of nodes
in the network and frame length, respectively. PFrame−discard should be substi-
tuted by either Pdiscard or Pdiscard−Simplified depending on the analytical model
considered.
Frame delivery ratio η: This is the ratio between the number of successful
frame transmissions and the total number of frame transmissions. The total
number of frame transmissions can be obtained by adding the number of frames
transmitted successfully and the number of frames discarded due to transmission
failures (Note: the frames discarded due to channel access failures have not been
considered as they were not transmitted). Therefore, by considering a unit backo
period, η can be given as
η =S/NL
(S/NL) + Pdiscard−TX−fails, (3.23)
where S, N and L have their respective meanings as above. In the simplied
model, always η = 1 as there are no transmission failures. Therefore, only the
analytical models presented in Sections 3.3 and 3.5 will be considered to compute
η.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 59
3.7 Results and Discussion
This section presents numerical results obtained from each analytical model to in-
vestigate the performance of beacon-enabled IEEE 802.15.4 networks under the
inuence of dierent network parameters, MAC-layer parameters and channel
conditions. First, the proposed analysis is validated using a system with ideal
channel conditions. Then, the same system is used to investigate the eects
of network parameters (frame arrival rate λ, number of sensor nodes N , frame
length L) and MAC-layer parameters (macMaxFrameRetries, macCSMABack-
os, macMinBE, and macMaxBE ) on the performance of system. Finally, a sys-
tem with channel errors is considered to discuss the eects of erroneous channel
conditions on system performance.
3.7.1 Validation of Analysis
In this section, the analytical models proposed in Sections 3.3 and 3.41 are vali-
dated using a two-fold process. First, the analytical results are veried using ns-2
simulations. Second, they are compared and contrasted with the results of an ex-
isting analysis [126], which models the same system without ACK transmission.
For the validation of analysis, a beacon-enabled (BO = 6), star topology net-
work of 10 sensor nodes was considered. Each node of this network generated
frames of length 10 backo slots based on a Poisson arrival rate of λ frames
per frame duration. MAC-layer parameters were assumed to have their default
values as specied in the standard [61] (i.e., macMinBE =3, macMaxBE =5,
macMaxCSMABackos =4 and macMaxFrameRetries =3). In analytical mod-
els, the beacon duration tbcn, ACK frame length Lack and starting delay sdack
of ACK transmission were set to 2, 2 and 1 backo slots, respectively (assum-
ing 2.4-GHz PHY layer). For all ns-2 simulations, IEEE 2.4-GHz PHY layer
1The extended analytical model presented in Section 3.5 will be validated while investigatingthe eects of erroneous channel conditions in Section 3.7.4.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 60
was used along with the two-ray ground propagation model. A simulation trial
ran until each node completes 20, 000 frame transmissions. Each simulation data
point was averaged over 20 independent simulation trials using dierent random
seeds. Approximations used for the analytical models were not considered for
simulations.
(a) S (b) Yav
(c) ρ (d) η
Figure 3.7: Performance of beacon-enabled IEEE 802.15.4 networks with andwithout ACK transmission (N = 10 and L = 10 backo slots).
Performance of the protocol (in terms of the aggregate network throughput S,
power consumption per node Yav, frame discard ratio ρ and frame delivery ratio
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 61
η) obtained from both analytical models and simulations are compared in Figure
3.7. The close agreement between simulations and analytical results demonstrates
the validity of the proposed models. Results obtained for an equivalent network-
without-ACK [126] are also included in Figure 3.7 for comparison.
For both network scenarios (i.e., with and without ACK), the aggregate
throughput S remains almost the same at low λ values where data frames collide
rarely. On the other hand, a network-with-ACK shows a less aggregate through-
put than the network-without-ACK at high λ values. This is because nodes in the
network-with-ACK have to share the common channel not only to transmit data
frames but also to receive ACK frames and to retransmit collided data frames.
Despite having dierent face values, the aggregate throughput of both networks
follow a similar trend. They maximise at λ ≈ 0.2, and then decrease slowly
towards the saturation region (i.e., λ ≥ 1) as shown in Figure 3.7(a).
In contrast to the throughput results, the average power consumption per
node Yav in the network-with-ACK is greater than that of its counterpart (Fig-
ure 3.7(b)). This is caused by the excess energy consumed while receiving ACK
frames and retransmitting collided data frames. The frame discard ratio ρ of both
network scenarios have a similar behaviour as shown in Figure 3.7(c). In both
networks, ρ is insignicant in the low λ region; however, it escalates rapidly with
increasing λ. Although the face values of ρ in the two networks dier marginally,
their compositions vary largely from each other. While the network-without-ACK
discards a large amount of data frames due to collisions1, in the network-with-
ACK data frames are dropped mainly due to channel access failures. It appears
that the retransmission mechanism forces a large amount of frames to be discarded
due to lack of buering. Nevertheless, the retransmission mechanism assures suc-
cessful delivery of transmitted frames as depicted in Figure 3.7(d). On the other
hand, the network-without-ACK, which has approximately similar ρ values with1In systems with ideal channel conditions, transmission failures occur only due to frame
collisions. Therefore, `the frames discarded, due to transmission failures' are denoted as `theframe discarded due to collisions' throughout this section.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 62
its counterpart, performs poorly in frame delivery due to the absence of a frame
retransmission mechanism.
Figure 3.7 also explains the implications of the simplications proposed in Sec-
tion 3.4. Because of the approximation of endless retransmissions, the simplied
mode does not drop frames due to collisions. In contrast, dropped frames due to
collisions1 can be seen in the full analytical model where retransmission is limited
(Figure 3.7(c)). Therefore, the simplied model always yields 100% frame deliv-
ery as shown in Figure 3.7(d). Moreover, due to approximated backo counter
(based on geometric distribution), nodes in the simplied model back o slightly
more than nodes in the full analysis. This increased backing o time and extra
retransmissions may cause a marginal reduction in average network throughput
(Figure 3.7(a)) and a marginal increase in power consumption (Figure 3.7(b)).
The above implications of the simplied model can also be observed in further
results presented in coming sections.
3.7.2 Eects of Network Parameters
This section investigates the eects of network parameters - in particular the
frame length and number of nodes - on system performance. Except the values
of frame length L and number of nodes N , the same system parameter values
mentioned in Section 3.7.1 were applied for this investigation.
Figure 3.8 illustrates the system performance obtained for three dierent
frame lengths: 5, 8, and 12 backo slots. The aggregate throughput of networks
with dierent frame lengths are similar in the low λ region. This is because net-
works with short frame length compensate their short channel occupancy (per
transmitted frame) by transmitting more frames than their counterparts in a
given period of time2. However, when λ increases, more transmissions cause1Even though, frame dropped due to collision is negligible compared with that due to
channel congestion2For a given λ, short frame lengths yield more frame arrivals, since λ is expressed per frame
length.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 63
(a) S (b) Yav
(c) ρ (d) ρ and its composition
Figure 3.8: Eects of frame length L on the performance of beacon-enabled IEEE802.15.4 networks (when N = 10).
more frame collisions which in turn result more frame retransmissions. There-
fore, in high λ region, networks with short frames exhibit reduced throughput
than their counterparts with long frames (Figure 3.8(a)). The higher the number
of frames to transmit the higher the energy used for backing o, channel sensing,
transmitting and ACK receiving. This explains the high power consumption per
node observed in networks with shorter frames throughout the entire range of λ.
Similar to throughput results, the frame discard ratio ρ shows almost the same
behaviour in networks with dierent frame lengths at low frame arrival rates.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 64
However, at high λ values, networks with long frames discard more frames due
to channel access failures as the `probability of channel is sensed busy' in such
networks escalates with increased λ.
(a) S (b) Yav
(c) ρ (d) ρ and its composition
Figure 3.9: Eects of number of nodes N on the performance of beacon-enabledIEEE 802.15.4 networks (when L = 10 backo slots).
Eects of the number of nodes N on system performance is depicted in Fig-
ure 3.9. At low frame arrival rates, the aggregate throughput increases with the
number of nodes while the power consumption per node remains unchanged (Fig-
ures 3.9(a) and (b)). This behaviour can be mainly contributed to two reasons.
First, each nodes of all considered networks generates a similar number of frames
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 65
for a given λ value. Second, all most all frames are successfully transmitted in the
low λ region irrespective of the number of nodes in the network (Figures 3.9(c)
and (d)).
The larger the number of nodes, the sooner the network reaches the peak in
aggregate throughput. After reaching its peak, the throughput of larger networks
start decreasing towards saturation. However, in this region of λ, the aggregate
throughput of small networks keep on increasing towards their respective peaks,
which means the number of frames transmitted by a node in small networks could
be greater than that of large networks in this region. This explains the higher
power consumption per node observed in small networks in the same region.
For all networks considered, the number of collisions and channel occupancy
increase with λ yielding a rapid escalation in the frame discard ratio. However,
the situation is worst in larger networks as more nodes lead to more collisions
and higher channel occupancy as shown in Figures 3.9(c) and (d).
3.7.3 Eects of MAC-Layer Parameters
In this section, the proposed analytical models are used to investigate the eects
of dierent MAC-layer parameters including the number of frame retransmission
attempts (macMaxFrameRetries), number of backo stages (macCSMABackos)
and length of backo window (macMinBE, and macMaxBE ) on system perfor-
mance. The system parameters mentioned in Section 3.7.1 were applied for
these investigations with necessary modications to MAC parameters mac-
MaxFrameRetries, macCSMABackos, macMinBE, and macMaxBE as sum-
marised in Table 3.3. Furthermore, number of nodes N and frame length L were
set to 10 and 10 backo slots, respectively. Both analytical and simulation results
were obtained for ve dierent λ values that represent the entire range of frame
arrival rates.
Figures 3.10(a) 3.10(d) show the behaviour of system performance for three
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 66
Table 3.3: MAC-layer parameter values for dierent investigations.
Investigation Investigation Investigationon frame on number on length
MAC Parameter retransmission of backo of backoattempts stages window
macMaxFrameRetries (1,3,5) 3 3macMaxCSMABackos 4 (1,3,5) 4macMinBE 3 3 (2,3,4)macMaxBE 5 5 (3,5,8)
(a) S (b) Yav
(c) ρ (d) η
Figure 3.10: Eects of macMaxFrameRetries on the performance of beacon-enabled IEEE 802.15.4 networks.
dierent macMaxFrameRetries values: 1, 3 and 5. For comparison, these gures
also include the system performance related to boundless retransmission attempts,
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 67
(a) S (b) Yav
(c) ρ
Figure 3.11: Eects of macMaxCSMABackos on the performance of beacon-enabled IEEE 802.15.4 networks.
which was obtained from the simplied analytical model. The number of frame
retransmission attempts appears to have no signicant impact on the aggregate
throughput and power consumption of nodes as they exhibit identical behaviour
for dierent macMaxFrameRetries. In contrast, a noticeable increase in the num-
ber of `frames discarded due to collisions' can be observed with decreasing mac-
MaxFrameRetries. This eect is further illustrated in Figure 3.10(d) in which the
lowest macMaxFrameRetries value is shown to be ineective in delivering frames
successfully (in particular at high λ values). For low macMaxFrameRetries val-
ues, this behaviour is self-explanatory as the collided frames (i.e., the frames
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 68
which were not succeeded by corresponding ACKs) are easily discarded without
getting further chance for retransmission.
Eects of the maximum number of backo stages on system performance
is depicted in Figures 3.11(a) 3.11(c)1. Clearly, the higher the macMaxCS-
MABackos value the lower the number of frame discarded due to channel access
failures (Figure 3.11(c)). Although this signicant decline in frame discard ra-
tio should lead to a rapid escalation in aggregate throughput, only a moderate
increase can be observed in throughput with increased macMaxCSMABackos
(Figure 3.11(a)). This is because networks with less number of backo stages
transmit more frames than their counterparts, since low macMaxCSMABackos
values make nodes ready to receive frames from upper layers more frequently2.
Thus, for a given period of time, nodes with less number of backo stages spend
more time on channel sensing (i.e., radio in receiving state) than nodes with
higher number of backo stages, which spend more time on backing o (i.e., ra-
dio in idle state). This explains the increased average power consumption per
node observed with decreasing macMaxCSMABackos in the high λ region (Fig-
ure 3.11(b)).
Next, the impact of the backo window length on system performance is
studied considering three dierent combinations of macMinBE and macMaxBE
values: (2,3), (3,5) and (4,8). Results of this study are shown in Figure 3.12.
Networks with shorter backo windows experience an excessive amount of frame
discarding mainly due to channel access failures as shown in Figure 3.12(c). This
is because shorter backo windows force nodes to perform channel sensing more
frequently. On the other hand, networks with longer backo windows have the
benet of delaying frame transmissions, and hence they rarely discard data frames
due to channel access failures. This explains the increased aggregate throughput1In Figures 3.11 and 3.12, the eects of the number of backo stages and backo window
length on η are not shown explicitly. η remains ≈ 1 with varying values of these two parameters,since the amount of frames discarded due to collisions is negligible as shown in Figures 3.11(c)and 3.12(c).
2By forcing nodes to discard frames instead of keeping them backing o.
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 69
(a) S (b) Yav
(c) ρ
Figure 3.12: Eects of the backo window length on the performance of beacon-enabled IEEE 802.15.4 networks.
of such networks observed particularly in the high λ region, in which the channel
is busy in most of the time. Due to frequent channel sensing, nodes in networks
with shorter backo windows consume more energy than their counterparts in
networks with longer backo windows. This increment is more signicant at
higher frame arrivals due to the escalation of oered trac.
Comparing the eects of aforementioned MAC-layer parameters (i.e., number
of frame retransmission attempts, number of backo stages and length of the
backo window) on system performance, it can be observed that all of them have
a similar impact on the aggregate network throughput (Figures 3.10, 3.11 and
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 70
3.12). On the other hand, the backo window length appears to have a higher
impact on system performance in terms of average power consumption and frame
discard ratio than the other two parameters. Therefore, it is recommended to
tune the backo window length before other two MAC parameters to achieve
better performance (related to power consumption and transmission reliability)
in a given beacon-enabled IEEE 802.15.4 network with ACK transmission.
3.7.4 Eects of Channel Errors
This section investigates the eects of channel errors on the performance of
beacon-enabled IEEE 802.15.4 protocol. In addition, it validates the extended
analysis presented in Section 3.5 using numerical and simulation results. To this
end, the same system mentioned in Section 3.7.1 was considered with three dif-
ferent non-ideal channels : channel 1 (v−1g = 100ms, v−1b = 10ms), channel 2
(v−1g = 50ms, v−1b = 10ms) and channel 3 (v−1g = 20ms, v−1b = 10ms), where v−1g
and v−1b represent the mean duration of good and bad states, respectively. Similar
channel characteristics have been used to model erroneous channels in [141].
Figure 3.13 illustrates the eects of channel errors on system performance.
Performance of an equivalent network with ideal channel conditions, obtained
from the analysis in Section 3.3, is also presented for comparison. The per-
formance of networks with channel errors exhibit similar trends to that of the
network with ideal channel. However, as expected, the system performance de-
grades with rising channel errors as shown in Figure 3.13. This performance
deterioration can be mainly attributed to the increased frame retransmissions
caused by channel errors, which in turn increase the average time that frames
dwell in transmitter-nodes. Longer average dwell times increase frame discarding
due to channel access failures while reducing oered trac due to lack of buer-
ing. Therefore, the frame discard ratio ρ increases notably with rising channel
errors causing a signicant throughput reduction. On the other hand, the average
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 71
(a) S
(b) Yav (c) ρ
Figure 3.13: Eects of channel errors on the performance of beacon-enabled IEEE802.15.4 networks (N = 10 and L = 10 backo slots).
power consumption per node Yav exhibits only a marginal increment with poor
channel conditions. This is because the additional power consumed for frame
retransmissions is neutralised by the power conserved due to reduction of oered
trac. As shown in Figure 3.13, the close agreement between analytical results
and simulations validates the extended analysis for the entire range of λ and
erroneous channel conditions considered.
Outcomes of this chapter have been published in [150]-[153].
3. BEACON-ENABLED IEEE 802.15.4 MAC WITH ACKS 72
3.8 Conclusion
ADTMC based analysis is presented to analyse the beacon-enabled IEEE 802.15.4
MAC protocol by integrating the characteristics of ACK transmission and frame
retransmissions into one of the existing models. Making some approximations,
the proposed model is simplied to a mathematically less complex model with the
cost of marginal aws in performance (≤ |5|% of deviation from original results).
Furthermore, an extended version of the proposed model is presented to analyse
the performance of IEEE 802.15.4 networks operating under non-ideal channel
conditions. The proposed analytical models can be used to derive the perfor-
mance of protocol including the aggregate network throughput, average power
consumption per node, frame discard ratio and frame delivery ratio. Numerical
results obtained from the proposed models are validated using ns-2 simulations.
Due to the generalised nature of the proposed models, they can be used to analyse
the eects of dierent network and MAC-layer parameters on the performance of
protocol.
Chapter 4
Analysis of Non-beacon-enabled
IEEE 802.15.4 MAC Protocol
4.1 Introduction
WSNs have been deployed in many event detection (ED) applications where
events occur randomly and rarely [154]-[156]. In such applications, the nodes'
transceivers wake up only in the presence of events, and remain switched o dur-
ing long inter-event durations to conserve energy. Thus, the periodic transmission
of beacons may not complement with the nature of these monitoring applications.
Therefore, the non-beacon enabled mode of the IEEE 802.15.4 protocol is pre-
ferred over the beacon enabled mode for such event monitoring applications.
In literature, the performance of non-beacon enabled IEEE 802.15.4 networks
has been evaluated using simulations [157], experiments [158] and analytical mod-
els [159]-[171]. An early attempt to determine the upper limits of the throughput
and delay performance of the non-beacon enabled mode can be found in [159], in
which a single communication link between a transmitter-receiver pair is analysed.
Recently, the performance of the non-beacon enabled mode has been evaluated
in the context of a network of nodes, by considering more than one communica-
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 74
tion link [160]-[171]. Among these studies, the models presented in [160]-[163],
and [164] consider two successive CCAs similar to the beacon enabled mode,
and hence fail to model the non-beacon enabled protocol accurately. Kim et al.
[89] have modelled the protocol using a M/G/1 queuing system by overlooking
the collisions in data transmission. The analyses presented in [165]-[167] have
evaluated the performance of the IEEE 802.15.4 non-beacon enabled mode for
networks with specic behaviours. More specically, Buratti and Verdone [165]
analyse the protocol in a star topology network where all data transmissions are
triggered by a request from the network coordinator. Gribaudo et al. [166] carry
out a transient analysis of the protocol for sensor networks deployed in k-coverage
applications. In [167], Fischione et al. have modelled the performance of the non-
beacon enabled mode in a cluster network topology, in which the IEEE 802.15.4
protocol is used on top of a preamble sampling MAC.
The comprehensive analyses presented in [168] and [169] have assumed a single
backo slot as the basic time unit of the protocol's time evolution. Thus, they
are unable to integrate specic time constraints such as CCA duration and node's
receive to transmit (RX-to-TX) turnaround time, which have signicant impacts
on the protocol performance, into the analyses. Whilst, the analysis in [168]
overlooks the collisions between data frames and ACK frames1, the model in
[169] does not consider ACK transmission at all.
On the other hand, the protocol's continuous time evolution, and non-negligible
CCA duration and Rx-to-Tx turnaround have been taken into account in the
studies presented in [170] and [171]. In [170], only the saturated throughput per-
formance of the protocol has been evaluated using a semi-Markov process based
model. Given the number of nodes competing for channel access and their frame
generation rates, Goyal et al. [171] have analysed the frame loss probability and
transmission delay of the non-beacon-enabled IEEE 802.15.4 protocol. However,1Even though this type of collisions do not occur in the beacon enabled mode, it is a common
scenario in the non-beacon-enabled mode as discussed in Section 4.2.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 75
neither of these analyses derives the energy performance of the protocol, which
is vital in the context of WSNs.
This chapter presents a Markov chain based analysis to model the non-beacon-
enabled IEEE 802.15.4 MAC protocol under unsaturated trac conditions. First,
the unslotted CSMA/CA protocol - the only channel access mechanism specied
for the non-beacon-enabled mode - is analysed without considering optional ACK
transmissions. Then, the proposed analysis is extended to model the advanced
behaviour of the protocol in the presence of ACK transmissions. The proposed
models meticulously follow the specications for the IEEE 802.15.4 non-beacon-
enabled mode by considering a single CCA procedure and nite time durations
involved in CCA and node's Rx-to-Tx turnaround. The performance of the proto-
col in terms of the aggregate network throughput, power consumption per node,
frame discarding ratio and frame delivery ratio is evaluated using the proposed
analysis. The validity of the analysis is demonstrated over a range of networks
with varying number of nodes and frame lengths. Numerical results are substan-
tiated through extensive ns-2 simulations.
4.2 Collision of Transmissions
One of the major drawbacks in contention based MAC protocols is the collision
of transmissions of contending nodes. Both CSMA/CA mechanisms (i.e., slotted
and unslotted) specied in the IEEE 802.15.4 standard provide counter measures
(e.g., random backos and CCAs) to minimise the collision of transmissions.
However, collisions can not be completely eliminated due to the following reasons:
• Simultaneous beginning of transmissions in two or more nodes,
• Non-negligible Rx-to-TX turnaround time,
• Presence of hidden nodes (i.e., nodes that are not within the carrier sensing
range of a given node).
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 76
Collisions due to hidden nodes may occur in any contention based wireless net-
work. Their impact on the performance of IEEE 802.15.4 based networks will
be investigated separately in Chapter 5. Thus, the discussion in this section is
conned only to the collisions occurred due to the simultaneous beginning of
transmissions or non-negligible Rx-to-Tx turnaround time. The nature of these
collisions in the slotted and unslotted CSMA/CA mechanisms is shown in Figure
4.1 considering the transmissions of two contending nodes (Node A and Node B).
(a) Collisions in slotted CSMA/CA
(b) Collisions among data frames in unslotted CSMA/CA
(c) Collisions between ACK frames and data frames in un-slotted CSMA/CA
Figure 4.1: Collision of transmissions: slotted CSMA/CA vs. unslottedCSMA/CA.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 77
In the slotted mechanism, collisions do not occur due to the non-negligible
Rx-to-Tx turnaround time, since the discrete time unit (i.e., unit backo slot)
of that mechanism is greater than the turnaround duration1. Thus, a collision
occurs if and only if two or more nodes perform their rst CCA at the same backo
slot, which causes a simultaneous beginning of frame transmissions as shown in
Figure 4.1(a). Due to the discrete time evolution of the slotted mechanism, the
only possible time to encounter a beginning of a collision is the same backo slot
boundary that the frame transmission begins (Figure 4.1(a)).
On the other hand, collisions in the unslotted mechanism may occur due to
the simultaneous beginning of transmissions as well as the nite time of Rx-
to-Tx turnaround. However, the continuous time evolution and unsynchronised
behaviour of the mechanism reduce the possibility of beginning of simultaneous
transmissions, causing the Rx-to-Tx turnaround to be the main reason behind
the collisions. The non-negligible Rx-to-Tx turnaround time in the unslotted
mechanism may cause not only collisions among data frames but also collisions
between ACK frames and data frames2 as described below:
Collisions among data frames: Suppose Node A nishes its backo phase
before the node B at time t (Figure 4.1(b)). Then Node A performs a CCA
and Rx-to-Tx turnaround for tcca (8 symbols) and tta (aTurnaroundTime = 12
symbols) durations, respectively. Despite the Node A's activities, the channel will
remain idle until time t+ tcca+ tta at which Node A begins its frame transmission.
Meanwhile, if Node B begins its CCA at time t′, where t ≤ t′ ≤ (t + tta), it
would succeed and the subsequent frame transmission would collide with Node
A's transmission3. In contrast to the slotted mechanism, the possible time to
encounter a beginning of a collision for a given frame in the unslotted mechanism1Rx-to-Tx turnaround takes 12 symbol durations (= 0.6 unit backo slots).2In the slotted mechanism, collisions between ACK frames and data frames are avoided by
using two back-to-back CCAs.3When node B begins its CCA simultaneously with Node A at time t, the subsequent
collision occurs due to the simultaneous beginning of transmissions.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 78
is not an instant but a window of time. For collisions among data frames, this
window is equal to tta (i.e., aTurnaroundTime symbols), and it will be denoted
as the First Collision Window throughout this chapter (Figure 4.1(b)).
Collisions between ACK frames and data frames: Suppose Node A
has completed its data frame transmission successfully at time t. Then, the
destination node needs to complete a Rx-to-Tx turnaround before sending the
corresponding ACK frame. If node B begins a CCA at time t′, where t ≤ t′ ≤
(t + tta − tcca), that CCA would succeed and a subsequent data transmission
would collide with the ACK transmission destined towards Node A as shown in
Figure 4.1(c). The possible time window to begin this type of collision equals to
(tta − tcca) symbol durations, and it will be referred to as the Second Collision
Window throughout this chapter (Figure 4.1(c)).
In an ACK-enabled IEEE 802.15.4 based network, the impact of a collision
between an ACK frame and data frames is almost same as that of a collision
between two data frames, because in both collision scenarios all associated nodes
have to retransmit their respective data frames (i.e., in the scenario depicted
in Figure 4.1(c), not only Node B but also Node A has to retransmit its data
frame, even though Node A's data frame has been received successfully at the
destination node).
Thus, the success of data transmission in non-beacon-enabled IEEE 802.15.4
networks depends not only on sensing the idle channel successfully, but also on
commencing the CCA at proper time instants. Therefore, this chapter analyses
the non-beacon-enabled IEEE 802.15.4 MAC protocol based on the following
basic probabilities:
• The probability that a node begins a CCA in a given time instant,
• The probability that the channel is sensed idle.
These two probabilities will be determined for the system model described next.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 79
4.3 System Model
Consider a non-beacon-enabled IEEE 802.15.4 single-hop star topology network
that consists of a network coordinator and N sensor nodes. The network coor-
dinator acts as a common receiver for all sensor nodes, and all nodes are within
the carrier sensing range of each other. Data transmission within the system is
assumed to be in the uplink direction. Ideal channel conditions are assumed, so
that transmitted data frames can be lost only due to frame collisions. All data
frames are equal in length, and the transmission of a data frame lasts for xed-L
backo slots. Data frames arrive at the nodes according to a Poisson distribution
with an arrival rate of λ frames per frame duration. Buering at the nodes is
not considered; therefore, a node that already holds a frame will discard further
frame arrivals. Moreover, the capture eect [142][143] is not implemented for
collided frames, i.e., all collided frames are dropped regardless of their received
signal strengths at the common receiver.
4.3.1 Approximations
In the system considered, the computation of the basic probability that a node
begins a CCA in a given time instant is mathematically intractable due to the con-
tinuous time evolution of the unslotted IEEE 802.15.4 MAC protocol1. Therefore,
for tractability, the continuous time evolution of the protocol is approximated by
a discrete time evolution where the discrete time unit is equal to one symbol
duration. These discrete time units are referred to as mini-slots, and all nodes
are assumed to be synchronised at mini-slot boundaries. Similar approximations
have been used in [164] and [166] to model the unslotted CSMA/CA protocol.
Although the unslotted protocol with new time discretisation resembles a slot-1This is the shortened form used to refer to the `unslotted CSMA/CA mechanism of the
IEEE 802.15.4 MAC protocol' in this chapter.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 80
Figure 4.2: Timing of starting the same event in dierent protocols.
ted protocol, the diminutive duration of a mini-slot1 ensures an analogy between
the proposed time discretisation and the timing of events in the unslotted IEEE
802.15.4 protocol (see Figure 4.2).
Because of the proposed discrete time evolution, the `probability that a node
begins a CCA in a given time instant ' can be represented by the `probability that
a node begins a CCA in a given mini-slot '. For the simplicity of the analysis
this probability is approximated by the steady state probability pncca that a node
begins a CCA. In [170] and [172], it has been shown that the other basic model
probability - the probability that the channel is sensed idle - varies depending
on the current backo stage of the channel sensing node. However, results of
the same studies demonstrate that this variation is signicant only in networks
where the number of nodes N < 10. Therefore, in this chapter, the `probability
that the channel is sensed idle' is assumed constant, and it is approximated by
the steady state probability pci that the channel is idle for 8 consecutive mini-
slots (i.e., duration of a CCA). These approximations provide the basis for the
following analyses.1= (1/20) × backo slot , where backo slot is the discrete time unit of the slotted IEEE
802.15.4 protocol.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 81
4.4 Analytical Model without ACKs
This section analyses the unslotted IEEE 802.15.4 MAC protocol in the absence of
ACKs by formulating two DTMCs to model the behaviour of an individual node
and the common channel. The two DTMCs are coupled and solved numerically
for the basic model probabilities pncca and pci to evaluate the performance of the
protocol.
4.4.1 Node State Model
Consider a node that follows the unslotted IEEE 802.15.4 MAC protocol to ac-
cess the channel. It behaves almost similar to a node in the rst transmission
attempt of the slotted protocol described in Chapter 3 Section 3.3.1. Thus,
the node behaviour can be modelled using a DTMC as shown in Figure 4.3.
The node begins the backo mechanism when it receives a frame to transmit,
which happens with probability p (i.e., the probability of frame arrival in a given
mini-slot, p = λ/20L). Node's backing o is denoted by BOyz states, where
y ∈ 1, 2, ...,macMaxCSMABackos + 1 and z ∈ 1, 2, ..., wy−11 represent the
current backo stage and backo counter value, respectively. The node resides in
each BOyz state for a single backo slot duration (i.e., 20 mini-slots).
When the rst backing o expires, the node moves to CS1 state, which rep-
resents the 8 mini-slot long channel sensing state2 of the rst backo stage. If
the channel is found to be idle during the sensing, which occurs with probabil-
ity pci , the node enters the TA state representing node's transceiver Rx-to-Tx
turnaround. After expiring the 12 mini-slot turnaround time, the node moves to
TX and resides 20L mini-slots in there to transmit the data frame. At the end of1wy = 2BEy , where BEy is the backo exponent of the yth backo stage.)2In the slotted CSMA/CA analytical models presented in Chapter 3, the channel sensing
states (i.e., CSxy) were considered one backo slot long (=20 symbol durations), since allMAC layer events in the slotted mechanism occur at the boundaries of backo slots. Thatrepresentation can be explained as follows: For a successful channel sensing CSxy representschannel sensing + Rx-to-Tx turnaround (8+12 = 20 symbols). Otherwise CSxy representschannel sensing + remaining time to begins the next backing o (8+12 = 20 symbols).
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 82
Figure 4.3: DTMC model for node without ACKs. The steady state probabilitypci is denoted by a .
the data transmission, node moves back to the IDLE state. On the other hand, if
the channel had been found busy when the node was in the CS1 state, the node
moves to the next backo stage and continue the backo procedure. This mech-
anism is repeated up to y backo stages, where y = macMaxCSMABackos + 1.
If the channel is found busy in the CSy state, the node declares a channel access
failure and moves back to the IDLE state. Possible node states and their dwell
times are summerised in Table 4.1.
Table 4.1: Node states and their dwell times.
State Description Dwell time(in mini slots)
IDLE Idling 1BOyz Backing o 20CSy Channel sensing 8TA Rx-to-Tx turnaround 12TX Transmitting 20L
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 83
Based on the aforementioned state transitions, the steady state equations of
the node model DTMC (Figure 4.3) can be given as
π(idle) = (1− p)π(idle) + (1− pci)π(csy) + π(tx)
π(bo1z) = gpπ(idle)/w1 + hπ(bo1(z+1))
where (g, h) =
(0, 1) 1 ≤ z ≤ 2; (4, 1) z = 3
(1, 1) 4 ≤ z < w1 − 1; (1, 0) z = w1 − 1
π(cs1) = π(bo11)
π(boyz) = (1− pci)π(cs(y−1))/wy + hπ(boy(z+1))
where 2 ≤ y ≤ y; h =
1 1 ≤ z < wy − 1
0 z = wy − 1(4.1)
π(csy) = (1− pci)π(csy−1)/wy + π(boy1) where 2 ≤ y ≤ y
π(ta) = pci
y∑y=1
π(csy)
π(tx) = π(ta)
where w1 = 2macMinBE and wy = 2BEy . The notation π(state) represents the
long term proportion of transitions into STATE. The normalised condition of the
DTMC is also expressed as
π(idle) +
y∑y=1
wy−1∑z=1
π(boyz) +
y∑y=1
π(csy) + π(ta) + π(tx) = 1. (4.2)
Let θ = π(idle) and φ = (1 − pci). Then, the balanced equations in (4.1) are
rearranged to obtain
wy−1∑z=1
π(boyz) =wy − 1
2φy−1pθ where 1 ≤ y ≤ y (4.3)
π(csy) = φy−1pθ where 1 ≤ y ≤ y (4.4)
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 84
π(ta) = π(tx) = (1− φ)
y∑y=1
π(csy) = (1− φy)pθ. (4.5)
Thus, (4.2) can be simplied to
[1 +
y∑y=1
wy − 1
2φy−1p+
1− φy
1− φp+ 2(1− φ)yp
]θ = 1. (4.6)
Considering the steady state probabilities and dwell times of each DTMC state,
the steady state probability pncca that a node begins a CCA is obtained as
pncca =
∑yy=1 π(csy)
Λ, (4.7)
where Λ = θ+20∑y
y=1
∑wy−1
z=1 π(boyz)+8∑y
y=1 π(csy)+12π(ta)+20Lπ(tx). Then,
for a given network pncca can be expressed as a function of θ and φ by substituting
expressions (4.3), (4.4) and (4.5) in (4.7). Since θ is a function of pci through
the expression of φ and (4.6), the basic model probability pncca can be completely
determined by the other basic model probability pci . To compute the probability
pci , the common channel seen by all sensor nodes is analysed next.
4.4.2 Channel State Model
The behaviour of the channel can be modelled using a Markov chain as shown
in Figure 4.4. Assume that the channel is in SUCC state, which represents the
channel during a successful data transmission. After a data transmission, the
channel remains idle until the beginning of the next data transmission, which
takes at least another 20 mini-slots (8 mini-slot CCA + 12 mini-slot RX-to-
TX turnaround). Therefore, the channel is certainly idle during the rst 20
mini-slots after a transmission; however, channel idleness may extend beyond
this guaranteed duration if none of the contending nodes attempt to access the
channel.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 85
Figure 4.4: DTMC model for channel without ACKs.
The channel idleness is represented by IDLEC1 and IDLEC2 states as shown in
Figure 4.4. The IDLEC1 state represents the rst 19 mini-slots of the idle channel
after a data transmission. No data transmission begins during this state. The
remaining duration of the channel idleness is represented by the single mini-slot
long IDLEC2 state. Next transmission may or may not begin at the end of this
state depending on nodes' activities. The channel remains in the IDLEC2 state if
none of the nodes begin a transmission, which occurs with probability α. This
probability is same as the probability that none of the nodes begin a CCA 20
mini-slots ago. Hence, α = (1 − pncca)N . Conversely, if only one node begins
a transmission, the channel moves to the CW1 state with probability β where
β = Npncca(1− pncca)N−1. The CW1 state represents the rst mini-slot of the rst
collision window described in Section 4.2. On the other hand, if more than one
node begin transmissions the channel enters the FAIL state by experiencing a
collision.
After expiring the CW11 state, the channel enters to CW12 (i.e., second
mini-slot of rst collision window) given that none of the remaining nodes1 begin
transmissions. This happens with propagability γ = (1−pncca)N−1. If at least one
of the remaining node begins a transmission at the end of CW11 state, a collision1The channel being in the collision window implies that a node is already transmitting.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 86
happens. Consequently, the channel falls back to the FAIL state. Following
similar arguments, the channel moves through the remaining mini-slots of the rst
contention window, which are denoted by the CW1i states where 1 ≤ i ≤ 12. If
there was no collision during the entire rst collision window, the channel enters
to the SUCC state. The channel resides (20L− 12) mini-slots in the SUCC state
to complete the data transmission. Conversely, the channel's dwell time in the
FAIL state varies from 20L to (20L+ 12) mini-slots depending on the time that
the collision began within the collision window. For simplicity, the dwell time
of the FAIL state is computed by taking the average of all possible values, and
hence it is given as (20L + 6) mini-slots. Respective dwell times of the channel
states are also shown in Figure 4.4.
According to the channel transition described above, the steady state equa-
tions and the normalisation condition of the channel state DTMC (Figure 4.4)
can be obtained as
π(idlec1) = π(succ) + π(fail)
π(idlec2) = απ(idlec2) + π(idlec1)
π(cw11) = βπ(idlec2)
π(cw1i) = γπ(cw1i−1) where 2 ≤ i ≤ 12 (4.8)
π(succ) = γπ(cw112)
π(fail) = (1− α− β)π(idlec2) + (1− γ)12∑i=1
π(cw1i)
π(idlec1) + π(idlec2) +12∑i=1
π(cw1i) + π(succ) + π(fail) = 1. (4.9)
By considering the dwell times of all channel states, the steady state probability
pci that the channel is idle for 8 consecutive mini-slots can be then expressed as
pci =1219
(19)π(idlec1) + π(idlec2)
Γ(4.10)
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 87
where, Γ = 19π(idlec1) + π(idlec2) +∑12
i=1 π(cw1i) + (20L − 12)π(succ) + (20L +
6)π(fail). The rst coecient (i.e., 12/19) of π(idlec1) at the numerator repre-
sents the fraction of time having 8 consecutive mini-slots during the IDLEC1 state.
In contrast, the corresponding coecient of π(idlec2) is 1, because being in IDLEC2
state guarantees the channel idleness for the previous 8 consecutive mini-slots.
The second coecient (i.e., 19) of π(idlec1) gives the dwell time of the IDLEC1
state in mini-slots.
By solving the steady state equations and normalisation condition in (4.8)
and (4.9), π(idlec2) is obtained as
π(idlec2) =1
2(1− α) + 1 + β(1−γ12
1−γ ). (4.11)
Furthermore, the steady state probabilities of the channel being in all the other
states can be expressed using π(idle2) as
π(idlec1) = (1− α)π(idlec2) (4.12)
π(cw1i) = βγi−1π(idlec2) where 1 ≤ i ≤ 12 (4.13)
π(succ) = βγ12π(idlec2) (4.14)
π(fail) = (1− α− βγ12)π(idlec2). (4.15)
Substituting (4.11) - (4.15) in (4.10), pci can be simplied to
pci =12(1− α) + 1
5(1− α)(5 + 4L) + β(1−γ12
1−γ − 18γ12) + 1. (4.16)
In (4.16), the probability pci has been expressed as a function of the other basic
model probability pncca through α, β and γ. Finally, (4.3)- (4.7) and (4.11) - (4.16)
form a system of equations that can be solved numerically. Numerical results of
this model are used in Section 4.6 to evaluate the performance of the unslotted
IEEE 802.15.4 MAC protocol in the absence of ACK transmissions.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 88
4.5 Analytical Model with ACKs
In this section, the proposed analysis is extended to model the unslotted IEEE
802.15.4 MAC protocol with ACK transmission. To this end, the two DTMCs
presented in Section 4.4 are modied to incorporate ACK transmission and its
consequences. The modied DTMCs are then solved to nd the basic model
probabilities pncca and pci .
4.5.1 Node State Model
Consider a node in a non-beacon-enabled IEEE 802.15.4 network with ACK trans-
mission. The node has to wait for the corresponding ACK frame each time it
transmits data. If the ACK frame was not received, the node retransmits the data
frame up to macMaxFrameRetries attempts. Since receiving ACK is probabilis-
tic in nature, node's current transmission attempt is modelled using a stochastic
process with a random variable x, where x ∈ 1, 2, ...,macMaxFrameRetries +1.
New node states with the relevant transmission attempts are shown in the mod-
ied node state DTMC in Figure 4.5.
The node behaves similar to the model presented in Section 4.4.1 until end of
the data transmission in a given attempt (i.e., up to the expiring of TXx). At
the end of the data transmission, however, the node moves to the ACKx state
instead of moving back to the IDLE state. In the ACKx state the node awaits
the corresponding ACK frame for a period of (aTurnaroundTime + 20Lack) mini-
slots, where aTurnaroundTime and Lack represent the RX-to-TX turnaround
time and ACK frame length, respectively. If the node receives the ACK frame
(with probability q, which will be discussed in Section 4.5.2), it moves to the IDLE
state. Conversely, if no ACK frame is received, the node moves to the rst backo
stage of the next transmission attempt. This process repeats until the node
receives the corresponding ACK frame or tries x transmission attempts, where
x = macMaxFrameRetries + 1. If the node has not received the corresponding
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 89
Figure 4.5: DTMC model for node with ACKs. The steady state probability pciis denoted by a .
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 90
ACK frame after x transmission attempts, it terminates the frame transmission
and moves to the IDLE state after declaring a transmission failure.
The steady state equations of the node state DTMC (Figure 4.5) are derived
in Appendix A.3. Those equations can be rearranged to obtain
wy−1∑z=1
π(bo1yz) =wy − 1
2φy−1pθ 1 ≤ y ≤ y (4.17)
π(cs1y) = φy−1pθ 1 ≤ y ≤ y (4.18)
π(ack1) = (1− φ)
y∑y=1
π(cs1y) = (1− φy)pθ (4.19)
π(ackx) = (1− φy)(1− q)π(ackx−1) 1 < x ≤ x (4.20)wy−1∑z=1
π(boxyz) =wy − 1
2φy−1(1− q)π(ackx−1) 1 < x ≤ x, 1 ≤ y ≤ y (4.21)
π(csxy) = φy−1(1− q)π(ackx−1) 1 < x ≤ x 1 ≤ y ≤ y (4.22)
π(tax) = π(txx) = π(ackx) 1 ≤ x ≤ x, (4.23)
where θ = π(idle) and φ = (1−pci). By considering the steady state probabilities
and dwell times of each node state, the steady state probability pncca that a node
begins a CCA can be obtained as
pncca =
∑xx=1
∑yy=1 π(csy)
Λack
, (4.24)
where Λack = θ +∑x
x=1[20∑y
y=1
∑wy−1
z=1 π(boxyz) + 8∑y
y=1 π(csy) + 12π(tax) +
20Lπ(txx) + (12 + 20Lack)π(ackx)].
In (3.19), π(ack1) is expressed as a function of θ and φ1. Thus, π(ackx), where
1 < x ≤ x, can be determined in terms of θ, φ and q by recursively using (4.20)
with (4.19). Consequently,∑wy−1
z=1 π(boxyz), π(csxy), π(tax) and π(txx), where
1 ≤ x ≤ x and 1 ≤ y ≤ y, can be expressed using θ, φ and q according to1Note: p, the probability of frame arrival, is a known parameter for a given network.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 91
Figure 4.6: DTMC model for channel with ACKs.
(3.21) (3.23). Thus, for a given network, the steady state probability of the
each node state can be completely determined in terms of θ, φ and q. Therefore,
pncca expressed in (4.24) also becomes a function of θ, pci (via φ) and q. Since θ
can be expressed as a function of pci and q using the normalised condition of the
node state DTMC (shown in Appendix (A.3)), the basic model probability pncca
is completely determined by the probabilities pci and q for a given network.
Next, the common channel seen by all sensor nodes is analysed to nd the
probabilities pci and q.
4.5.2 Channel State Model
The behaviour of the channel in a non-beaocn-enabled network with ACK trans-
mission can be modelled using a DTMC as shown in Figure 4.6. The IDLEc1,
IDLEc2, CW1i (1 ≤ i ≤ 12), SUCC and FAIL states represent the same channel
activities that they have represented in Figure 4.4. The CW2j (1 ≤ j ≤ 4) states
denote the second collision windows of the channel, while the channel idleness in
between a success data transmission and the corresponding ACK frame is rep-
resented by IDLEc3 state, which is 12 mini-slots in duration. Finally, the ACK1
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 92
and ACK2 states denote the rst 8 mini-slot and the last (20Lack − 12) mini-slot
durations of the ACK frame transmission where a collision cannot occur.
The channel transitions from the IDLEc1 state up to the SUCC state are sim-
ilar to those of the model presented in Section 4.4.2 with the same transition
probabilities α, β and γ. However, at the end of a successful data transmission
the channel moves to the IDLEc3 state instead of the IDLEc1 state compared with
the DTMC in Figure 4.4. When the common receiver starts the ACK transmis-
sion, the channel moves from IDLEc3 to ACK1 state with probability 1. If none
of the remaining nodes1 begin transmissions at the end of the ACK1 state, where
the second collision window starts, the channel enters CW21 state. Conversely, if
at least one remaining node begins a data transmission at the end of the ACK1
state, the channel falls back to the FAIL state.
Following similar arguments, the channel moves through the remaining mini-
slots of the second contention window. If there were no collisions during the entire
second collision window, the channel enters to ACK2 state and completes the ACK
frame transmission successfully. After the ACK transmission is completed, the
channel becomes idle again. The respective dwell times of each channel state are
shown in Figure 4.6.
The steady state equations and the normailsed condition of the channel state
DTMC (Figure 4.6) can be given by
π(idlec1) = π(ack2) + π(fail) (4.25)
π(idlec2) = α1π(idlec2) + π(idlec1)
π(cw11) = βπ(idlec2)
π(cw1i) = γπ(cw1i−1) where 2 ≤ i ≤ 12
π(succ) = π(idlec3) = π(ack1) = γπ(cw112)
π(cw21) = γπ(ack1)
1The channel being in second collision window implies that a node is waiting for an ACK.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 93
π(cw2j) = γπ(cw2j−1) where 2 ≤ j ≤ 4
π(ack2) = γπ(cw24)
π(fail) = (1− α− β)π(idlec2) + (1− γ)
[π(ack1) +
12∑i=1
π(cw1i) +4∑j=1
π(cw2j)
](4.26)
3∑u=1
π(idlecu) +2∑v=1
π(ackv) +12∑i=1
π(cw1i) +4∑j=1
π(cw2j) + π(succ) + π(fail) = 1.
(4.27)
By solving (4.25) (4.27), π(idlec2) can be obtained as
π(idlec2) =1
2(1− α) + 1 + β(1+2γ12−2γ13−γ171−γ )
. (4.28)
Moreover, the steady state probabilities of the channel being in all other states
can be given in terms of π(idle2) by rearranging (4.25) - (4.26) as follows:
π(idlec1) = (1− α)π(idlec2) (4.29)
π(cw1i) = βγi−1π(idlec2) where 1 ≤ i ≤ 12 (4.30)
π(succ) = π(idlec3) = π(ack1) = βγ12π(idlec2) (4.31)
π(cw2j) = βγ(12+j)π(idlec2) where 1 ≤ j ≤ 4 (4.32)
π(ack2) = βγ17π(idlec2) (4.33)
π(fail) = (1− α− βγ17)π(idlec2). (4.34)
Using the steady state probabilities and dwell times of the channel state
DTMC in Figure 4.6, the steady state probability pci that the channel is idle for 8
consecutive mini-slots can be given as
pci =1219
(19)π(idlec1) + π(idlec2) + 58(8)π(idlec3)
Γack(4.35)
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 94
where, Γack = 19π(idlec1) +π(idlec2) +∑12
i=1 π(cw1i) + (20L− 12)π(succ) + (20L+
6)π(fail)+12π(idlec3)+8π(ack1)+∑4
j=1 π(cw2j)+(20Lack−12)π(ack2). Substi-
tuting (4.29) (4.34) in (4.35), the basic model probability pci can be simplied
to
pci =12(1− α) + 1 + 5βγ12
5(1− α)(5 + 4L) + β[(20L+ 7)γ12 + 1−γ17
1−γ − (20(L− Lack) + 18)γ17]
+ 1.
(4.36)
In (4.36), the probability pci has been expressed as a function of pncca through α,
β and γ (Note: α = (1− pncca)N , β = Npncca(1− pncca)N−1 and γ = (1− pncca)N−1).
To determine the probability q in terms of pncca, consider a node that has just
completed a data transmission. The node will successfully receive the correspond-
ing ACK frame if and only if the following two events occur:
1. None of the remaining nodes begin a data transmission during the st col-
lision window, which happens with the probability γ12,
2. None of the remaining nodes begin a data transmission during the second
collision window, which happens with the probability γ4.
Since these two events are apart more than a duration of a data frame from each
other, they can be considered as independent. Therefore, q = γ12γ4 = γ16, which
is completely determined by pncca for a given network.
Finally, the expression for q, equations (4.17) - (4.24) and (4.28) - (4.36) form
a system of equations that can be solved numerically. Numerical solution of this
model is used to evaluate the performance of the unslotted protocol next.
4.6 Performance Evaluation
In this section, the performance of the non-beacon-enabled IEEE 802.15.4 MAC
protocol is derived using the proposed analytical models. In particular, following
performance metrics are obtained for networks with/witout ACK transmission:
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 95
the aggregate network throughput, average power consumption per node, frame
discard ratio, and frame delivery ratio.
4.6.1 Aggregate Network Throughput
The aggregate network throughput S of networks without ACKs is dened as the
fraction of time S the channel spends in successful transmission. The fraction of
time the channel spends in successful transmission is comprised of the time the
channel dwells in the SUCC state and the time it resides in each CWi (where
1 ≤ i ≤ 12) state for successful transmissions. Since a successful transmission
occurs if and only if the common channel dwells in SUCC state, the steady
state probability that the channel moves into each CWi state for a successful
transmission is equal to the steady state probability that the channel moves into
SUCC state. Therefore, the aggregate throughput S of the networks without
ACKs can be obtained as
S = S (4.37)
=(20L− 12)π(succ) +
∑12i=1 π(succ)
Γ
=20Lβγ12
5(1− α)(5 + 4L) + β(1−γ12
1−γ − 18γ12) + 1. (4.38)
by considering the steady state transition probabilities and dwell times of each
channel state in Figure 4.4.
In networks with ACKs, S is comprised of the fractions of time the channel
spent in both the original and the duplicate data frame transmissions. Duplicate
frames are transmitted due to the corrupt ACK receptions caused by the collisions
in the second collision window. Since the original data frames related to each
duplicate frames have been already received, all duplicate frames are rejected by
the receiving node's MAC layer. Therefore, they do not further contribute to the
aggregate network throughput. Thus, the aggregate network throughput Sack of
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 96
the networks with ACKs is dened as the fraction of time the channel spends in
data transmission followed by successful ACKs. Sack can be derived as
Sack =γ4((20L− 12)π(succ) +
∑12i=1 π(succ))
Γack(4.39)
by considering the steady state transition probabilities and the dwell times of
each channel state in Figure 4.6. In (4.39), γ4 represents the conditional proba-
bility of transmitting the ACK frame successfully given that the corresponding
data transmission was successful. Using the steady state probabilities and the
expression for Γack presented in Section 4.5.2, Sack can be simplied to
Sack =20Lβγ16
5(1− α)(5 + 4L) + β[(20L+ 7)γ12 + 1−γ17
1−γ − (20(L− Lack) + 18)γ17]
+ 1.
(4.40)
In both systems, the aggregate network throughput represents a normalised
value and is unitless.
4.6.2 Average Power Consumption
The average power consumption of a node is determined by considering the char-
acteristics and specications of the Chipcon CC2420 802.15.4 RF transceiver
shown in Figure 3.6. Table 4.2 links the node activities in each analytical model
to the corresponding states of the CC2420 transceiver.
Table 4.2: States of the CC2420 transceiver: Non-beacon-enabled mode.
State Description Related statesWithout ACKs With ACKs
Sleep Idling IDLE IDLEIdle Backing o BOyz BOxyz
Receive Carrier Sensing CSy CSxyReceiving ACK ACK
Transmit Transmitting TX TXx
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 97
Based on the state transition assumptions made in Chapter 3 Section 3.6.2,
the average power consumption of a node for networks without ACKs Yav and
with ACKs Yav−ack can be expressed as
Yav = (pni − pnsi)YSleep + (pnbo − pnir + pnsi)YIdle (4.41)
+(pncs − pnrt + pnir)YRx + (pntx + pnrt)YTx
Yav−ack = (pni − pnsi)YSleep + (pnbo − pnir + pnsi)YIdle (4.42)
+(pncs + pnack − pnrt + pnir)YRx + (pntx + pnrt)YTx.
In (4.42) and (4.43), YSleep, YIdle, YRx and YTx are the power expenditures cor-
responding to the transceiver's Sleep, Idle, Receive and Transmit states. The
parameter pnrt denotes the fraction of the time spent in transceiver's Receive to
Transmit transition. The other parameters pnsi, pnir, p
ni , p
nbo, p
ncs, p
nack and p
ntx have
the same meanings of their counterparts in Section 3.6.2, and their expressions
are given in Appendix B.2.
4.6.3 Data Transmission Reliability
In this section, the reliability of data transmission is evaluated using the perfor-
mance metrics: frame discard ratio ρ and frame delivery ratio η. The denitions
of these two performance metrics can be found in Chapter 3 Section 3.6.3.
Frame discard ratio ρ: In networks without ACKs, data frames are dis-
carded only due to y consecutive channel access failures at the MAC layer of
transmitting nodes. Therefore, the probability of frame discarding of a node in a
given mini-slot Pdiscard−nonBcn−noACK in networks without ACKs can be computed
as
Pdiscard−nonBcn−noACK = (1− pci)× 8π(csy)/Λ. (4.43)
On the other hand, the data frames are discarded either due to y consecutive
channel access failures or to (x−1) consecutive retransmission failures in networks
with ACKs. In such networks, the probability of frame discarding due to channel
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 98
access failures Pdiscard−nonBcnACK−CCA and the probability of frame discarding due
to retransmission failures Pdiscard−nonBcnACK−TX of a node in a given mini-slot can
be derived as
Pdiscard−nonBcnACK−CCA = (1− pci)× 8x∑x=1
π(csxy)/Λack (4.44)
Pdiscard−nonBcnACK−TX = (1− q)(Lack + 12)π(ackx)/Λack. (4.45)
Then, the `total probability of frame discarding' Pdiscard−nonBcn−ACK can be given
as Pdiscard−nonBcn−ACK = Pdiscard−nonBcnACK−CCA + Pdiscard−nonBcnACK−TX .
Considering a mini-slot period, ρ for both types of networks can be expressed
as
ρ =Pdiscard−nonBcn
(S/20NL) + Pdiscard−nonBcn(4.46)
where S, N and L represent the aggregate network throughput, number of nodes
in the network and frame length, respectively. Pdiscard−nonBcn should be substi-
tuted by either Pdiscard−nonBcn−ACK or Pdiscard−nonBcn−noACK depending on the
presence or absence of ACKs in the network considered.
Frame delivery ratio η: This can only be computed using analytical models
that include a retransmission mechanism as described in Section 3.6.3. Therefore,
η is obtained only for networks with ACKs, and it is given as
η =S/20NL
(S/20NL) + Pdiscard−nonBcnACK−TX(4.47)
where S, N and L have their respective meanings.
4.7 Results and Discussion
This section presents the numerical results of the proposed analytical models
and investigates the performance of the unslotted CSMA/CA mechanism in non-
beacon-enabled IEEE 802.15.4 WSNs. First, the analytical results of the basic
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 99
model probabilities are compared with simulations to validate the proposed mod-
els. Next, the performance of the unslotted IEEE 802.15.4 protocol is studied
using analytical and simulation results. Finally, the generality of the proposed
analyses is exploited to present the eects of the network parameters (i.e., number
of nodes and frame length) and MAC-layer parameters (i.e., macMaxFrameRe-
tries, macMaxCSMABackos, maxMaxBE, and macMinBE ) on the performance
of the unslotted IEEE 802.15.4 MAC protocol.
4.7.1 Validation of Analysis
The validation process presented in this section is two-fold. First, the numerical
results are substantiated by simulations performed using an improved version1 of
the ns-2 simulator. Then, the analytical results of the unslotted IEEE 802.15.4
protocol are compared and contrasted with those of the slotted IEEE 802.15.4
protocol obtained from the analytical models presented in Chapter 3.
Two identical non-beacon-enabled star topology networks were considered for
the validation. Both networks consisted of 10 sensor nodes, each generating
frames of length 10 backo slots based on a Poisson arrival rate of λ frames per
frame duration. Out of these two networks, one operated without ACKs, while
the other deployed ACKs and frame retransmission. The MAC-layer parameters
of both networks were assumed to have their default values as specied in the
standard [61]. For all simulations, the IEEE 2.4-GHz PHY layer and a two-ray
ground propagation model were used. A simulation trial ran until each node
completes 20, 000 frame transmissions. Each simulation data point was averaged
over 10 simulation trials using dierent random seeds. Approximations used for
the analytical models were not considered for simulations.
1Since the current ns-2 simulator (i.e., ns-2.34) detects the channel idleness incorrectly innon-beacon-enabled networks, a modication to the existing CCA implementation is proposedas shown in Appendix C.1.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 100
(a) pci and q (b) pncca
Figure 4.7: Behaviour of basic model probabilities (N = 10 and L = 10 backos).
The behaviour of the basic model probabilities pci , pncca, and q against the frame
arrival rate λ is depicted in Figure 4.7 for both network scenarios (i.e., with and
without ACKs). Numerical results of pci and pncca closely match with simulations
for the entire range of λ; however, the analytical results of q marginally deviate
from simulations at higher frame arrivals. This discrepancy can be attributed
to various model assumptions and approximations listed in Section 4.3.1. With
increasing λ in both networks, the steady state probability of channel idleness
pci decreases, while the steady state probability of starting a CCA pncca escalates
rapidly as shown in Figure 4.7. This is because more frame arrivals lead to
more transmission attempts, which in turn increase the channel occupancy. For
a given frame arrival rate, the probability pci of the network with ACKs is less
than that of the network without ACKs due to the extra channel occupancy
caused by ACKs and retransmitted frames. In contrast, the probability pncca of
the network with ACKs is higher than that of the network without ACKs as
the retransmission mechanism generates additional frame transmission attempts.
The probability q (i.e., the probability of receiving the corresponding ACK after
a data transmission) decreases with increasing λ, since more frame arrivals cause
more collisions in transmissions. The decreasing q implies more retransmissions,
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 101
and hence more frames may be discarded due to retransmission failures at higher
frame arrival rates.
Performance of both networks, in terms of the aggregate network through-
put, power consumption per node, frame discard ratio, and frame delivery ratio,
are compared and contrasted in Figures 4.8 and 4.9. Close agreement between
simulation and analytical results demonstrates the validity of the proposed anal-
ysis. Results obtained from the analyses in [126] (for the slotted IEEE 802.15.4
protocol without ACKs) and Chapter 3 (for the slotted IEEE 802.15.4 protocol
with ACKs) are also included in Figures 4.8 and 4.9 for comparison. Hence, four
equivalent IEEE 802.15.4 networks (in which N = 10 and L = 10) with dierent
MAC mechanisms:
• unslotted protocol without ACKs,
• unslotted protocol with ACKs,
• slotted protocol (BO = SO = 3) without ACKs and
• slotted protocol (BO = SO = 3) with ACKs
are considered for the following discussion.
The aggregate network throughput S of all four networks remain almost the
same at low frame arrival rates; however, with increasing λ they dier signicantly
from each other as shown in Figure 4.8(a). This can be explained as follows. The
networks with ACKs exhibit less throughput than their counterparts without
ACKs due to the extra channel occupancy caused by ACK transmissions and
frame retransmissions. In the networks without ACKs, the unslotted protocol
yields marginally better throughput than the slotted protocol at high frame ar-
rival rates. As indicated in [126], this may be caused by the additional CCA (i.e.,
the second CCA) duration of the slotted protocol, which creates an unnecessary
extra waiting time before each transmission in networks without ACKs.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 102
(a) S (b) Yav
Figure 4.8: Performance of IEEE 802.15.4 based networks (N = 10 and L = 10):(a) Aggregate network throughput S (b) Average power consumption per nodeYav.
In networks with ACKs, the slotted protocol shows signicantly better through-
put than the unslotted protocol at high λ values. This can be attributed to the
less number of frame collisions experienced by the slotted protocol compared with
that by the unslotted protocol (Figure 4.101). In the network with the slotted
protocol a data frame collides only with another data frame. In contrast, a data
frame may collide either with another data frame (i.e., collision at the rst colli-
sion window) or with an ACK frame (i.e., collision at the second collision window)
in the network with the unslotted protocol. In such networks, the collisions at
the second collision window increase the number of transmission attempts, which
in turn increase the number of collisions during the rst collision window as il-
lustrated in Figure 4.10. These additional collisions lead to more retransmissions
and ACK transmissions resulting a signicant drop in throughput performance.
The average power consumption of a node Yav of all four networks increases
with the frame arrival rate as shown in Figure 4.8(b). The networks with ACKs
1Figure 4.10 depicts the number of frame collisions experienced by a node per second inACK-enable networks with slotted and unslotted protocols. Results are based on ns-2 simula-tions.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 103
(a) ρ
(b) η
Figure 4.9: Performance of IEEE 802.15.4 based networks (N = 10 and L = 10):(a) Frame discard ratio ρ (b) Frame delivery ratio η.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 104
Figure 4.10: Number of collisions in slotted and unslotted protocols.
show a higher power consumption (in both slotted and unslotted scenarios) than
the networks without ACKs due to the excess power used for frame retransmission
and ACK transmission. The networks with slotted protocol (in both with ACKs
and without ACKs) consume extra amount of power on the beacon reception and
additional CCA procedure, and hence they exhibit a higher power consumption
than the networks with the unslotted protocol.
In all four networks, the frame discard ratio ρ follows a similar trend. While
ρ is insignicant at low frame arrival rates, it increases rapidly with λ as shown
in Figure 4.9(a)1. The frame discard ratio and its composition is almost equal
for both slotted and unslotted protocols when ACK frames are absent. However,
in the presence of ACK frames, more data frames are discarded by the unslot-
ted protocol due to its increased level of frame collisions shown in Figure 4.10.
Furthermore, ρ of the networks with ACKs is dominated by the channel access
failures, in contrast to the networks without ACKs where a signicant number of
frames are dropped due to collisions.
In networks with ACKs, the frame retransmission mechanism gives near ideal1In networks without ACKs, ρ of the slotted protocol (Figure 4.9(a)), and η of both the
slotted and unslotted protocols (Figure 4.9(b)) were obtained only from simulations as theseperformance metrics have not been derived in the respective analytical models.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 105
performance in frame delivery for both slotted and unslotted protocols. How-
ever, as shown in Figure 4.9(b), η of the network with the unslotted protocol is
marginally less than that of the network with the slotted protocol. It appears
that the extra amount of frame collisions in the unslotted protocol leads to an
additional amount of frames to be discarded due to transmission failures. On
the other hand, the networks without ACKs, which have approximately similar ρ
values with their counterparts with ACKs, perform poorly in frame delivery due
to the absence of a frame retransmission mechanism. Note that dierent y-scales
are used for the dierent graphs in Figure 4.9(b) for clarity.
4.7.2 Impact of Network and MAC-layer Parameters
This section presents the impact of network and MAC-layer parameters on the
performance of the unslotted IEEE 802.15.4 protocol. To this end, the same
system model described in Section 4.3 is used with dierent values of network
and MAC-layer parameters. Figure 4.11 and 4.12 illustrate the eects of the
frame length (for three dierent lengths: 4, 8, and 12 backo slots) and number
of nodes (for three dierent N values: 5, 10, and 20) on the performance of non-
beacon-enabled IEEE 802.15.4 networks in the absence of ACK transmission.
The eects of the MAC-layer parameters:
• macMaxFrameRetries (for three dierent values: 1,3 and 5),
• macMaxCSMABackos (for three dierent values: 1,3 and 5) and
• the length of the backo window (for three dierent combinations ofmacMinBE
and macMaxBE values : [2,3],[3,5] and [4,8])
on the protocol's performance are depicted in Figures 4.13, 4.14 and 4.15, re-
spectively. For this investigation, a non-beacon-enabled IEEE 802.15.4 network
(where N = 10 and L = 10 backos) is considered with ACK transmission. The
results are given for ve dierent values of λ to represent the entire range of
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 106
(a) S for dierent L. (b) Yav for dierent L.
(c) ρ for dierent L.
Figure 4.11: Eects of frame length L on the performance of non-beacon-enabledIEEE 802.15.4 networks without ACKs (N = 10).
frame arrival rates. According to the results in Figures 4.11 - 4.15, it appears
that the impact of network and MAC-layer parameters on the performance of the
unslotted protocol shows a similar trend to that of the slotted protocol presented
in Chapter 3. Therefore, the same discussion on the results in Sections 3.7.2 and
3.7.3 (in Chapter 3) is valid with the results presented in this section.
Research outcomes of this chapter have been either published or under review
in [173][174].
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 107
(a) S for dierent N .
(b) Yav for dierent N . (c) ρ for dierent N .
Figure 4.12: Eects of number of nodes N on the performance of non-beacon-enabled IEEE 802.15.4 networks without ACKs (L = 10 backo slots).
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 108
(a) S. (b) Yav.
(c) ρ. (d) η.
Figure 4.13: Eects of macMaxFrameRetries on the performance of non-beacon-enabled IEEE 802.15.4 networks with ACKs.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 109
(a) S.
(b) Yav.
(c) ρ.
Figure 4.14: Eects ofmacMaxCSMABackos on the perfor-mance of non-beacon-enabled IEEE802.15.4 networks with ACKs.
(a) S.
(b) Yav.
(c) ρ.
Figure 4.15: Eects of the backowindow length on the performance ofnon-beacon-enabled IEEE 802.15.4 net-works with ACKs.
4. ANALYSIS OF NON-BEACON-ENABLED IEEE 802.15.4 110
4.8 Conclusion
A Markov chain based analysis is presented to model the unslotted CSMA/CA
protocol in non-beacon enabled IEEE 802.15.4 WSNs. In the absence of ACKs,
the behaviour of the unslotted IEEE 802.15.4 protocol is modelled by approxi-
mating the protocol's continuous time evolution to a discrete time evolution with
a proper time unit. This study is then extended to analyse the network with
ACKs, taking the characteristics of ACK transmission and frame retransmissions
into account. The proposed analytical models can be used to derive the per-
formance metrics of the protocol including the aggregate network throughput,
average power consumption per node and frame discard ratio. Numerical results
obtained from the proposed models are validated using ns-2 simulations. Then,
these results are used to compare and contrast the performance of the unslotted
protocol with its slotted counterpart. In general, the unslotted protocol exhibits
better performance than the slotted protocol in networks without ACKs; however,
when ACK frames are utilised, the performance of the unslotted protocol becomes
signicantly worse than the slotted version mainly due to collisions between data
frames and ACK frames in the protocol.
Chapter 5
Throughput Analysis of IEEE
802.15.4 MAC Protocol in the
Presence of Hidden Nodes
5.1 Introduction
The performance of the IEEE 802.15.4 MAC protocol has been analysed in the
literature using various mathematical models by considering carrier sense multiple
access with collision avoidance (CSMA/CA) as the basic MAC mechanism. Most
of the existing analyses [86][87][124][126] have been performed for single-hop star-
topology networks assuming that all sensor nodes lie within the carrier sensing
range of each other. However, this assumption is not always valid for practical
WSN deployments due to the limited transmission range of the sensor nodes and
the presence of physical obstacles. This gives rise to the hidden node problem,
which is a common issue in many wireless networks that follow the CSMA/CA
scheme [175].
The hidden node problem in a single-hop, star-topology network is illustrated
in Figure 5.1. In this topology, nodes X and Y are in the communication range
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 112
Figure 5.1: Hidden node problem in a single-hop star-topology network.
of a common receiver R. Furthermore, X lies beyond the carrier sensing range of
Y and vice versa. When X starts a transmission it may collide with an ongoing
transmission from Y to R. Hence, X is considered as a hidden node to Y and
vice versa. The presence of hidden nodes degrades the performance of wireless
networks due to an excessive amount of collided transmissions. Busy tone mecha-
nisms, ready-to-send/clear-to-send (RTS/CTS) mechanisms, carrier sense tuning
and node grouping have been proposed to overcome the hidden node problem in
wireless networks [45][175][176]. However, the IEEE 802.15.4 standard neither
supports any of these techniques nor provides any other mechanism to prevent
collisions due to hidden nodes.
Although the hidden node problem is a well known phenomenon in wireless
networks, only a few studies have been undertaken to evaluate its impact on
the performance of IEEE 802.15.4 based networks. Most of the previous studies
([177], references there in) have focused on hidden node mitigation techniques
instead. Of those studies that analyse the eect of hidden nodes, all most all are
based on simulations [178]-[180]. Nevertheless, Goyal et al. [171] have recently
proposed a stochastic model to analyse the non-beacon-enabled mode of the IEEE
802.15.4 MAC protocol considering the impact of hidden node collisions on the
packet loss probability and the packet transmission latency. This model divides
the nodes in a given network into two distinct categories: regular nodes and
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 113
hidden nodes; and it can be used to evaluate the performance of regular nodes
but not the entire network. Taking a dierent approach based on Markov chains,
Marco et al. [168] have evaluated the data transmission reliability of the unslotted
IEEE 802.15.4 MAC protocol with hidden nodes. Thus far, the models presented
in [171] and [168] are the only mathematical analyses proposed for the IEEE
802.15.4 MAC protocol in the presence of hidden nodes; however, neither of these
models has considered the throughput performance of the protocol.
In contrast, the throughput performance of the IEEE 802.11 protocol [45] (i.e.,
the counterpart of the IEEE 802.15.4 protocol used in wireless local area networks
(WLANs)) has been studied in the literature considering the presence of hidden
nodes [181]-[187]. Wu et al. [181] have proposed a model for the distributed
coordination function (DCF) of the IEEE 802.11 protocol with hidden nodes
to analyse the throughput under the saturation condition. Similar saturation-
throughput analyses of the IEEE 802.11 protocol with hidden nodes can be found
in [182] and [183]. On the contrary, Yang et al. [184] proposed a non-saturated
goodput, a derivative of throughput, analysis for a single cell WLAN system. In
[186], Ekici and Yongacoglu have numerically evaluated the eect of hidden nodes
on throughput performance of symmetric networks where each node sees the same
number of hidden nodes and contending nodes. Moreover, Ray et al. [187] have
presented a queuing theoretic analysis for a network with linear topology and
derived an exact expression for the maximum throughput while considering the
presence of hidden nodes. However, none of aforementioned analyses can be
used to evaluate IEEE 802.15.4 based networks due to the dierences of the
two protocols in many aspects including backo mechanisms and carrier sensing
[45][61].
This chapter analyses the throughput performance of IEEE 802.15.4 based
star-topology networks in the presence of hidden nodes. The proposed analysis
models the wireless channel around the common receiver using a discrete-time
Markov chain (DTMC). Even though this analysis is developed for networks with
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 114
special node arrangements, it can be used to approximate the throughput of
generic star-topology networks as shown later in the chapter. In addition, the
proposed analysis can serve as a simple mathematical tool to investigate the
eects of various network parameters including the network size, frame length
and frame arrival rate on the throughput of the network.
5.2 System Model
Consider an IEEE 802.15.4 based star-topology network with N nodes scattered
around a common base station. The nodes directly transmit monitoring data
to the base station, which operates as a passive receiver enabling only uplink
data transmission in the network. Star-topology networks with only uplink data
transmission have been deployed in many current WSN applications including
cultural heritage monitoring [188], smart homes [189] and industrial automation
[190]. The network operates in the beacon-enabled mode of the IEEE 802.15.4
MAC protocol to achieve network-wide synchronisation.
The proposed analytical model is based on several assumptions on the network
of interest. First, it is assumed that the entire beacon interval is active and lled
with the CAP. The transmission of each data frame lasts for xed-L backo
slots, and data frames are assumed to arrive at the nodes according to a Poisson
distribution with an arrival rate of λ frames per frame duration. Accordingly,
the frame arrival probability per backo slot can be derived as p = λ/L. Next,
ideal channel conditions where there are no transmission errors introduced by the
channel are assumed; therefore, frames are dropped only due to their collisions.
Buering at the nodes is not considered, and the capture eect is neglected for
frame transmissions, i.e., all collided frames are assumed to be dropped regardless
of their received signal strengths at the base station. Moreover, node grouping
is assumed; thus, the entire network can be divided into several non-overlapping
groups as specied next.
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 115
Figure 5.2: Example network [4,6,8] with node grouping (K = 3).
5.2.1 Node Grouping
Node grouping divides an entire network into K groups such that∑K
j=1 nj = N
where nj denotes the number of nodes in Group j. Such a network is denoted by
the notation [n1, n2, . . . , nK ], and it has the following two characteristics:
• Nodes within a group share the same carrier sensing range. i.e., all sensor
nodes within a group can hear each others' transmissions,
• Nodes in dierent groups have dierent carrier sensing ranges. i.e., nodes
in dierent groups cannot hear each others' transmissions.
Accordingly, the data transmissions originating at dierent groups may collide
at the base station creating the hidden node problem in the network. Figure 5.2
illustrates the concept of node grouping in which a network with N = 18 nodes
is divided into K = 3 groups, where n1 = 4, n2 = 6 and n3 = 8. This network is
represented by the notation [4, 6, 8].
The grouping of sensor nodes is a key technique used to mitigate the hidden
node problem [176]. In [191], a node grouping network set-up has been deployed
in an experimental test bed to emulate a WSN with hidden nodes. Therefore, the
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 116
node grouping is not an unrealistic scenario in WSNs, specially in the presence
of hidden nodes. Thus, it is considered as a key assumption of the proposed
network analysis. However, later in this chapter it has been shown that the
proposed analysis can still be used to approximate the throughput performance
of networks that may not necessarily satisfy the node grouping condition.
5.3 Network Analysis
This section analyses the network of interest to determine its throughput per-
formance. Since all data transmissions are bound towards the base station, the
throughput of the network can be determined by analysing the channel seen by
the base station, i.e., the overlapping area of all carrier sensing ranges around the
base station as depicted in Figure 5.2). This channel is referred to as the common
channel throughout this chapter.
To model the common channel, two basic probabilities:
1. The probability of none of the nodes in the Group j begin transmission
(αj),
2. The probability of exactly one node in the Group j begins transmission
(βj).
are dened. These two probabilities are determined by analysing individual
groups as described in the next section.
5.3.1 Analysis of Individual Groups
The node grouping splits the single-hop star-topology network intoK non-overlapping
groups where each group can be considered as a mini star-topology network.
Thus, any existing analysis that derives the probabilities αj and βj for an IEEE
802.15.4 based star-topology network can be devised to analyse the individual
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 117
groups. For this study, the analysis proposed by Ramachandran et al. [126] is
chosen to model the individual groups by considering its analytical simplicity and
accuracy.
Ramachandran et al. [126] have modelled the behaviour of a generic node and
the channel of a single-hop star-topology network using two dierent discrete-
time Markov chains (DTMCs). They have approximated the probability that the
channel is sensed idle in a given backo slot by the steady-state probability of
channel idleness pci . Similarly, the probability that a node begins transmission
in a given backo slot is approximated by the steady-state probability that a
node transmits pnt . These two probabilities have been computed by solving a
consistent system of equations including the steady state equations of the two
DTMCs. Based on these two probabilities, the probability that any node begins
transmission given that the channel has been idle for two consecutive backo slots
pnt|ii is derived as [126]
pnt|ii =Lpnt
Lpci − 1 + pci, (5.1)
where L is the data frame length.
In the context of the j-th individual group, let pnt|ii(j) represent the probability
that any node in Group j begins transmission given that the channel seen by
Group j has been idle for two consecutive backo slots. Based on pnt|ii(j), the
basic probabilities αj and βj can be derived as
αj = [1− pnt|ii(j)]nj
βj = njpnt|ii(j)[1− jpnt|ii(j)]nj−1,
(5.2)
where nj is the number of nodes in Group j. The rst half of Table 5.1 lists
αj and βj values obtained for the groups shown in Figure 5.2 considering three
dierent frame arrival rates λ: 0.006, 0.06 and 0.6.
It is worthwhile to mention that the analysis of the common channel, which
will be presented next, is valid not only with the analysis in [126] but also with
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 118
Table 5.1: Probabilities αj, βj, A, B, C and E for the network shown in Figure 5.2when L = 10.
Group1 Group2 Group3n1 = 4 n2 = 6 n3 = 8 A B C E
λ α1 α2 α3
β1 β2 β3
0.006 0.99756 0.99630 0.99499 0.98890 0.01105 0.99288 0.007100.00244 0.00370 0.00499
0.06 0.97231 0.95298 0.92943 0.86121 0.12922 0.91126 0.084890.02740 0.04608 0.06833
0.6 0.81757 0.72560 0.64279 0.38132 0.37767 0.53951 0.339510.16889 0.23908 0.29207
any of the analyses that derive αj and βj for an IEEE 802.15.4 based star-topology
network.
5.3.2 Analysis of the Common Channel
This section analyses the common channel seen by the base station. Depending
on the number of current and recently ended data transmissions, the common
channel can operate in three dierent modes: idle, success and failure. It dwells
in the idle mode when there is no data transmission from any of the groups. If
there is only one data transmission and it has not collided with any of the recent
data transmissions, the common channel is in the success mode. Conversely,
it operates in the failure mode if there are more than one simultaneous data
transmissions. Even though there is only one data transmission in the common
channel for a given instant, it can be in the failure mode if the current transmission
has collided with recent data transmissions.
Possible modes of the common channel and their transitions can be mod-
elled using a discrete-time Markov chain as shown in Figure 5.3. The IDLE state
represents the idle mode, while SUCCx and FAILx states (where 1 ≤ x ≤ L) char-
acterise the success and failure modes, respectively. The subscript x in SUCCx
indicates that the common channel should remain in the success mode for the
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 119
Figure 5.3: DTMC model for the common channel.
next consecutive x backo slots (including the current backo slot) to complete
the current data transmission successfully. Similarly, the subscript in FAILx in-
dicates that the common channel will remain in the failure mode at least for the
next consecutive x backo slots including the current backo slot. Each state in
the DTMC corresponds to a single backo slot, and the transitions among these
states occur at the boundary of backo slots.
Transition probabilities of the DTMC for the common channel can be derived
as follows. Let the common channel be in the IDLE state. If none of the groups
begin transmission, the channel remains in the IDLE state with probability A,
which is given by
A =K∏j=1
αj. (5.3)
The common channel moves from the IDLE state to SUCCL state if only one
node from the entire network starts data transmission, which happens with the
probability B given by
B =K∑i=1
βi
K∏j=1j 6=i
αj . (5.4)
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 120
Figure 5.4: Transition from SUCCL to SUCCL−1 state.
Conversely, if more than one node begin transmissions simultaneously, the tran-
sition from the IDLE state to FAILL state happens with probability 1− (A+B).
Next, consider the transition from SUCCL state to SUCCL−1 state. For this
transition to take place, no other transmission, besides the one already going on,
should start. Suppose the ongoing transmission belongs to a node from Group i.
Then, no other node from Group i would begin a transmission due to the basic
CSMA operation. However, nodes from other groups may begin transmissions as
they cannot sense the ongoing transmission. Thus, if the current transmission
belongs to Group i, the common channel moves to the SUCCL−1 state if and
only if none of the nodes in other groups begin a transmission, which happens
with the probability∏K
j=1j 6=i
αj. Since there are K groups in the network, there
exist such K transition possibilities as illustrated in Fig. 5.4. Therefore, the
SUCCL → SUCCL−1 transition can be mathematically expressed as
π(succL−1) =K∑i=1
π
(Group i is transmitting at
SUCCLstate
) K∏j=1j 6=i
αj (5.5)
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 121
where π(state) represents the steady state probability of STATE. This can be
simplied to
π(succL−1) =K∑i=1
ψiπ(succL)K∏j=1j 6=i
αj , (5.6)
where ψi is the conditional probability that the current transmission belongs to
Group i given that the common channel is in SUCCL state. Thus, the transition
probability from SUCCL state to SUCCL−1 state can be given as
C =K∑i=1
ψi
K∏j=1j 6=i
αj . (5.7)
Since the derivation of ψi using the basic probabilities αi and βi is mathematically
intractable, ψi is approximated by the probability that the current transmission
belongs to Group i given that at least one group has successfully begun a trans-
mission, which is equal to βi/∑K
r=1 βr. Therefore, the approximated transition
probability C can be expressed as
C ≈K∑i=1
(βi∑Kr=1 βr
)K∏j=1j 6=i
αj . (5.8)
With probability C, the common channel moves through the other SUCC
states until it reaches SUCC1 state, where it completes a successful transmission.
After SUCC1 state, the common channel may move to one of the three dierent
states : IDLE, SUCCL or FAILL; depending on the number of groups that begins
data transmissions at the end of the current backo slot. According to the IEEE
802.15.4 standard, the nodes in the group that has just completed a successful
data transmission can not start another transmission immediately. They have to
wait at least another two consecutive backo slots. Therefore, when the common
channel sees the end of a successful data transmission, only (K − 1) groups are
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 122
eligible to begin a new data transmission at the next backo slot. Thus, the com-
mon channel moves from SUCC1 state to IDLE state with probability D, where
D represents the probability of none of the other groups begin data transmission
given that there was a data transmission at the previous backo slot. Hence,
D = C. On the other hand, if only one node from the entire network begin
transmission, the common channel moves from SUCC1 state to SUCCL state.
This happens with probability E, which represents the probability of exactly one
node begins transmission given that there was a data transmission at the previous
backo slot. Following similar arguments to that of deriving C, the probability
E can be approximated as
E ≈K∑i=1
(βi∑Kr=1 βr
)K∑j=1j 6=i
βj
K∏l=1
l 6=i, l 6=j
αl. (5.9)
If more than one node in the entire network begin transmission after SUCC1
state, the common channel moves to FAILL state with probability 1− (C + E).
Unlike in the success mode where only one group is transmitting, the number
of transmitting groups in the failure mode may vary from one to K. However, for
simplicity, it is assumed that the channel experiences a single collision at a time.
This simplication represents the worst case scenario as it allows more groups
to cause future collisions, and hence guarantees that the common channel does
not leave the failure mode prematurely. Because of this simplication, the same
transition probabilities used with SUCC states are applicable to the FAIL states.
The values of the transition probabilities A, B, C and E for the network shown
in Figure 5.2 are listed in Table 5.1 considering three dierent frame arrival rates.
In terms of the transition probabilities, the steady state equations and the
normalisation condition for the DTMC shown in Figure 5.3 can be derived as
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 123
π(idle) = Aπ(idle) + C[π(succ1) + π(fail1)] (5.10)
π(succL) = Bπ(idle) + E[π(succ1) + π(fail1)] (5.11)
π(succx) = CL−xπ(succL); 1 ≤ x < L (5.12)
π(failL) = [1− (A+B)]π(idle) + (1− C)L∑x=2
[π(succx) + π(failx)]
+(1− [C + E])[π(succ1) + π(fail1)] (5.13)
π(failx) = CL−xπ(failL); 1 ≤ x < L (5.14)
π(idle) +L∑x=1
π(succx) +L∑x=1
π(failx) = 1, (5.15)
where π(state) represents the steady state probability of STATE. Equations above
can be rearranged to obtain
π(idle) =
[CL
1− A
][π(succL) + π(failL)] (5.16)
π(succL) =
[BCL + (1− A)ECL−1] π(failL)
1− A−BCL − (1− A)ECL−1 (5.17)
π(idle) +
[1− CL
1− C
][π(succL) + π(failL)] = 1. (5.18)
For a given network, the above set of equations can be numerically solved by
using related A, B, C, and E values1 to determine the steady state probabilities
of the common channel DTMC. These steady state probabilities will be used in
Section 5.5 to compute the aggregate network throughput.1These values can be computed using Equations (5.3), (5.4), (5.8) and (5.9) along with
corresponding αj and βj values obtained from the analysis in [126].
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 124
5.4 Simplied Analysis for Networks with Uni-
form Groups
Networks with uniform groups (i.e., networks with groups consisting of an equal
number of nodes) is a special case of the node grouping. The analysis presented
in Section 5.3 can be simplied for this case by substituting nj = n to yield
αj = α and βj = β ∀j where j = 1, 2, ..., K. Consequently, the probabilities A,
B, C and E are reduced to αK , KβαK−1, αK−1, and (K − 1)βαK−2 according to
Equations (5.3), (5.4), (5.8) and (5.9). Therefore, Equations (5.16) (5.18) can
be simplied to
π(idle) =
[αL(K−1)
1− αK
][π(succL) + π(failL)] (5.19)
π(succL) =
[(αK +K − 1)βαL(K−1)
]π(failL)
α(1− αK)− [αK +K − 1]βαL(K−1)(5.20)
π(idle) +
[1− αL(K−1)
1− αK−1
][π(succL) + π(failL)] = 1 (5.21)
in networks with uniform groups. Given the corresponding α and β values, Equa-
tions (5.19) (5.21) can be numerically solved to obtain the steady state proba-
bilities of the common channel DTMC. The computed steady state probabilities
will be used to derive the aggregate network throughput.
5.5 Throughput Analysis
The aggregate network throughput S is dened as the fraction of time the com-
mon channel spends in successful data transmission; thus, it represents a dimen-
sionless normalised value. Successful data transmission occurs if and only if the
common channel dwells in SUCC1 state. Before arriving at SUCC1 state, the
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 125
common channel has to progress through all other SUCC states. Therefore, the
normalised aggregate network throughput can be obtained by adding the fractions
of time that the common channel dwells in each SUCC state for successful data
transmission. The fraction of time the common channel spends in each SUCC
state for successful data transmission is the same as that in SUCC1 state. Thus,
S = L
[π(succ1)
π(idle) +∑L
x=1 π(succx) +∑L
x=1 π(failx)
], (5.22)
where L represents the number of SUCC states (note: According to Figure 5.3,
the number of SUCC states is equal to the frame length in backo slots). Due to
the normalisation condition of the DTMC model shown in (5.15) the denominator
of (5.22) becomes one. Consequently, S can be simplied to
S = Lπ(succ1). (5.23)
In other words, the normalised aggregate network throughput is entirely deter-
mined by the frame length and the steady state probability of the SUCC1 state.
Analytical results presented in the next section are based on this nding.
5.6 Results and Discussion
In this section, the proposed analytical model is veried using Monte-Carlo sim-
ulations. Simulations are performed using ns-2 [105] based on the assumptions
in Section 5.2. For all simulations, the 2.4-GHz physical layer and the beacon-
enabled (beacon order BO = 6 ) mode of the IEEE 802.15.4 standard are consid-
ered. IEEE 802.15.4 PHY and MAC layer parameters are assumed to have their
default values as specied in the standard [61]. In addition, the transmission and
carrier sensing ranges of the nodes are tuned to be identical. Unless mentioned
otherwise, it is assumed that data frames arrive at the same rate at all nodes,
and the transmission of each data frame lasts for 10 backo slots. Furthermore,
the nodes of the star-topology networks are deployed carefully in the simulation
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 126
Figure 5.5: Normalised aggregate network throughput S of dierent networkswith hidden nodes.
set-up to maintain the properties of node grouping (i.e., all nodes in a group can
hear each other while the nodes in dierent groups are hidden from each other).
A simulation trial runs until each node completes 20, 000 frame transmissions.
Simulation results are averaged over 10 simulation trials using dierent random
seeds for each trial.
5.6.1 Validation of Analytical Results
To validate the proposed analysis, four network scenarios with dierent number
of groups K and dierent number of nodes per group nj (i.e., [8,8] network,
[12,4] network, [6,4,2] network, and [10,8,4,2] network)1 are used. Analytical and
simulated aggregate network throughput S of the four networks, obtained against
dierent frame arrival rates λ, are compared in Figure 5.5. Close agreement
of analytical and simulation results validates the proposed analysis for dierent
networks with varying group and node numbers. This in turn suggests the validity
of the approximations made to derive the transition probabilities C and E.1The notation [n1, n2, ..., nk] indicates that the network has K groups and the group j has
nj nodes.
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 127
According to Figure 5.5, the aggregate network throughput S of all four net-
works increases monotonically at low-λ values where collisions due to hidden
nodes are unlikely. When collisions due to hidden nodes increase (i.e., in high-
λ region) the throughput decreases with increasing λ. Since the networks with
larger number of nodes (N =∑K
i=1 ni ) generate more trac, they exhibit higher
throughput and reach the corresponding peak value before other networks in the
low-λ region. On the other hand, they experience a large number of hidden node
collisions and consequently show poor throughput performance in the high-λ re-
gion. Apparently, networks with the same number of nodes N and the same
number of groups K (e.g., [8,8] network and [12,4] network) behave dierently
depending on the distribution of nodes among groups. The [12,4] network, in
which a higher number of nodes shares the same carrier sensing range compared
with the [8,8] network, experiences less hidden node collisions. This explains the
higher throughput achieved by the [12,4] network compared with the [8,8] net-
work for the entire range of λ. It appears that the distribution of nodes among
groups within a network has a signicant impact on the throughput performance
of the network. The impact of dierent network parameters on the aggregate
network throughput of a given network is investigated next.
5.6.2 Impact of Dierent Network Parameters on Through-
put
This section investigates the impact of network parameters including the number
of groups K, number of nodes per group n, and frame length L on the aggregate
network throughput S considering networks with uniform groups (i.e., networks
with equal number of nodes in each group). For comparison, it also presents S
of corresponding networks with no hidden nodes obtained using the analysis in
[126].
The eects of the number of groups K on the aggregate network throughput S
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 128
(a) S for dierent number of groups Kwhen n = 4 and L = 10
(b) S for dierent number of nodes n per groupwhen K = 3 and L = 10
(c) S for dierent network setupswhen K × n = 12 and L = 10
(d) S for dierent data frame lengths Lwhen K = 3 and n = 4
Figure 5.6: Normalised aggregate network throughput S for varying networkparameters.
is illustrated in Figure 5.6(a). Since additional groups generate additional trac,
S of a network with more groups is expected to be higher than that of a network
with less groups at low-λ values. On the other hand, adding more groups would
increase the number of hidden nodes in the network causing a large number of
hidden node collisions at higher λ values. This explains the low aggregate network
throughput experienced by networks with more groups in this region.
The aggregate network throughput of networks having dierent number of
nodes per group n is shown in Figure 5.6(b). In this case, the network trac
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 129
increases with n as K is xed for all networks considered. Therefore, networks
with higher number of nodes per group have better throughput in low-λ region.
However, when λ increases those network show poor throughput performance
than their counterparts with less number of nodes per group. This is because
increasing the number of nodes in each group will potentially escalate the number
of hidden node collisions at high frame arrival rates.
The network congurations considered above have dierent total number of
nodes N , and hence they generate dierent amounts of trac. Dierence in total
network trac may mask the eects of hidden nodes on S up to some extent.
Therefore, to uncover the actual impact of hidden nodes, S of dierent network
setups that generate the same amount of trac were obtained, and the results
are presented in Figure 5.6(c). As expected, networks with more hidden nodes
exhibit same or less throughput than that of networks with a few hidden nodes
for the entire range of λ. Moreover, Figures 5.6(a) and 5.6(c) clearly illustrate
the impact of the presence of hidden nodes on S by comparing the throughput of
similar networks with and without hidden nodes. As per these gures, networks
with no hidden nodes exhibit a normailsed throughput greater than 0.5 when
they approaches to the saturation (i.e., λ = 1). In contrast, the throughput
of equivalent networks with hidden nodes tend towards zero near the saturation
region. This signicant throughput reduction indicates the excessive amount of
hidden node collisions experienced by networks with hidden nodes in this high-λ
region.
The eects of the data frame length L on the aggregate network throughput
is illustrated in Figure 5.6(d). Since the dierences in frame length aect the
total number of frame arrivals (due to the normalisation of frame arrival rate),
S was obtained against the frame arrival probability p instead of λ. As shown in
Figure 5.6(d), long data frames provide higher network throughput at low frame
arrivals; however, they fail to prevail in high-p region due to increased number
of hidden node collisions caused by their longer duration. Therefore, a careful
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 130
Figure 5.7: Normalised aggregate network throughput S for [4,8,12] network whendierent groups generate frames at dierent rates.
adjustment of data frame length may be helpful in achieving maximum possible
aggregate network throughput at a known frame arrival rate in a given WSN with
hidden nodes.
So far in this study, it has been assumed that the frame arrival rate of all
the nodes in the network of interest is similar. However, in some WSN appli-
cations such as human health monitoring [192], frames arrive at dierent nodes
at dierent rates. The proposed analysis can be readily applied to such situ-
ations by embracing dierences in frame arrivals during the calculation of two
basic probabilities αj and βj. To evaluate the aggregate network throughput
with varying frame arrival rates, the [4,8,12] network was considered with three
dierent cases. In Case 1, data frames arrive at an equal rate at the nodes of
all three groups (λ1 = λ2 = λ3). In Case 2, the frame arrival rates of Group 1
(with 4 nodes) and Group 2 (with 8 nodes) are xed to 0.1 and 0.01 frames/frame
duration, respectively. On the other hand, in Case 3 the arrival rates of Group
1 and Group 2 are swapped with each other and are 0.01 and 0.1 frames/frame
duration. For all three cases, the frame arrival rate of Group 3 varies from 0.0002
to 0.8 frames/frame duration.
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 131
Analytical and simulation results of aggregate network throughput S obtained
for all three cases are shown in Figure 5.7. The aggregate network throughput
of cases 2 and 3 are signicantly higher compared to that of the Case 1 when
frames arrive at Group 3 at low rates (i.e., λ < 0.02). In this region, most of
the nodes in all three cases generate a fewer number of frames, and consequently
there are less collisions due to hidden nodes. Therefore, the network that has
the highest number of nodes with a higher frame arrival rate (i.e., Case 3) yields
the highest throughput in this region. On the other hand, when λ3 increases,
hidden node collisions escalate due to increased trac in Group 3. Therefore, the
network with the lowest number of nodes with a higher frame arrival rate (i.e.,
Case 2) prevails in the high-λ3 region. The outcomes of this investigation would
be useful for designing IEEE 802.15.4 based WSNs with hidden nodes, in which
dierent nodes generate data at dierent rates (e.g., WSNs deployed in smart
infrastructure monitoring [35], human health monitoring [192]).
5.6.3 Approximating Throughput of Generic Networks
Node grouping, as discussed in Section 5.2.1, may not always be valid for practical
WSN deployments. Therefore, this section investigates the applicability of the
proposed analysis for generic networks that do not necessarily satisfy the node
grouping condition. First, it is shown that the aggregate network throughput
of `networks with relaxed node grouping condition' can be approximated using
the analysis presented in Section 5.3. Then, the aggregate network throughput
of dierent congurations of a given network are shown to be approximately
equal, provided that all network congurations considered have similar average
number of hidden-nodes-per-node. Finally, based on these ndings, a technique is
proposed to approximate the aggregate network throughput of generic networks.
The node grouping condition in a given network can be relaxed by converting
a few of the nodes in each group into intermediate nodes such that they can
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 132
(a) Cong. 1 (havg = 8) (b) Cong. 2 (havg = 8.125)
(c) Cong. 3 (havg = 8.5)
Figure 5.8: [8,8] network and its relaxed node grouping congurations.
hear some of the nodes in their neighbouring groups. Figure 5.8(b) illustrates a
possible way of relaxing the node grouping condition of the [8,8] network depicted
in Figure 5.8(a)1. In Figure 5.8(a), all nodes in Group 1 share a single carrier
sensing range and so do the nodes in Group 2. Therefore, no intermediate nodes
exist in Figure 5.8(a). Now, consider the conguration in Figure 5.8(b) where
Node 1 from Group 1 and Node 2 from Group 2 perform as intermediate nodes.
As illustrated, Node 1 can hear2 Nodes 2, 4, 6 and 8 from Group 2, and Node
2 can hear Nodes 1, 3, 5 and 7 belonging to Group 1. The new carrier sensing
ranges of Node 1 and Node 2 now include only one half of the nodes (including
1Nodes in Figure 5.8(a) and 5.8(b) are numbered for the simplicity of explanation.2Symmetric hearing between nodes is assumed, i.e.,
Node A hears Node B ⇐⇒ Node B hears Node ANode A cannot hear Node B⇐⇒ Node B cannot hear Node A.
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 133
(a) Cong. 1 (havg = 8) (b) Cong. 2 (havg = 8)
(c) Cong. 3 (havg = 8)
Figure 5.9: [8,8,8] network and its relaxed node grouping congurations.
themselves) in their respective original groups. In other words, Node 1 cannot
hear Nodes 9, 11, 13 and 15 while Node 2 cannot hear Nodes 10, 12, 14 and 16 any
more. Therefore, the presence of intermediate nodes has created dierent carrier
sensing ranges within the groups and consequently divided the entire network
into several sub-groups with overlapping carrier sensing ranges. The nodes in a
given sub-group have the same carrier sensing range, and they can hear not only
the nodes in that particular sub-group but also all the nodes in adjacent sub-
groups. For clarity of illustration, the sub-groups in Figure 5.8(b) are enclosed
by dotted boxes, and each sub-group is linked to its adjacent sub-groups using
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 134
Figure 5.10: Normalised aggregate network throughput S of networks with re-laxed node grouping.
arrows. The value h represents the number of hidden nodes related to each sub-
group. For instance, the sub-group of Nodes 9, 11, 13 and 15 has nine hidden
nodes as the nodes in that sub-group can hear only Nodes 3,5 and 7 apart from
themselves. Based on h values of all sub-groups, the average number of hidden-
nodes-per-node havg of the network shown in Figure 5.8(b) can be computed as
havg = [2× (4× 9 + 3× 7) + 2× 8] /16 = 8.125.
Figure 5.8c shows another relaxed node grouping conguration of the [8,8]
network where the number of intermediate nodes equals four. Similarly, Figure 5.9
shows the [8,8,8] network and two of its relaxed node grouping congurations. The
aggregate network throughput of those six network congurations1 are compared
in Figure 5.10. In each network, the congurations with intermediate nodes
have approximately equal throughput to that of the equivalent node grouping
conguration as illustrated in Figure 5.10. This nding suggests that the proposed
analysis can approximate the aggregate network throughput even for networks
with a relaxed node grouping condition.
However, it should be noted that the average number of hidden-nodes-per-1ns-2 simulation results obtained for all congurations are presented along with the analyt-
ical results related to the node grouping congurations (i.e., Conguration 1 of each networks).
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 135
(a) Cong. 1 (havg = 8) (b) Cong. 2 (havg = 7.7) (c) Cong. 3 (havg = 5.75)
(d) Cong. 4 (havg = 6) (e) Cong. 5 (havg = 3.5) (f) Cong. 6 (havg = 3.375)
Figure 5.11: Dierent congurations of 16-node-network (N = 16) with dierentaverage number of hidden nodes havg.
node havg of each of the above relaxed node grouping congurations is equal or
approximately-equal to that of the equivalent conguration with node grouping.
Does this imply that two dierent network congurations of a given (in the sense
of N , L and λ) network have equal aggregate network throughput when both
network congurations have similar average number of hidden-nodes-per-node
havg?
To investigate this, dierent congurations of a 16-node-network and a 24-
node-network are set up with dierent havg values. Figures 5.11 and 5.12 show
these congurations, emphasising the possible sub-groups and corresponding havg
values of the 16-node-network and 24-node-network, respectively. For both net-
works, Conguration 1 satises the node grouping condition. Apart from that, all
other congurations are generic WSN deployments as there are no restrictions on
node grouping. Simulation results of the aggregate network throughput obtained
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 136
(a) Cong. 1 (havg = 16) (b) Cong. 2 (havg = 15.9) (c) Cong. 3 (havg = 14.2)
(d) Cong. 4 (havg = 13.9) (e) Cong. 5 (havg = 8) (f) Cong. 6 (havg = 7.5)
Figure 5.12: Dierent congurations of 24-node-network (N = 24) with dierentaverage number of hidden nodes havg.
(a) 16-node-network (b) 24-node-network
Figure 5.13: S of dierent network congurations with varying havg: (a) 16-node-network (N = 16) and (b) 24-node-network (N = 24).
for all congurations of the 16-node-network and the 24-node-network are shown
in Figure 5.13(a) and 5.13(b), respectively. As expected, the congurations with
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 137
(a) Generic 25-node-network(havg ≈ 15)
(b) Throughput comparison
Figure 5.14: Validation of proposed technique.
similar havg values exhibit almost equal aggregate network throughput in each
network considered.
This result can be exploited to analyse the aggregate network throughput
of a given generic network. First, it is required to nd an `equivalent network
with node grouping' that has the same number of total nodes N and the same
or approximately the same average number of hidden nodes per node havg with
the generic network. Then, S of the generic network can be approximated by
analysing the `equivalent network with node grouping'. This technique can be
explained using the following example. Assume a network with 25 nodes where
havg ≈ 15 as shown in Figure 5.14(a). By applying the analytical model pre-
sented in Section 5.3 to the [12,10,3] network with node grouping, which ful-
ls N = 25 and havg ≈ 15 ([12× 13 + 10× 15 + 3× 22] /25), the aggregate
network throughput of the given network can be found approximately. The
[14,5,5,1] network (with four non-overlapping groups) is another possible `equiva-
lent node-grouping network' to the given generic network. The simulation results
obtained for the network shown in Figure 5.14(a) and the analytical results for
the equivalent networks with node grouping (i.e., the [12,10,3] network and the
5. THROUGHPUT ANALYSIS WITH HIDDEN NODES 138
[14,5,5,1] network) are compared in Figure 5.14(b). The close agreement between
the analytical results and the simulation results shows the applicability of the
proposed analytical model to evaluate the throughput performance of a generic
star-topology network, which does not meet the node grouping condition.
Outcomes of this chapter have been either published or under review in [193]
and [194].
5.7 Conclusion
An analytical model is proposed to evaluate the performance of the IEEE 802.15.4
MAC protocol in the presence of hidden nodes by grouping the nodes into non-
overlapping carrier sensing ranges. The proposed analysis can be used to derive
the aggregate network throughput. The close agreement between the analytical
results and simulations validate the accuracy of the analysis. The results reveal
that adding more hidden nodes, either by increasing the number of groups or
by increasing the number of nodes in a group, would signicantly decrease the
aggregate network throughput at high frame arrival rates where frames collide
frequently. Furthermore, for a given frame arrival rate, an increased aggregate
network throughput can be achieved by adjusting the frame length appropriately.
Using ns-2 simulations, it is shown that the throughput performance of dierent
congurations that have equal number of average hidden-nodes-per-node of a
given network are approximately equal. Based on these results, a simple technique
is proposed to approximate the throughput of generic star-topology networks with
hidden nodes.
Chapter 6
IEEE 802.15.4 based MAC Protocol
for Hybrid Monitoring WSNs
6.1 Introduction
Remote monitoring and data collection is arguably the main application of the
WSN technology. This wide-ranging application can be broadly classied into
three main categories: realtime monitoring, periodic monitoring, and event de-
tection (ED) [7]. Among them the realtime monitoring is rarely supported by
energy constrained WSNs as it demands a greater amount of energy. Therefore,
most of the existing WSNs are deployed either in periodic monitoring or in event
detection applications. In periodic monitoring, each sensor node monitors a cer-
tain phenomenon of interest according to a predened schedule and transmits
the sensed data periodically towards a sink-node over a long period of time. This
monitoring scenario enables end-users to examine the long-term evolution and
variability of certain monitoring parameters; therefore, it can be duly named as
long-term periodic monitoring (LTPM). On the other hand, in the ED scenario,
each sensor node monitors a certain phenomenon of interest continuously to de-
tect a pre-specied abnormal or rare events. Once an event is detected, sensor
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 140
nodes alert end users about the precarious situation by transmitting the data
gathered during that event.
With the introduction of multi-sensor WSNs, a new class of applications which
combines the aforementioned basic monitoring scenarios (LTPM and ED) has
emerged recently. For instance, a patient monitoring application may require
to detect sudden falls of patients while periodically monitoring their heartbeats.
Similarly, a SHM system may be deployed to capture both sudden and long-
term variations in structural health [35][195]. A temperature monitoring system
could generate the evolution of the ambient temperature whilst indicating re
alarms [196]. Since these monitoring applications perform both LTPM and ED
simultaneously, they can be simply referred to as hybrid monitoring applications.
Most of the WSNs deployed in the above monitoring scenarios are based on the
IEEE 802.15.4 standard due to its simple, energy ecient data transmission mech-
anism and its availability in many commercial-o-the-shelf (COTS) platforms.
However, similar to many other standard protocols, the IEEE 802.15.4 data trans-
mission mechanism has some inherent drawbacks. For example, it lacks the adapt-
ability for trac variations and has an inecient contention based medium access
scheme, which introduces many frame retransmissions and idle backos. To over-
come these drawbacks, several improvements to the IEEE 802.15.4 standard have
been proposed in the literature under the following key areas: modications to
the CSMA/CA algorithm [138][197]-[200], adjustments to the superframe struc-
ture [201]-[204], and amendments to the guaranteed time slot (GTS) allocation
scheme [205]-[207]. Taking a generic approach, all these modications improve
the performance of the IEEE 802.15.4 protocol without explicitly focusing on the
underlying monitoring application.
In contrast, some of the other studies have suggested dierent amendments to
the IEEE 802.15.4 protocol to tailor it for specic monitoring applications. Krish-
namurthy and Sazonov [36] have presented a TDMA scheduler that improves the
data transmission reliability and energy eciency in IEEE 802.15.4 based WSNs
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 141
deployed in periodic SHM applications. To meet the stringent data transmis-
sion requirements in industrial applications, Chen et al. [37] have implemented
a new TDMA based superframe structure on top of the standard protocol. Fol-
lowing a similar approach, a new set of MAC superframes has been introduced to
the standard protocol in [208] for the applications with time-critical communica-
tions. Further improvements to the IEEE 802.15.4 protocol in emergency-event-
notifying WSNs have been suggested in [209]-[211]. Moreover, an IEEE 802.15.4
based MAC protocol with adaptive active period and turned-o beacons has been
proposed to enable a low power data transmission mechanism for WSNs used in
long-lived smart utility networks [38]. Based on the design concept of Z-MAC
[57], Gilani et al. [212] have developed an adaptive CSMA/TDMA hybrid IEEE
802.15.4 MAC protocol for the better performance in trac varying WSNs. By
comprehensively reviewing the requirements of human health monitoring applica-
tions, Li et al. [213] have presented a modied IEEE 802.15.4 protocol known as
Hybrid unied-slot access (HUA) protocol, which allocates radio resources exi-
bly in wireless body area networks (WBANs). Similar amendments introduced to
the standard protocol in the context of WBANs can be found in [214]-[216]. How-
ever, all most all these existing application-specic modications were developed
for WSNs with a single monitoring scenario, and none of them were designed to
meet the requirements of hybrid monitoring WSNs.
This chapter presents a new wireless MAC mechanism that enables the IEEE
802.15.4 standard to provide an energy ecient, reliable, and delay bounded data
transmission for hybrid monitoring WSNs. The new protocol modies the `hy-
brid medium access mechanism' proposed in the IEEE 802.15.4 standard (See
Chapter 2 Section 2.4.3.1) by introducing a TDMA schedule to transmit regu-
lar LTPM trac while utilising the standard CSMA/CA mechanism to transmit
rarely and randomly generated ED trac. Several techniques are suggested with
the proposed hybrid protocol to improve the energy performance of LTPM data
transmission, and the reliability in ED data transmission. Given the monitoring
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 142
requirements of the underlying application, the hybrid protocol guarantees the
required quality of service (QoS) in data transmission by controlling the number
of nodes allowed to associate with the network. The proposed protocol was im-
plemented using ns-2 [105], and its performance was evaluated in terms of energy
consumption, delay, and reliability in data transmission. Extensive ns-2 simula-
tions verify the improved performance of the proposed protocol in the context of
hybrid monitoring.
6.2 System Model
The system model considered in this chapter is a beacon enabled IEEE 802.15.4
star topology network composed ofN sensor nodes and a common network coordi-
nator. Each sensor node contains at least two dierent sensors and simultaneously
performs two monitoring scenarios (LTPM + ED) as shown in Figure 6.1.
In the LTPM application, all nodes synchronously monitor the phenomenon of
interest throughout a Tm period for a given monitoring instance. The monitoring
instances occur periodically for every Tcycle duration. During a given monitoring
instance each node generates m data frames of Lltpm backos slots. The LTPM
data frames generated at all nodes should be received completely at the network
coordinator-node (i.e., the sink-node) within Trpt duration, where Trpt ≤ Tcycle,
to enable end-users to extract useful information before the next monitoring in-
stance.
In the ED application, all nodes monitor the phenomenon of interest syn-
chronously with a sampling frequency of 1/Ted. When an event is detected, each
node generates a single data frame of Led backo slots. The alarm data generated
at all nodes should be received at the network coordinator-node before d seconds,
where d < Ted << Trpt, to alert end-users in a timely manner. Due to the delay
constraint on alarm data transmission, the network always operates in the active
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 143
(a) LTPM scenario
(b) ED scenario
Figure 6.1: Hybrid monitoring scenario.
mode1 (i.e., SO = BO). Furthermore, it is assumed that all nodes are within
the carrier sensing range of each others, and only uplink data transmission exists
within the network.
6.2.1 QoS Requirements of Hybrid Monitoring Application
In the system considered, LTPM generates regular data streams in contrast to
the random and infrequent event detections. Therefore, LTPM data should be
transmitted with a minimum energy cost to achieve overall energy eciency in
data transmission. Moreover, the reliability of LTPM data transmission is critical1Only the coordinator-node remains active always, Sensor nodes power-o their radio during
idle times of medium access for energy eciency.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 144
as almost all LTPM data are required to deduce the long-term behaviour of a
certain phenomenon of interest. On the other hand, the delay in transmission is
not a major concern for LTPM data, and hence nodes are allowed to exploit the
`store now and transmit later' mechanism.
On the contrary, ED data have to be transmitted within a stringent time
constraint imposed by the monitoring application. Moreover, they have to be
delivered reliably to generate the complete monitoring report of a given precarious
situation. Energy eciency is not considered as a key requirement for ED data
transmission due to the rarity in event detection.
6.3 New MAC Mechanisms
Because of the disparity between the data generation and QoS requirements of two
monitoring scenarios, two distinct channel access mechanisms are proposed for
LTPM and ED data transmissions. These new MAC mechanisms are developed
on the IEEE 802.15.4 standard without altering any of its PHY layer function-
alities. However, each of the new mechanisms amends the IEEE 802.15.4 MAC
layer functionalities in its own way to meet the requirements of the corresponding
monitoring application as described in the next sections.
6.3.1 MAC Mechanism for LTPM Data Transmission
In the LTPM application, each node generates a signicant amount of data; thus,
the network operates with a high trac volume. Under such trac conditions,
the IEEE 802.15.4 CSMA/CA mechanism - the basic MAC protocol of the stan-
dard - fails to transmit data reliably due to the competition in medium access
[36][93][150]. This competition, which is inherited in any contention based MAC
protocol, causes a signicant amount of energy wastage on backing o, channel
sensing, and frame retransmissions as shown in Chapter 3 Section 7. Therefore,
a scheduled based mechanism that eliminates the competition in medium access
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 145
is required to achieve a reliable and energy ecient LTPM data transmission.
The IEEE 802.15.4 standard provides a schedule based access mechanism us-
ing an optional GTS mechanism (See Chapter 2 Section 2.4.3.1 for more details).
However, this mechanism fails to guarantee dedicated channel resources for all
nodes in large networks as it can allocate only up to seven GTSs at a time. Fur-
thermore, the length of a GTS is restricted to integer multiplies of a superframe
slot1; therefore, a GTS may not exactly t with the duration of a given data
transmission causing a wastage in scarce channel resources. These drawbacks of
the standard GTS mechanism have been investigated in the literature, and some
improvements have been proposed in [205][206][214] with the cost of additional
control overheads. Even in these improved versions of the GTS mechanism, nodes
have to request GTSs from the network coordinator at each time they generate
data. Although this demand based resource allocation is well suited for networks
with dynamic trac generation (e.g., military applications), it creates a signi-
cant amount of unnecessary control overheads (used for allocation, deallocation,
and reallocation of GTSs every now and then) in networks deployed in LTPM
applications where each node periodically generates the same amount of data.
Therefore, instead of the demand assignment GTS mechanism, an IEEE
802.15.4 based xed assignment mechanism is proposed to transmit LTPM data
reliably with minimal energy. As shown in Figure 6.2, the new xed assignment
mechanism is essentially a TDMA based protocol in which the sensor nodes access
specic durations of superframes - known as dedicated time slotss (DTSs) - in a
round robin manner to transmit LTPM data. DTSs in all superframes are equal
in length, and they are located at the end of superframes (i.e., just before the bea-
cons) to minimise time synchronisation errors [217]. During DTSs, LTPM data
frames are transmitted as a continuous ow2 without following the IEEE 802.15.4
CSMA/CA mechanism. Access to the DTS in a given superframe is limited to
1superframe slot = 2SO × 3× unit-backoslot , where SO represents the superframe order.2Data frames are only separated by interframe spacing (IFS) dened in the IEEE 802.15.4
standard.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 146
Figure 6.2: DTS mechanism.
a single node; therefore, dierent nodes access DTSs at dierent superframes
enabling a contention-free MAC mechanism1 for LTPM data transmission.
In a network with N sensor nodes, N consecutive superframes form a trans-
mission cycle where each node gets access to the DTS once (Figure 6.2). The
arrangement of DTSs in a transmission cycle, which is denoted as the DTS sched-
ule, can be completely described using three parameters: the length of the DTS
Tdts, beacon interval of the network BInwk, and number of nodes in the network
N . The network coordinator broadcasts these parameters at the beginning of
each transmission cycle using a special beacon known as the superbeacon. The
format of the superbeacon and calculation of parameters Tdts and BInwk for a
network of N sensor nodes will be presented in detail in Section 6.4.1.
The proposed protocol provides a simple TDMA scheduling mechanism com-
pared with the complex solutions proposed in [36][212] due to its `xed length
and location of DTSs' and `Single DTS per superframe' conditions. Moreover, it
allows nodes to shutdown their transceivers for all the time but their respective
DTSs and beacon durations and thereby minimises the energy consumption in1See Section 6.4.2 for the implementation of this mechanism.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 147
radio-communication. The proposed DTS based MAC mechanism will be referred
to as the DTS mechanism throughout this chapter for clarity.
6.3.2 MAC Mechanism for ED Data Transmission
In the ED application, all data frames have to be transmitted within a stringent
delay constraint. Clearly, the DTS mechanism mentioned above is unable to
meet this strict requirement as it may delay a particular data transmission up to a
complete transmission cycle (i.e., N.BInwk). Thus, the IEEE 802.15.4 CSMA/CA
mechanism - the random access scheme of the standard - is proposed to use during
the periods in between DTSs to transmit randomly generated ED data frames.
The idea of using the CSMA/CA mechanism for a delay bounded reliable
data transmission has its own doubts. As mentioned in the previous section,
CSMA/CA based MAC protocols discard many data frames due to the contention
in medium access at high trac condition. On the other hand, the results pre-
sented in Chapter 3 (see Figures 3.8(c) and 3.9(c)) suggest that the IEEE 802.15.4
CSMA/CA protocol with ACK frames can provide a reliable data transmission
for a large range of network set-ups with dierent number of nodes N and frame
lengths L at low frame arrival rates, which is the typical scenario of the ED appli-
cation. However, it should be noted that this observation was made for networks
with Poisson frame arrivals. On the contrary, in the ED application, data frames
are generated at all nodes synchronously. The synchronicity of frame generation
creates a congestion period in data transmission just after each detection instance
(Figure 6.1); thus, it adversely aects the reliability in data transmission regard-
less of the sampling frequency of the ED application [137][218][219]. Therefore,
as described next, additional techniques have to be devised to improve the data
transmission reliability of the IEEE 802.15.4 CSMA/CA protocol in networks
with synchronised low density trac.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 148
6.3.2.1 Improving Data Transmission Reliability of networks with
synchronised trac
The MAC layer unreliability problem of the IEEE 802.15.4 based WSNs that gen-
erate synchronised trac has been studied comprehensively in [137][218]. Using
experimental results, Francesco et al. [137][218] have emphasised the protocol's
inability to achieve an acceptable degree of transmission reliability, when MAC-
layer parameters have their default values. As a remedy, they have proposed
using of non-standard (out of the possible range) values for MAC parameters
with the cost of longer delays in data transmission. To avoid the synchronised
transmissions occurred right after the radio inactive period of the IEEE 802.15.4
MAC protocol, Misi¢ et al. [220] have suggested deferring each data transmission
for a duration of a frame length. However, their suggestion has overlooked the
impact of the network size on medium access congestion, and hence it fails in
networks with large number of nodes.
Following Misi¢ et al. [220] approach, this section proposes to apply a random
delay for each data frame to break the synchronicity in `starting the CSMA/CA
mechanism at the MAC layer' of networks with synchronised frame arrivals. By
introducing a pseudo-randomness to arrival process, the random delays make
data frames to appear at theMAC layer at dierent time instances1, even though
they are generated synchronously at the Application layer. The proposed random
delays are uniformly distributed within the window [0, Drnd]. The maximum value
of the random delay Drnd can be quantied as Drnd ∝ NL by owing to the fact
that the congestion of the common transmission medium is increased with the
number of nodes in the network N and the length of the data frames L (Section
3.7.2 in Chapter 3). Using a proportional constant δ, Drnd can be given as
Drnd = δNLtunit-backoslot , (6.1)
1Once data frames arrive at MAC layer, the standard CSMA/CA mechanism is used totransmit them.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 149
where tunit-backoslot denotes the duration of a single backo slot1.
The parameter δ is quantied using simulation based experiments, and the
related results are presented in Appendix D.1. According to the experimental
results, when δ ≥ 1.0 the random delay technique improves the data transmission
reliability of networks with synchronised data arrivals up to a certain level that
can be approximated using the reliability of equivalent networks having Poisson
data arrivals. However, it should be noted that larger δ values will increase
Drnd, which in turn increases the delay in transmission. Thus, only the minimum
δ value that validates the above reliability approximation (i.e., δ = 1.0) will be
considered for the rest of the study. For simplicity, the IEEE 802.15.4 CSMA/CA
protocol associated with the random delay technique will be referred to as the
randomly-delayed CSMA/CA throughout this chapter.
Given that the randomly-delayed CSMA/CA provides a reliable transmission,
an upper bound Dmax for the delay in data transmission can be presented as
Dmax = Drnd +Dcsma, (6.2)
where Drnd and Dcsma represent the maximum delays introduced by the ran-
dom delay and the standard CSMA/CA mechanism, respectively. While Drnd is
computed using (6.1), Dcsma can be given as
Dcsma =
[∑macMaxBE - 1
i=macMinBE2i + [(macMaxCSMABackos + 1) + macMinBE
−macMaxBE ]× 2macMaxBE
+ncca × (macMaxCSMABackos + 1) + (Led + tack)
]× (macMaxFrameRetries + 1)× tunit-backoslot , (6.3)
where macMinBE, macMaxBE, macMaxCSMABackos, and macMaxFrameRe-
tries represent the CSMA/CA related parameters (denitions and default values
1= 0.00032 s in the 2.4-GHz physical layer.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 150
can be found in Table 3.1 in Chapter 3); and ncca, Led, tack , and tunit-backoslot
represent the number of CCAs before a transmission (default value = 2), length
of the ED data frame in backo slots, waiting time for relevant ACK frame (= 3
backo slots), and the duration of a backo slot, respectively.
Thus, it appears that the randomly-delayed CSMA/CA mechanism not only
improves the data transmission reliability, but also delivers data within a certain
delay boundary.
6.4 Hybrid Protocol
After choosing appropriate MAC mechanisms for each monitoring application, it
has to be meticulously decided how these two MAC mechanisms are going to be
coexist in hybrid monitoring WSNs. The straightforward solution would be using
a switching scheme where the application layer essentially signalling the MAC
layer to use the most appropriate MAC for the application. Even though this
approach is standard compliant and easy to implement, it will not be optimal
in hybrid monitoring. If both applications generate data simultaneously, the
switching scheme can only serve a single application as only one MAC mechanism
can operate at a given time with this scheme. Therefore, in such situations,
measurements from the other application could not be collected within the delay
constraint specied. This causes a measurement hole in the monitoring, and
consequently it will jeopardise the integrity of the hybrid monitoring application.
Therefore, in this section, the proposed medium access control mechanisms for
LTPM and ED data transmissions are merged (instead of switched between each
other) to create a hybrid data transmission mechanism that fulls QoS require-
ments of the hybrid monitoring application. In this hybrid protocol, LTPM data
are transmitted as a continuous ow during the DTS of each superframe, and then
the time in between DTSs (i.e., contention access period (CAP)) is utilised to
transmit delay bounded ED data using the randomly-delayed CSMA/CA mech-
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 151
Figure 6.3: Hybrid MAC mechanism.
anism as shown in Figure 6.3.
These two MAC mechanisms are unied with the following strategies to guar-
antee the delay bounded transmission of ED data even in the presence of DTSs:
Backo Counter Freezing: Suppose an event has occurred just before the
beginning of a DTS. Then, the corresponding ED data frames may be dropped
due to the channel access failures as the channel is busy with LTPM data trans-
missions. To avoid this scenario, the backo counter (including the random delay)
of the randomly-delayed CSMA/CA freezes during the time reserved for DTSs.
When the DTS duration expires, the backo counter resumes decreasing its value
as usually. This strategy virtually hides the DTS schedule from the random-
delayed CSMA/CA mechanism.
Minimum Duration of CAP: Suppose the CAP of superframes is too
small such that the ED data frames generated by a particular event have to be
transmitted over a several superframes. This will increase the delay in ED data
transmission as the backo-counter-freezing eect adds an additional Tdts waiting
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 152
time for each superframe. Therefore, the minimum duration of CAP of the hybrid
protocol is set to Dmax (Equation 6.2) to ensure that ED data transmissions of
an event occurred at any instant of the current sueperframe will be completed
before the beginning of the DTS in the next superframe. In other words, this
strategy limits the additional delay experienced by ED data transmissions, due
to the unication process, to a single Tdts duration.
Because of the above strategies, the duration of DTS Tdts needs to be con-
trolled to meet the delay requirement of ED data transmission. On the other
hand, controlling Tdts limits the amount of LTPM data transmitted during a su-
perframe, which in turn limits the total amount of LTPM data transmitted by all
nodes in a given network within Trpt. Therefore, for a given hybrid monitoring
application, there exists a maximum number of nodes Nmax that can operate with
the proposed hybrid protocol.
The proposed hybrid protocol has two operational phases: initial phase and
steady phase. At the beginning of the the initial phase Nmax is computed. Then,
the network set-up takes place by associating sensor nodes and establishing the
DTS schedule. After the initial phase, the steady phase commences where all data
transmissions and modications to the established DTS schedule (if required)
occur. The two phases are described in detail below.
6.4.1 Initial Phase
The initial phase starts with the initialisation of the network coordinator. During
its initialisation, the network coordinator is fed with the following requirements
of the hybrid monitoring application:
• Number of LTPM data frames m generated during Tm in a sensor node,
• Reporting cycle Trpt of LTPM data,
• Delay bound d for ED data,
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 153
• Length of LTPM data frames Lltpm,
• Length of ED data frames Led.
Using the above application-specic requirements and a few other IEEE 802.15.4
protocol-specic characteristics (i.e., beacon frame duration, Rx-to-Tx turnaround
duration, inter frame spacing (IFS), CSMA/CA parameters, and unit bakco
length) the network coordinator calculates the following quantities:
• Transmission duration tltpm of single LTPM data frame (including IFS),
• Maximum delayDcsma caused by CSMA/CAmechanism (See Equation (6.3)),
• Transmission duration tbcn of beacon frames (including Rx-to-Tx turnaround).
Next, the network coordinator nds the number of nodes Nmax that can be
associated with the network by calculating the following intermediate parameters:
DTS length Tdts: This can be given as
Tdts = nltpmtltpm, (6.4)
where nltpm is the number of LTPM data frames transmitted within the DTS.
nltpm can be computed as
nltpm =
⌊d− (tbcn +
Dmax︷ ︸︸ ︷Drnd +Dcsma)
tltpm
⌋, (6.5)
by considering the fact that the maximum delay in ED transmission should be
less than the delay bound of the ED application, i.e.,
Tdts + tbcn +Dmax < d. (6.6)
Number of DTSs ndts required for a single node to transmit LTPM
data within a reporting cycle: Since the number of LTPM frames that a node
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 154
has to transmit during a single report cycle equals m,
ndts =
⌈m
nltpm
⌉. (6.7)
Minimum beacon interval BImin−nwk that the network can operate: Due
to the minimum duration of CAP, the network should operate with a BI1 that
satises,
BI ≥Tdts︷ ︸︸ ︷
nltpmtltpm +
Dmax︷ ︸︸ ︷Drnd +Dcsma +tbcn. (6.8)
Thus, the minimum BI value that satises the above inequity gives BImin−nwk.
Then, Nmax can be obtained using BImin−nwk as
Nmax =
⌊Trpt
ndtsBImin−nwk
⌋(6.9)
due to the fact that a single DTS corresponds to a single BI.
However, it should be noted that the calculation of Drnd in (6.5) and (6.8)
requires the knowledge about total number of nodes in the network [refer (6.1)].
Since this information is not available at the beginning of the initial phase2,
the recursive algorithm described in Algorithm 6.1 is used to nd Tdts, ndts,
BImin−nwk, and Nmax. The algorithm initialises with the maximum number of
nodes Nmax−ltpm allowed to associate with the network when there exists only the
LTPM application [See Appendix D.2].
Once Nmax is calculated, the network coordinator begins the association pro-
cess to allow sensor nodes to join the network. For this purpose, the network
coordinator starts a countdown timer3, sets BI to BImin−nwk, and begins bea-
con transmission. When beacons are received, sensor nodes follow the standard1Because of the assumption SO = BO, the beacon interval and superframe duration can
be interchanged. As mentioned in Chapter 2, BI = aBaseSuperframeDuration × 2BO, where0 ≤ BO ≤ 14. Thus, BI can represent only 15 dierent values.
2The network coordinator is in the process of nding the maximum number of nodes per-mitted in this phase.
3The countdown timer sets the duration of the initial phase, Tinit.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 155
Algorithm 6.1 Find Nmax, Tdts, ndts, and BImin−nwk.Calculate Nmax−ltpmInitialise : Nprev = 0; Ncurrent = Nmax−ltpmwhile Ncurrent 6= Nprev doNprev ← Ncurrent
Calculate Drnd substituting Nprev in (6.1)Calculate Tdts, ndts, BImin−nwk, and Nmax using (6.4) (6.9)Ncurrent ← Nmax
end whilereturn Tdts, ndts, BImin−nwk, and Nmax
association procedure specied in [61]. For each node associated, the network co-
ordinator assigns a unique number (denoted as association number) that equals
i for the i-th node to associate, where i ∈ N and 1 ≤ i ≤ Nmax. The associa-
tion numbers are used to distinguish sensor nodes from each other instead of the
64-bit long device addresses recommended by the IEEE 802.15.4 standard. The
respective association number and the remaining time to expire the initial phase
(i.e., current value of the countdown timer) are informed to each node during
their associations. The information about the expiring time of the initial phase
enables nodes to shutdown their transceivers until the beginning of the steady
phase.
The network association process ends either by expiring the countdown timer
(i.e., elapsing Tinit duration) or by reaching the number of nodes associated with
the network N to Nmax. In the latter case, the network coordinator waits until
the countdown timer expires to stop the initial phase. At the end of the initial
phase, the network coordinator calculates the Drnd, nltpm, Tdts, and ndts using
(6.1), (6.4) (6.7) for the current network of N nodes. To reduce the energy
consumed for beacon listening in the current network, the network coordinator
then sets BI to its maximum value BInwk that can operate with the given LTPM
application. For a network of N nodes, BInwk equals to the maximum value of
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 156
Figure 6.4: SuperBeacon payload.
BI that satises the inequality
Trpt ≥ NndtsBI. (6.10)
Finally, the network coordinator declares the end of the initial phase by trans-
mitting the rst superbeacon. The superbeacon frame fully conforms with the
standard beacon format specied in [61] and carries DTS-schedule-specic infor-
mation in its payload as illustrated in Figure 6.4.
SuperBeacon Payload: The superbeacon payload consists of three com-
mon elds: isSuperBeacon, superFrameNumber, and ACK ; and three superbea-
con elds: isChanged, dtsSchduleData, and disAssociatedNodes. The common
elds appear in both superbeacons and general beacons (i.e., the beacons trans-
mitted in between superbeacons) of the hybrid protocol, while the superbeacon
elds present only in superbeacons.
The single bit long isSuperBeacon eld is used to distinguish superbeacons.
It is set to one in superbeacons and to zero in general beacons. The superFra-
meNumber is 10 bits in length and contains the corresponding sequence number
of the current superframe within the transmission cycle. It is always set to one
in superbeacons owing to the fact that each superbeacon marks the beginning of
respective transmission cycles. On the other hand, the superFrameNumber may
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 157
vary from two to N1 in general beacons depending on their positions within the
transmission cycle. The ACK eld is one bit in length and is set to one if and only
if all LTPM data frames transmitted during the DTS of the previous superframe
were received successfully. If not, the ACK eld is set to zero.
The isChanged eld of superbeacon elds is set to one if the existing DTS
schedule has been modied. Otherwise it is set to zero. The dtsSchduleData eld
contains the number of node associated with the DTS schedule N and duration
of the DTS in backo slots Tdts. The beacon interval of the network, which is
the remaining parameter required to construct the DTS schedule, is explicitly
contained in the `Superframe Specication' eld of the beacon frame (see Figures
44 and 47 in [61]). Therefore, it is not included in the dtsSchduleData eld to
avoid the repetition.
If the current DTS schedule has been changed due to disassociation of some
nodes, the disAssociatedNodes eld will contain their association numbers. This
information helps the remaining nodes to adjust to the new DTS schedule by
reordering their association numbers as described in the next section.
6.4.2 Steady Phase
The steady phase starts with the transmission of the rst superbeacon. At the
beginning of this phase, all associated nodes listen to the superbeacon, obtain the
DTS-schedule-specic parameters, and consequently construct the DTS schedule
that is followed by the entire network. Then, nodes track the sequence number
of each superframe (by decoding the superFrameNumber eld of beacon frames)
to start the data transmission.
Data Transmission: In the i-th superframe of the transmission cycle, the
node with association number i (i ∈ 1, 2, ..., N) accesses the DTS and transmits
LTPM data. If the data transmission in the DTS was successful (i.e., ACK eld
1i.e., the total number of nodes associated with the network. For practical WSNs, themaximum value of N is assumed to be equal to 1024.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 158
of the next beacon is received as one), the node's transceiver goes back to sleep.
Otherwise, the node retransmits the corrupted or lost LTPM data1 immediately
using the standard CSMA/CA mechanism without waiting for its next allocated
DTS. Meanwhile, if an event is detected all nodes transmit ED data using the
randomly-delayed CSMA/CA mechanism during the CAP in between DTSs. Af-
ter N superframes, the network coordinator transmits the next superbeacon to
indicate the beginning of the next transmission cycle where the data transmission
continues in similar manner.
Apart from the data transmission, the association of new nodes and disassoci-
ation of existing nodes may also occur during the steady phase. New nodes may
be associated with the network either to replace dead nodes or to increase the
monitoring entities. On the other hand, existing nodes those fail to transmit data
(due to depleted energy level or other hardware failures) would be disassociated
from the network to avoid under-utilised resource reservations.
New Node Association: A new node added to the network follows the
standard association procedure of the IEEE 802.15.4 standard. It sends an `as-
sociation request' command once it receives a beacon frame (Note: Since this
communication takes place at the beginning of superframes, it does not overlap
with DTSs located at the end of superframes). When the network coordinator
receives the `association request' command, it veries the total number of nodes
in the network including the new node Nnew with Nmax computed in the initial
phase. If Nnew ≤ Nmax, the network coordinator accepts the association and send
`association success' command with the relevant association number. Otherwise,
it ignores the request. Although the association is success, the new node has to
wait until the next transmission cycle to transmit data.
Meanwhile, the network coordinator calculates the new values for Tdts, ndts,
and BInwk for the network of Nnew nodes using (6.4) (6.10). Then, it broadcasts
1Although the DTS mechanism provides a non-contentional MAC scheme, data frames canbe still lost or corrupted due to channel errors and other interferences.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 159
the new DTS schedule by transmitting a superbeacon with modied parameters
at the beginning of the next transmission cycle. Accordingly, the nodes update
the necessary parameters, reconstruct the DTS schedule, and continue with the
steady phase.
Existing Node Disassociation: If a node does not transmit during its
DTS for consecutive 2j transmission cycles, the network coordinator disassoci-
ates that node from the network. By following a similar approach to the GTS
disassociation in the standard protocol [61], the value of j is determined as
j = 2(8−BOnwk) 0 ≤ BOnwk ≤ 7
j = 2 8 ≤ BOnwk ≤ 14 (6.11)
where BOnwk represents the current beacon order of the network. Due to the lim-
ited length of disAssociatedNodes eld in superbeacons, the network coordinator
can disassociate only up to four nodes during a transmission cycle.
At the beginning of the next transmission cycle after a disassociation, the net-
work coordinator informs the remaining nodes about the modications required
to the DTS schedule (i.e., new values of Tdts, N , and BInwk) along with the as-
sociation numbers of the disassociated nodes. The remaining nodes then update
the relevant parameters, and change their association numbers appropriately1.
This reordering maintains the continuity in the sequence of association numbers,
and hence it guarantees the allocation of DTSs for all the remaining nodes in the
new DTS schedule.
If a node does not have any data to transmit during its allocated DTS2, it
will transmit a dummy data frame at the beginning of that DTS to avoid being
disassociated. The dummy frame has the same format of a general data frame1i.e., the nodes with higher association numbers than that of the disassociated nodes de-
crease their association numbers by relevant steps, while the others remain with the existingassociation numbers.
2In general, this situation may occur near the end of Trpt period as transmission cycles maynot perfectly align with the reporting cycle.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 160
(Figure 52 in [61]), however its payload contains only a single byte of all zeros.
6.5 Results and Discussion
In this section, the performance of the proposed MAC mechanism is investigated
using simulation based experiments. To this end, a new simulation platform was
developed on top of the existing implementation of the IEEE 802.15.4 protocol in
ns-2 simulator [108][126]. The functionalities of the protocol were implemented
at the Service Specic Convergence (SSCS) and Medium Access Control (MAC)
sub-layers either by developing new modules or by modifying the existing mod-
ules (note: A summary of the new implementation is presented in Appendix
C.2.) These new implementations and modications were done in a way such
that a network can operate in one of the three dierent MAC mechanisms: the
DTS mechanism1, randomly-delayed CSMA/CA mechanism, and hybrid (DTS
+ randomly-delayed CSMA/CA) mechanism.
Using the new simulation platform, four dierent experiments were carried
out to evaluate the performance of the proposed hybrid MAC protocol and its
basic components. In the rst experiment, the performance of the DTS mecha-
nism is studied by investigating its ability to deliver a reliable, energy ecient
data transmission mechanism for LTPM data. The second experiment investi-
gates the performance of randomly-delayed CSMA/CA mechanism in delivering
delay-bounded, reliability-critical ED data. Then in the third experiment, the
performance of the hybrid MAC protocol is studied in the context of a hybrid
monitoring scenario deployed in a structural health monitoring (SHM) applica-
tion. Finally, the network scalability of the proposed hybrid protocol is examined
during the fourth experiment.
In all experiments, a beacon enabled star topology network comprised with N
sensor nodes and a common coordinator is assumed. The 2.4 GHz physical layer1Algorithms related to this mechanism can be found in Appendix D.2.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 161
Table 6.1: Simulation parameters.
Parameter Value Parameter Value
PHY layer 2.4 GHz band
Channel rate 250 kbps Radio Characteristics
Propagation model Two-ray ground Transmit Power 30.67 mW
PHY header 6 Bytes Receive Power 35.28 mW
MAC header 13 Bytes Idle Power 712 µW
tunit-backoslot 320 µs Sleep Power 144 nW
tbcn 3 backos
tack 3 backos MAC Parameters
IFS 2 backos Default Max
Rx-to-TX turnaround 0.6 backos macMinBE 3 8
Tinit 600 s macMaxBE 5 8
δ 1.0 macMaxCSMABackos 4 5
BO†† 9 macMaxFrameRetries‡ 3 7
SO† 9† only used in simulations related to the standard protocol‡ macMaxFrameRetries is set to 2 in hybrid protocol simulations to minimise Dcsma
and the two-ray ground propagation model are chosen as the wireless communi-
cation channel between sensor nodes and the coordinator. The wireless channel
is assumed to be error-free, and no hidden nodes are considered. Thus, in all sim-
ulations, frames are dropped only due to channel access failures (i.e. number of
backo stages exceedmacMaxCSMABackos+1) or frame retransmission failures
(i.e. number of transmission attempts exceed macMaxFrameRetries + 1). Unless
mentioned otherwise, ACK frames and frame retransmissions are deployed in all
experiments, and the default values of all MAC-layer parameters are assumed.
Furthermore, nodes transceivers are assumed to be Chipcon CC2420 radios, and
hence their energy characteristics are represented using the energy model de-
veloped in [126][149]. These radio characteristics along with other simulation
parameters are summarised in Table 6.1. Simulation trials for each experiment
are run independently, and their results are averaged over 50 dierent seeds.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 162
6.5.1 Experiment 1 - Performance Evaluation of the DTS
Mechanism
In this experiment, the performance of the DTS mechanism is studied in terms
of its reliability (expressed by the reliability factor 1 R) and power consumption
(expressed by the per-bit energy cost2) of LTPM data transmission. For compar-
ison, the performance of the standard medium access mechanism of the IEEE
802.15.4 MAC protocol (i.e., CSMA/CA mechanism) in LTPM applications is
also examined. Simulation results were obtained for three LTPM scenarios of
dierent data generation rates represented by three m values3: 250, 500, and
1000. For all simulations, the reporting cycle Trpt and LTPM data frame length
Lltpm were set to 10 minutes and 12 backo slots, respectively. In simulations
associated with the IEEE 802.15.4 CSMA/CA mechanism, LTPM data frames
were transmitted at continuous bit rates (CBRs) throughout the reporting cycle
to minimise the contention in channel access. Each simulation trial was run for
a 100 Trpt duration.
Reliability and power consumption of the DTS mechanism and IEEE 802.15.4
CSMA/CA mechanism in LTPM data transmission are shown in Figures 6.5 and
6.6, respectively. As shown in Figure 6.5, the IEEE 802.15.4 CSMA/CA mech-
anism fails to transmit LTPM data reliably even in networks with a few sensor
nodes (N ≤ 8) due to its contention based medium access. Moreover, the data
transmission reliability of the CSMA/CA mechanism decreases rapidly with in-
creased network size and data generation, as they intensify the contention in
medium access. A substantial improvement can be observed in the data trans-
mission reliability when the CSMA/CA mechanism is applied with ACK frames
1i.e., the ratio between the number of frames successfully received by the coordinator andthe total number of frames generated by all sensor nodes.
2i.e., the average power consumption per successfully transmitted data bit.3Note: When the reporting cycle Trpt and LTPM data frame length Lltpm are xed, the
data generation rate can be completely represented by the number of data frames m generatedwithin a reporting cycle.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 163
Figure 6.5: Reliability in LTPM data transmission.
and frame retransmissions. However, these improved values too fall below the ex-
pected degree of reliability of LTPM data transmission (i.e., R ≈ 100%). On the
other hand, irrespective of the network size and data generation rate, the DTS
mechanism achieves the ideal data transmission reliability for LTPM applications
by completely eliminating the contention in medium access (note: R curves and
per-bit energy cost curves of DTS mechanism for dierent m values lie on top of
each other in Figures 6.5 and 6.6, respectively).
Contrast to the signicantly increasing power consumption of the IEEE 802.15.4
CSMA/CA protocol, that of the DTS mechanism remains constant with increased
network sizes and data generation rates. More Importantly, that constant value
is signicantly less than the power consumption of the IEEE 802.15.4 CSMA/CA
protocol (both with and without ACK) in all LTPM scenarios considered. For
instant, when m = 1000 and N = 40 the per-bit energy cost of the DTS mecha-
nism is approximately 12 times and 16 times less than that of the IEEE 802.15.4
CSMA/CA protocol with ACK and without ACK, respectively (Figure 6.6).
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 164
Figure 6.6: Power consumption in LTPM data transmission.
6.5.2 Experiment 2 - Performance Evaluation of the Randomly-
delayed CSMA/CA Mechanism
This experiment investigates the performance of the randomly-delayed CSMA/CA
mechanism in terms of delivering a reliable and delay bounded transmission mech-
anism for ED applications that generate synchronised low-density trac. To
this end, eight network congurations comprised of dierent network sizes N
and frame lengths Led were considered. In each network conguration, sensor
nodes were deployed in an ED application where they sample the monitoring
phenomenon at a rate of one sample per second synchronously. By consider-
ing the worst case scenario, it was assumed that each node detects an event at
each sampling instance, and hence generates ED frames at the sampling rate.
ED frames are transmitted to the coordinator node using three dierent sets of
transmission strategies:
1) IEEE 802.15.4 CSMA/CA with default MAC parameter values,
2) IEEE 802.15.4 CSMA/CA with maximum MAC parameter values, and
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 165
3) Randomly-delayed IEEE 802.15.4 CSMA/CA with default MAC parameters
values (δ = 1.0).
Simulation trials of each transmission strategy in a given network conguration
run until each node completes 20, 000 frame transmissions.
As shown in Figure 6.7a, the standard IEEE 802.15.4 CSMA/CA protocol
with the default parameter values is not reliable enough to transmit synchronised
low-density ED trac. This is because the default MAC parameter values are
unable to break the synchronicity in data transmission procedures. On the other
hand, the maximum MAC parameter values relax that synchronicity up to some
extend; therefore, the standard protocol with the maximum parameter values
exhibits an acceptable level of reliability in data transmission particularly in small
networks (N ≤ 10). Nevertheless, this transmission strategy too fails in data
transmission reliability when the network size and/or frame length increase. In
contrast, the randomly-delayed CSMA/CA mechanism achieves an almost ideal
degree of reliability1 (i.e., R ≈ 100%) in all considered networks as it completely
breaks the synchronicity in data transmission regardless of network size and frame
length.
The maximum transmission delay caused by the randomly-delayed CSMA/CA
mechanism is depicted in Figure 6.7b. For each network conguration, the av-
erage and range2 of the maximum delay are presented along with the respective
theocratical upper bound Dmax, which is derived in Section 6.3.2. As shown in
Figure 6.7b, the maximum delay in data transmission increases with network size
N and frame length L as Drnd ∝ NL. This relationship has been already taken
into account while calculating Dmax, and hence the experimental maximum delay
lies noticeably below the theocratical upper bound for all networks considered.
Therefore, it is apparent that a randomly-delayed CSMA/CA based WSN de-
1Note that the randomly-delayed CSMA/CA is a contention based MAC mechanism; thus,it does not guarantee R = 100%.
2i.e., the dierence between the maximum and minimum values obtained for the maximumdelay in all simulation trials related to a given network conguration.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 166
(a) Reliability
(b) Maximum delay
Figure 6.7: Performance of the randomly-delayed CSMA/CA mechanism in EDdata transmission: (a) reliability R (b) maximum delay.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 167
Table 6.2: Sensor measurements and monitoring application requirements.
Sensor Characteristics Application requirementsMeasuring Number Sampling Sample Monitoring Reporting Delay
quantity of axes frequency size period period constraint
naxes fs (Hz) ls (bits) Tm (s) Trpt (min) d (s)
Acceleration 3 200 16 210 30 −Strain 3 1 16 − − 0.25, 0.5, 1.0
ployed in an ED application will satisfy the delay constraint d of the monitoring
application when Dmax ≤ d.
6.5.3 Experiment 3 - Performance Evaluation of the Hy-
brid Protocol
In this experiment, a proof of concept for the proposed hybrid MAC protocol is
presented in the context of a structural health monitoring (SHM) application.
Then, the performance of the hybrid protocol is examined in the same context
with regard to its reliability, power consumption, and delay in data transmission.
The SHM application considered in this experiment is a hybrid monitoring
application where a WSN is deployed to monitor structural strains and acceler-
ations, which are the most common SHM measurements found in the literature
[221]. Thus, each sensor node in this hybrid monitoring WSN is comprised of an
accelerometer and a strain transducer. Sensor nodes collect acceleration measure-
ments periodically to enable long-term structural health analyses, while monitor-
ing strains to detect sudden anomalies in the structural health. The character-
istics of these sensor measurements and the monitoring application requirements
are formulated in Table 6.2 based on previous studies on SHM [221]-[223].
Using the SHM application requirements in Table 6.2 and the IEEE 802.15.4
protocol-specic characteristics in Table 6.1, the initialising-parameters m, Lltpm,
and Led of the hybrid protocol in the above monitoring application can be de-
termined as 2400 frames, 12 backo slots, and 3 backo slots, respectively (See
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 168
Table 6.3: DTS-scheduling parameters Tdts, ndts, and BInwk (when Trpt = 30 min.).
Application 1 Application 2 Application 3Number d = 0.25 s d = 0.5 s d = 1.0 s
of m = 2400 m = 2400 m = 2400
nodes Tdts† ndts BInwk
† Tdts ndts BInwk Tdts ndts BInwkN [BO]‡ [BO] [BO]
8 336 100 1.96608 [7] 1120 30 3.93216 [8] 2590 13 15.72864 [10]
16 322 105 0.98304 [6] 1092 31 1.96608 [7] 2590 13 7.86432 [9]
24 294 115 0.49152 [5] 1050 32 1.96608 [7] 2590 13 3.93216 [8]
28 280 120 0.49152 [5] 1050 32 1.96608 [7] 2590 13 3.93216 [8]
32 −§ − − 1050 32 0.98304 [6] 2590 13 3.93216 [8]
40 −§ − − 1022 33 0.98304 [6] 2590 13 1.96608 [7]† Tdts and BInwk are expressed in backo slots and seconds, respectively‡ Corresponding beacon order BO§ Nmax equals to 28 in Application 1
Appendix D.3 for computation). Along with these parameter values, three dier-
ent delay constraints (i.e., 0.25 s, 0.5 s, and 1.0 s) are considered. Consequently,
the hybrid protocol is simulated for three monitoring applications: Application 1
( d = 0.25 s, m = 2400), Application 2 (d = 0.5 s, m = 2400), and Application
3 (d = 1.0 s, m = 2400). Using simulations, the maximum number of sensor
nodes Nmax that is allowed to form a network for each of the above applications
are found as 28, 52, and 122, respectively. DTS-scheduling parameters Tdts, ndts,
and BInwk of various networks deployed in these monitoring applications are then
obtained, and they are summarised in Table 6.3.
The parameter values in Table 6.3 lead to the following observations on the
behaviour of the hybrid protocol:
• Tdts shrinks with decreasing d. This decrement, which occurs to facilitate
the tightened delay bound in ED data transmission, conforms with Inequal-
ity (6.6). Consequently, ndts increases, and BInwk decreases with stricter
delay requirements,
• BInwk decreases with increasing N . This observation conforms with In-
equality (6.10),
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 169
• Tdts decreases with increasing N . This decrement, which occurs to accom-
modate the increased Drnd with rising N , conforms with Equations (6.5)
and (6.4). However, for larger Tdts, increments in Drnd may not be signif-
icant enough to make an impact on relevant ndts value (Equations (6.5)
(6.7)). Thus, in such situations, neither ndts nor Tdts changes with N as
seen in the Application 3.
These observations along with the parameter values in Table 6.3 provide a proof-
of-concept for the proposed hybrid protocol by demonstrating its ability to con-
struct dierent DTS schedules based on the network size and application require-
ments.
Next, the performance of the hybrid protocol in the aforementioned moni-
toring applications is investigated. With each monitoring application, three dif-
ferent event detection rates EDrates 1: one frame/min, 10 frames/min, and 60
frames/min; are considered. Two additional m values (i.e., 1200 and 4800) are
also employed for some simulations. Similar to the previous experiments the per-
formance of the protocol is compared with that of the standard IEEE 802.15.4
MAC protocol. In simulations related to the standard protocol, LTPM data
frames are transmitted at continuous bit rates (CBRs) throughout the reporting
cycle while ED data frames are transmitted synchronously at their detection rates.
On the other hand, in simulations related to the hybrid protocol, LTPM data are
transmitted using the DTS mechanism while the randomly-delayed CSMA/CA
mechanism is deployed to transmit ED data frames. Each simulation trial in this
investigation runs for a 500 Trpt duration.
The reliability of data transmission R of dierent networks deployed in the
Application 3 2 (i.e., the one with d = 1.0 s) is shown in Figure 6.8. As expected,
1Since extreme events are rare, it is dicult to study their impact on the performance ofthe protocol. Thus, they are assumed to be regular and frequent during this investigation.
2According to simulations, the reliability and power consumption of networks deployed inall three applications show similar trends respectively. Therefore, the results obtained only forthe Application 3 are presented here to avoid the repetition.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 170
(a) LTPM data transmission (m = 2400, Trpt = 30 min, d = 1.0 s)
(b) ED data transmission (EDrate = 60 frms./min, Trpt = 30min, d = 1.0 s)
Figure 6.8: Data transmission reliability of hybrid protocol: (a) LTPM datatransmission with varying EDrate (b) ED data transmission with varying m.
neither ED data nor LTPM data are delivered reliably in networks with the
standard IEEE 802.15.4 protocol. Furthermore, in such networks a rise in one
type of data transmission will adversely aect the transmission reliability of the
other type of data as shown in Figures 6.8(a) and (b). This is because the standard
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 171
protocol uses the same MAC mechanism simultaneously for both LTPM and ED
data transmissions. In contrast, the networks with the hybrid protocol, which use
two separate MAC mechanisms for LTPM and ED data, always achieve a near
ideal level of reliability (R ≈ 100) for both types of data transmissions regardless
of network size and monitoring-application requirements.
Figure 6.9: Power consumption of hybrid protocol (m = 2400).
Per-bit-energy costs1 of both hybrid and standard protocols are illustrated in
Figure 6.9. For all network sizes and ED rates considered, the hybrid protocol
consumes signicantly less amount of power compared with the standard pro-
tocol. The improved energy performance of the hybrid protocol can be mainly
attributed to the energy eciency of the DTS mechanism in LTPM data transmis-
sion (See Figure 6.6). Moreover, the hybrid protocol shows only an insignicant
increment in the per-bit energy cost with rising event detections, in contrast to the
standard protocol's notably escalating power consumption under the same condi-
tions. This is because the hybrid protocol relax the medium-access-contention in
ED data transmission by introducing random delays appropriately. Even though1Unlike reliability, the energy consumption on ED and LTPM data transmissions can not
be determined separately (in particular for the standard protocol). Thus, `per-bit energy cost'of the overall transmission process is considered here.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 172
(a) d = 0.25 s (b) d = 0.5 s
(c) d = 1.0 s
Figure 6.10: Maximum delay in ED data transmission.
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 173
random delays escalate with increased network size causing longer backing o
durations, their impact on the per-bit-energy cost is trivial as shown in the closet
in Figure 6.9. Therefore, sensor nodes conformed with the hybrid protocol in
a given monitoring application consume all most a constant amount of power
regardless of the network size N . This fact highlights the energy stability of the
proposed hybrid protocol.
The delay characteristics of the proposed hybrid protocol is investigated next.
To this end, the maximum delay in ED data transmission of dierent networks
deployed in all three monitoring applications has been obtained, and the results
are illustrated in Figure 6.10. For all considered monitoring applications and
network sizes, the hybrid protocol is able to transmit ED data within the delay
constraint imposed by the monitoring application. The protocol achieves this
by setting an upper bound that is less than or equal to the delay constraint
d. According to Equation (6.6), this upper bound can be quantied as Tdts +
tbcn + Drnd + Dcsma. In each network considered, the maximum delay in ED
data transmission varies in between the upper bound and the respective Tdts
value as shown in Figure 6.10. If the upper bound tends to exceed d due to
increasing network size, the protocol automatically adjusts it by lowering the
DTS length. This reduction may cause subsequent decrements in the maximum
transmission delay (Figures 6.10(a) and (b)). On the other hand, for a given DTS
length the maximum delay increases with the network size N due to rising Drnd
(Figure 6.10(c)).
6.5.4 Experiment 4 - Network Scalability of the Hybrid
Protocol
The proposed hybrid protocol controls the number of nodes allowed to form a
sensor network for a given hybrid monitoring application (See Section 6.4.1).
Although this control is needed to satisfy the requirements of the underlying
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 174
application, it may aect the size/scale of the sensor network. Therefore, to in-
vestigate the network scalability of the hybrid protocol, this experiment examines
the maximum number of nodes Nmax that is allowed to form a network under
dierent application requirements. For this purpose, a network-coordinator-node
developed in the simulation environment was initialised repeatedly with dierent
values of the application specic parameters Trpt, m, and d. During theses sim-
ulations, the parameters listed in Table 6.1 were applied. In addition, the frame
length parameters Lltpm and Led were set to 12 and 3 backo slots, respectively.
At the end of the each initialisation trial, the Nmax value generated at the network
coordinator is observed.
As shown in Figures 6.11(a) and 6.11(b), a higher network scalability can
be achieved either by relaxing the delay constraint d of the ED application or
by decreasing the amount of LTPM data m generated or by increasing the re-
porting cycle Trpt. This observation has been mathematically represented by
Equation (6.9) in which Nmax ∝ Trpt and Nmax ∝ 1/ndts [note: ndts ∝ m and
ndts ∝ 1/d according to (6.7) and (6.5)]. Moreover, it is worthwhile to observe
that Nmax for a given Trpt reaches to an upper limit as m decreases and d relaxes
(See the inner-right corner of Figures 6.11(a.2) and (b.2)). The upper limit of
Nmax is achieved when the parameters m and d are relaxed to certain values such
that all m frames would be able to be transmitted within a single DTS (i.e.,
ndts = 1) and BInwk would reaches to its minimum possible value. However,
it should be noted that even this minimum value of BInwk satises the mini-
mum CAP requirement presented in Section 6.4 to enable delay bounded ED
data transmission. In contrast, there is no such minimum CAP requirement in
networks deployed entirely in LTPM applications. Therefore, a higher network
scalability can be achieved in such networks than those deployed in hybrid mon-
itoring applications with equivalent LTPM requirements. For comparison, the
Nmax−ltpm values obtained for the same set of LTPM requirements have been
depicted in Figures 6.11(a.1) and (b.1).
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 175
(a) Reporting cycle, Trpt = 1 min.
(b) Reporting cycle, Trpt = 5 min.
Figure 6.11: Network scalability of hybrid protocol (in terms of the maximumnumber of nodes allowed to form the network Nmax).
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 176
When the monitoring requirements become strict, Nmax of the proposed hy-
brid protocol decreases. However, the protocol is capable of forming a network
(i.e., Nmax > 0) even for monitoring applications with extreme requirements as
illustrated in Figure 6.11(a) (For instance, the protocol forms a network with 5
nodes for a monitoring application where m, Trpt, d, Lltpm, and Led are equal to
1000 frames, 1 minute, 0.25s, 12 backos, and 3 backos, respectively). Never-
theless, sometimes the network size Nmax determined by the hybrid protocol may
not be sucient to cover the entire monitoring area of a given application with
extreme requirements. For such situations, it is suggested to deploy a cluster of
mini-networks where data transmission within each mini-network is governed by
the proposed hybrid protocol.
Research outcomes of this chapter have been either published or under re-
viewed in [219][224]-[226].
6.6 Conclusion
The IEEE 802.15.4 MAC standard appears to have shortcomings in delivering
a reliable and energy ecient data transmission for WSNs deployed in delay
bounded hybrid monitoring applications. By addressing the shortcomings of
the standard protocol, an IEEE 802.15.4 compliant new MAC mechanism is
developed for such hybrid monitoring WSNs. The new MAC protocol deploys
two dierent medium access mechanisms, namely DTS mechanism and random-
delayed CSMA/CA mechanism, to discretely meet the unique data-transmission-
requirements associated with each monitoring application (i.e., long-term periodic
and event detection). Furthermore, the proposed protocol comprehensively ad-
dresses network administrative tasks including network initialisation, new node
association, and existing node disassociation. The new hybrid protocol is built on
an ns-2 simulation environment, and then a series of simulation based experiments
are carried out to investigate its performance. Simulation results demonstrate
6. IEEE 802.15.4 BASED MAC FOR HYBRID MONITORING WSNS 177
that the new protocol not only outperforms the standard IEEE 802.15.4 protocol
(in terms of reliability and power consumption of data transmission) but also
provides a reliable, energy ecient, and delay bounded data transmission mech-
anism for hybrid monitoring applications with dierent QoS requirements. Since
the new protocol does not alter any of the IEEE 802.15.4 PHY layer functionali-
ties, it can be easily implemented in commercial-o-the-shelf (COTS) platforms
after introducing a few software modications to the existing IEEE 802.15.4 ra-
dios.
Chapter 7
Conclusion
7.1 Summary and Conclusion
This thesis investigated performance of the IEEE 802.15.4 Medium Access Control
(MAC) standard under dierent operational conditions and application scenarios.
The major investigations and their outcomes can be summarised as follows:
• The beacon-enabled IEEE 802.15.4 MAC protocol with acknowledgment
(ACK) frame transmissions is analysed using a Discrete Time Markov Chain
(DTMC) model [150][151]. This analytical model is then used to derive the
performance of the protocol in terms of its throughput, power consumption
and data transmission reliability. Using the proposed analysis, impact of
the network and MAC-layer parameters on the performance of the protocol
is investigated [153]. The protocol's performance is further examined un-
der erroneous channel conditions by extending the proposed model [152].
Altogether, these investigations provide a generalised platform to analyse
the performance of the beacon-enabled IEEE 802.15.4 MAC protocol with
ACK frame transmission.
• Operational beahviour of the non-beacon-enabled IEEE 802.15.4 MAC pro-
tocol is studied with particular emphasis on possible contention scenarios
7. CONCLUSION 179
in data transmission. Based on a time discretisation approximation, the
non-beacon-enabled protocol is modelled using a DTMC [173][174]. The
performance of the protocol - in terms of throughput, power consumption
and data transmission reliability - is evaluated, and it is compared and
contrasted with that of the beacon-enabled protocol. The proposed model
provides a new analytical tool to evaluate the performance of the non-
beacon-enabled protocol both with and without ACK frame transmissions.
It has an added advantage of general applicability to wide range of network
congurations with dierent network and MAC-layer parameter values.
• A DTMC based analysis is proposed to investigate the performance of the
IEEE 802.15.4 MAC protocol in the presence of hidden nodes [193][194].
The proposed analysis, which models concurrent data transmissions oc-
curred in IEEE 802.15.4 based networks with hidden nodes, is used to derive
the aggregate network throughput by grouping nodes into non-overlapping
carrier sensing ranges. Using the proposed model, the impact of the various
network parameters on the aggregate throughput is examined, and then sev-
eral recommendations are discussed to improve the performance. Based on
experimental ndings, the node grouping condition of the analysis is relaxed
to propose a simple technique that approximates the throughput of generic
(i.e., without node grouping) IEEE 802.15.4 based networks with hidden
nodes. This investigation provides a basis for future studies on analysing
the inuence of hidden nodes on the performance of IEEE 802.15.4 based
networks.
• The necessity of having hybrid monitoring scenarios in WSNs is identied
in the context of multifaceted monitoring applications emerged recently
[224]. Quality of Service (QoS) requirements of data transmissions associ-
ated with dierent monitoring scenarios (in particular long term periodic
monitoring (LTPM) and event detection (ED)) are presented to show the
7. CONCLUSION 180
incapacity of the current IEEE 802.15.4 standard in such hybrid monitoring
WSNs. Two distinct MAC mechanisms - known as DTS mechanism and
randomly-delayed CSMA/CA mechanism [219] - are proposed to meet the
dierent data-transmission requirements posed by LTPM and ED applica-
tions. By carefully merging these two mechanisms, a hybrid MAC protocol
is developed on top of the existing IEEE 802.15.4 MAC standard [225][226].
The new protocol provides a reliable, energy ecient, and delay bounded
data transmission mechanism for hybrid monitoring WSNs with dierent
data-transmission requirements.
7.2 Future Work
This section provides recommendations for possible directions for future research
on the subject of this thesis.
• WSNs operate generally in hostile environments where data transmission
may frequently aect by channel errors. Therefore, a natural extension to
the study presented in Chapter 4 would be to analyse the performance of
non-beacon-enabled IEEE 802.15.4 MAC protocol under erroneous chan-
nel conditions. The error channel model developed in Chapter 3 possibly
provides a good basis for such an extension.
• Owing to the fact that buering of data frames may be required in some
WSNs, the analytical models presented in Chapter 3 and 4 could be ex-
tended to investigate the performance of the IEEE 802.15.4 MAC protocol
(both beacon-enabled and non-beacon-enabled) under the inuence of data
frame buering.
• Even in the presence of hidden nodes, MAC level acknowledgements might
provide a robust mechanism for reliable data transmission and thereby im-
prove the network throughput. Thus, investigating the throughput of IEEE
7. CONCLUSION 181
802.15.4 based networks that deployed ACK frame transmission in the pres-
ence of hidden nodes could be a possible extension to the analysis presented
in Chapter 5.
• Another potential improvement to the work presented in Chapters 4 and 5
includes developing an analytical model to evaluate the throughput of the
non-beacon-enabled IEEE 802.15.4 MAC protocol in the presence of hidden
nodes.
• The proposed hybrid protocol was developed for single-hop transmissions
in hybrid monitoring WSNs. Extending this protocol for multi-hop trans-
missions would enable large scale WSNs to meet the stingiest requirements
posed by underlying hybrid monitoring applications.
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Appendix A
Steady State Transition Equations
of Node Model DTMCs
A.1 Beacon-enabled IEEE 802.15.4 - Analytical
Model
The steady state probabilities of Markov chain model of node (Figure 3.1) can beobtained by solving the balance equations shown in (A.1.1), where w1 = 2macMinBE
and wy = 2BEy . BEy represents the backo exponent of yth backo stage. Thenotationπ(statei) represents the long term proportion of transitions into statei.The normalised condition of the DTMC is presented in (A.1.2).
π(idle) = (1− p)π(idle) + (1− pci)∑x
x=1 π(csxy0) + (1− pci|i)∑x
x=1 π(csxy(−1))
+q∑x−1
x=1 π(ackx) + π(ackx)
π(bo11z) = gpπ(idle)/w1 + hπ(bo11(z+1))
where (g, h) =
(0, 1) 1 ≤ z ≤ 2; (4, 1) z = 3
(1, 1) 4 ≤ z < w1 − 1; (1, 0) z = w1 − 1
APPENDIX 205
π(box1z) = (1− q)π(ackx−1)/w1 + hπ(box1(z+1))
where 2 ≤ x ≤ x; h =
1 1 ≤ z < w1 − 1
0 z = w1 − 1
π(boxyz) = [(1− pci)π(csx(y−1)0) + (1− pci|i)π(csx(y−1)(−1))]/wy + hπ(boxy(z+1))
where 1 ≤ x ≤ x; 2 ≤ y ≤ y; h =
1 1 ≤ z < wy − 1
0 z = wy − 1
π(csxy0) = g(1− q)π(ackx−1) + h[(1− pci)π(csx(y−1)0)
+(1− pci|i)π(csx(y−1)(−1))]/wy + π(boxy1)
where (g, h) =
(0, 0) x = 1; y = 1
(1, 0) 2 ≤ x ≤ x; y = 1
(0, 1) 1 ≤ x ≤ x; 2 ≤ y ≤ y
π(csxy(−1)) = pciπ(csxy0) where 1 ≤ x ≤ x; 1 ≤ y ≤ y
π(txx) = pci|i∑y
y=1 π(csxy(−1)) where 1 ≤ x ≤ x
π(ackx) = π(txx) where 1 ≤ x ≤ x(A.1.1)
π(idle)+x∑x=1
y∑y=1
wy−1∑z=1
π(boxyz)+x∑x=1
y∑y=1
0∑z=−1
π(csxyz)+x∑x=1
π(txx)+x∑x=1
π(ackx) = 1
(A.1.2)
APPENDIX 206
A.2 Beacon-enabled IEEE 802.15.4 - Simplied Model
The steady state probabilities and the normalisation condition of Markov chainmodel of node (Figure 3.3) can be obtained as
π(idle) = (1− p)π(idle) + (1− pci)π(csy1) + (1− pci|i)π(csy2) + qπ(ack)
π(bo1) = (1− pn1 )[pπ(idle) + π(bo1) + (1− q)π(ack)]
π(cs11) = pn1 [pπ(idle) + π(bo1) + (1− q)π(ack)]
π(cs12) = pciπ(cs11)
π(boy) = (1− pny )[(1− pci)π(cs(y−1)1) + (1− pci|i)π(cs(y−1)2) + π(boy)] ; 2 ≤ y ≤ y
π(csy1) = pny [(1− pci)π(cs(y−1)1) + (1− pci|i)π(cs(y−1)2) + π(boy)] ; 2 ≤ y ≤ y
π(csy2) = pciπ(csy1) ; 2 ≤ y ≤ y
π(ack) = π(tx)
(A.2.3)
π(idle) +
y∑y=1
π(boy) +
y∑y=1
2∑z=1
π(csyz) + π(tx) + π(ack) = 1 (A.2.4)
APPENDIX 207
A.3 Non-beacon-enabled IEEE 802.15.4 with ACK
Transmission
The steady state transition equations of the Markov chain model of node in Fig-ure 4.5 and the relevant normalisation condition can be obtained as follows. Thenotation π(statei) represents the long term proportion of transitions into statei,and w1 = 2macMinBE, wy = 2BEy where BEy represents the backo exponent ofyth backo stage.
π(idle) = (1− p)π(idle) + (1− pci)∑x
x=1 π(csxy) + q∑x−1
x=1 π(ackx) + π(ackx)
π(bo11z) = gpπ(idle)/w1 + hπ(bo11(z+1))
where (g, h) =
(0, 1) 1 ≤ z ≤ 2; (4, 1) z = 3
(1, 1) 4 ≤ z < w1 − 1; (1, 0) z = w1 − 1
π(box1z) = (1− q)π(ackx−1)/w1 + hπ(box1(z+1))
where 2 ≤ x ≤ x; h =
1 1 ≤ z < w1 − 1
0 z = w1 − 1
π(boxyz) = [(1− pci)π(csx(y−1))]/wy + hπ(boxy(z+1))
where 1 ≤ x ≤ x; 2 ≤ y ≤ y; h =
1 1 ≤ z < wy − 1
0 z = wy − 1
π(csxy) = [g(1− q)π(ackx−1) + h(1− pci)π(csx(y−1))]/wy + π(boxy1)
where (g, h) =
(0, 0) x = 1; y = 1
(1, 0) 2 ≤ x ≤ x; y = 1
(0, 1) 1 ≤ x ≤ x; 2 ≤ y ≤ y
π(tax) = pci∑y
y=1 π(csxy) where 1 ≤ x ≤ x
π(ackx) = π(txx) = π(tax) where 1 ≤ x ≤ x
Appendix B
Fractions of Time Spent by a Node
in Dierent Transceiver Activities
B.1 Analysis of Beacon-enabled IEEE 802.15.4
The fraction of time spent by a node in dierent transceiver activities in eachanalytical models are listed in Table B.2, where
T = 1 + (L− 1)x∑x=1
π(txx) + (Lack + sdack − 1)x∑x=1
π(ackx)(B.1.1)
Tsim = 1 + (L− 1)π(tx) + (Lack + sdack − 1)π(ack). (B.1.2)
Since the analytical model presented in Section 3.3 and the extended model pre-sented in Section 3.5 share the same node model, corresponding fractions of timespent by a node in these models are listed under the same column. In TableB.2, tbcn, fbcn and p represent the beacon duration in terms of backo slots, fre-quency of beacon reception and frame arrival rate per backo slot, respectively.fbcn = 1/BI where BI is the beacon interval in terms of backo slots.
APPENDIX 209
Table B.1: Fractions of time spent by a node in dierent transceiver activities inbeacon-enabled networks.
Parameter Analytical model Simplied modelExtended model
pnbcn tbcn.fbcn tbcn.fbcnpnsi 3 (fbcn + p) 3 (fbcn + p)
pnir 0.6(fbcn +
[∑xx=1
∑yy=1 π(csxy0)
]/T)
0.6(fbcn +
∑yy=1 π(csy1)/Tsim
)pni π(idle)/T π(idle)/Tsimpnbo
∑xx=1
∑yy=1
∑wy−1z=1 π(boxyz)/T
∑yy=1 π(boy)/Tsim
pncs∑x
x=1
∑yy=1
∑0z=−1 π(csxyz)/T
∑yy=1
∑2z=1 π(csyz)/Tsim
pnack (Lack + sdack)∑x
x=1 π(ackx)/T (Lack + sdack)π(ack)/Tsimpntx L
∑xx=1 π(txx)/T Lπ(tx)/Tsim
B.2 Analysis of Non-beacon-enabled IEEE 802.15.4
Table B.2 lists the fractions of time spent by a node in dierent transceiver activ-
ities in both analytical models (i.e., with and without ACKs). The parameters p,
L, and Lack represent the frame arrival rate per mini slot, data frame length and
ACK frame length in backo slots, respectively. Λ and Λack are given in (B.2.1)
and (B.2.2).
Table B.2: Fractions of time spent by a node in dierent transceiver activities innon-beacon enabled networks.
Parameter Model without ACKs Model with ACKspnsi (3× 20)p (3× 20)p
pnir 12∑y
y=1 π(csy)/Λ 12∑x
x=1
∑yy=1 π(csxy)/Λack
pnrt 12π(ta)/Λ 12∑x
x=1 π(ta)/Λack
pni π(idle)/Λ π(idle)/Λack
pnbo 20∑y
y=1
∑wy−1z=1 π(boyz)/Λ 20
∑xx=1
∑yy=1
∑wy−1z=1 π(boxyz)/Λack
pncs 8∑y
y=1 π(csy)/Λ 8∑x
x=1
∑yy=1 π(csxy)/Λack
pnack - (12 + Lack)∑x
x=1 π(ackx)/Λack
pntx (20× L)∑x
x=1 π(txx)/Λ (20× L)∑x
x=1 π(txx)/Λack
APPENDIX 210
Λ = π(idle) + 20
y∑y=1
wy−1∑z=1
π(boyz) + 8
y∑y=1
π(csy) + 12π(ta)
+20Lπ(tx). (B.2.1)
Λack = π(idle) +x∑x=1
[20
y∑y=1
wy−1∑z=1
π(boxyz) + 8
y∑y=1
π(csy) + 12π(tax)
+20Lπ(txx) + (12 + 20Lack)π(ackx)
]. (B.2.2)
Appendix C
Modications to ns-2.34 Simulator
During the course of this dissertation, several modications were introduced to
ns-2.34 simulator to model IEEE 802.15.4 based networks accurately. The mod-
ications come under two main streams:
1. Modications to CCA Procedure
2. Modications to implement the new hybrid protocol.
Above modications (including source codes) have been published on [227], and
they can be summarised as below.
C.1 Modications to CCA Procedure
According to the IEEE 802.15.4 standard, PHY layer of a node should perform
a CCA continuously for eight symbol durations upon receiving a CCA request
from the MAC layer. However, in the ns-2 simulator, CCA procedure has been
implemented as an event of a single time instant rather than an event of successive
eight symbol durations as specied in the standard. More specically, in the
current release of the simulator (i.e., ns-2.34), the PHY layer of a node performs
the CCA at the boundary of the fourth symbol duration after receiving a CCA
request, and the outcome of that CCA is informed to the MAC layer at the end
APPENDIX 212
Figure C.1: Timing of CCA procedure in dierent scenarios.
of the eighth symbol duration. Although this implementation diers largely from
the standard, it produces accurate CCA outcomes for the beacon enabled IEEE
802.15.4 networks whose frame transmissions last for integer multiples of backo
slots. In such networks, the beginning and ending of frame transmissions and
the beginning of CCAs occur only at the boundaries of backo slots. Thus, the
outcome of a CCA performed at the fourth symbol after a backo slot boundary
is similar to that of a CCA performed continuously for the rst eight symbols of
a backo slot.
Nevertheless, the current CCA implementation in ns-2 simulator fails to pro-
duce accurate CCA outcomes in the non-beacon enabled networks in which frame
transmissions may begin and end at any time instant. For example, if a node's
transmission ends during the rst four symbols or begins during the last four
symbols of another node's CCA procedure, the current implementation of CCA
indicates idle channel while in reality it should not.
APPENDIX 213
Therefore, a modication to the current CCA implementation in ns-2 sim-
ulator is proposed to generate accurate CCA outcomes in non-beacon enabled
networks. In the modied implementation, a CCA is performed in two phases:
at the middle of the rst and eighth symbol durations. If the channel is found
busy during the rst phase (i.e., in the rst symbol) the PHY layer indicates
a `busy channel' massage at the end of eighth symbol duration. Otherwise, the
PHY layer performs the second phase of CCA (i.e.,in the eighth symbol) and
reports the outcome of that phase as the nal outcome at the end of eighth sym-
bol duration. Since all transmissions in IEEE 802.15.4 networks last for more
than eight symbol durations, the modied CCA implementation produces ac-
curate CCA outcomes by sensing the channel at the beginning and the ending
of a CCA duration. The timing diagrams shown in Figure C.1 summarises the
aforementioned scenarios. The proposed modication to ns-2 simulator has been
implemented by modifying the existing CCA timer and handler (i.e, CSH and
CSHandler in ./wpan/p802_15_4phy.cc and ./wpan/p802_15_4phy.h les) to
enable the rst phase of CCA and by adding a new timer and handler (i.e, CSH2
and CSHandler2) to enable the second phase of CCA.
C.2 Implementation of Hybrid Protocol
The hybrid protocol presented in Chapter 6 is implemented in ns-2 simulator by
developing new software modules and modifying the existing ones. The modica-
tions are summerised in Table C.1. To simulate the complete protocol, the new
and modied modules are linked to all the other software modules in the ns-2
implementation of the IEEE 802.15.4 at ./ns-2.34/wpan/.
APPENDIX 214
Table C.1: Impelemetation of hybrid protocol in ns-2 .
New modules
p802_15_4sscs_hybrid_init.hp802_15_4sscs_hybrid_init.cc
Functionalities :coordinator node:
initialise the coordinator node with initialising-parameters Led, Lltpm, and m
calculate Nmax, BImin−nwkstart and handle the countdown timer (Tinit)
call p802_15_4mac.cc to set BI in MAC-PIB database to BImin−nwkand to transmit beacons for association purposes
maintain a counter to get the number of associated nodes
manage sensor nodes association at the initial phase
sensor nodes:call p802_15_4mac.cc to listen the beacons
call p802_15_4mac.cc to transmit association request
get association conrmation from p802_15_4mac.cc
call p802_15_4mac.cc to set the variable association_number
turn o the radio transceiver after a successful association, until the expiration
of countdown timer or beginning of the next superbeacon transmission
(depending on the network's operational phase)
p802_15_4sscs_dts_schedule.hp802_15_4sscs_dts_schedule.cc
Functionalities :coordinator node:
calculate the DTS schedule parameters (i.e., Tdts, ndts, and BInwk)
for a network with N nodes
initialise the DTS schedule parameters in MAC-PIB database
by calling p802_15_4mac.cc
call p802_15_4mac.cc to transmit superbeacons
start and handle a timer for superbeacon transmission
maintain a disassociation-counter for each associated node
handle node disassociation
continued on next page...
APPENDIX 215
handle association requests from the new nodes
(while the network is in the steady phase)
calculate new DTS schedule parameter values for the network with the new N
call p802_15_4mac.cc to update new DTS schedule parameter values
in MAC-PIB database after each new association or disassociation
sensor nodes:call p802_15_4mac.cc to reset all current rx-tx operations and to reset
the variable association_number, if received a disassociation conrm
call p802_15_4sscs_hybrid_init.cc to reassociate with the network
Modied modules
p802_15_4mac.hp802_15_4mac.cc
Functionalities :coordinator node:
transmit beacons (with modied payloads) and superbeacons
update the DTS schedule parameters in MAC-PIB database
sensor nodes:receive beacons (with modied payloads) and superbeacons
update the DTS schedule parameters in MAC-PIB database
start and handle a timer to locate the start of DTS in each superframe
maintain a counter to nd the associated DTS within the transmission cycle
handle data transmission within DTSs
transmit dummy frames during the associated DTSs when no data to transmit
send disassociation conrm command to p802_15_4mac.cc,
if the node's association number is found in Disassociated Nodes eld
set and reset the variable association_number
call p802_15_4_csma.cc to transmit data if the data transmission within
node's DTS is not acknowledged by the immediate beacon
call p802_15_4_csma.cc to transmit ED data
set an identier for ED data frames
continued on next page...
APPENDIX 216
p802_15_4csma.hp802_15_4csma.cc
Functionalities :sensor node:
introduce a random delay before starting the csma/ca procedure for ED data
freeze the backo procedure (including random delays) during DTSs
seize all CSMA/CA trac during the DTS of each superframe
p802_15_4timer.hp802_15_4timer.cc
Functionalities :dene timers for
- countdown timer (Tinit),
- superbeacon transmission,
- locating the start of the DTS in each superframe
p802_15_4eld.hp802_15_4paket.h
Functionalities :make changes in the payload of beacon and superbeacon frames
Appendix D
Supportive Calculations and
Algorithm Descriptions
D.1 Quantifying δ
The proportional constant δ in Equation (6.1) in Chapter 6 can be found by
matching the data transmission reliability of networks having randomly-delayed
synchronised data arrivals with that of equivalent networks having Poisson data
arrivals. To quantify δ, three dierent simulations were performed for IEEE
802.15.4 based networks with these two dierent types of frame arrivals (i.e.,
randomly-delayed synchronised arrivals and Poisson arrivals) . The rst simula-
tion was conducted for dierent network sizes ranging from 4 to 80 with a xed
frame length (L = 10), while the second simulation was performed for dierent
frame lengths with a xed network size (N = 40). The frame arrival rate λ
was xed to 1 frame/s for all networks during rst two simulations. Finally, the
third simulation considered dierent frame arrival rates with xed network size
(N = 40) and xed frame length (L = 10). For simulations, 13 dierent Drnd
values were applied for each network with randomly-delayed synchronised data by
varying δ from 0.1 to 3. Furthermore, 2.4 GHz PHY layer and default values for
APPENDIX 218
all MAC layer parameters were assumed for simulations. ACK frame transmis-
sion and frame retransmissions were deployed in each simulation to minimise the
number of frames dropped due to collisions. Each simulation trial was run until
each node transmits 20,000 frames, and results were averaged for 10 simulation
trials.
Data transmission reliability of each network set-up was evaluated using the
performance index R (reliability factor), which represents the ratio between the
number of frames successfully received by the coordinator and the total number of
frames generated by all sensor nodes. To simplify the comparison, the normalised
dierence of R (i.e., ∆R) is dened as
∆R =
[Rsynchronised −RPoisson
]× 100%
RPoisson(D.1.1)
where Rsynchronised and RPoisson denote the reliability factors of the network
with randomly-delayed synchronised data and the equivalent network with Poisson
arrivals, respectively. Figure D.1 depicts ∆R with varying δ in dierent network
set-ups.
According to simulations, ∆R ≈ 0 when δ ≥ 1.0 for all network congurations
and frame arrival rates considered1. Therefore, by applying a random delay where
δ ≥ 1.0, the data transmission reliability of the IEEE 802.15.4 based networks
with synchronised data arrivals can be improved to be matched with the reliability
of equivalent networks having Poisson data arrivals.1Note: The chosen values for L, N , and frame arrival rate almost represent the entire
possible range for these parameters in the context of the reference hybrid monitoring WSN. Forinstance, it is not possible to go beyond 5 frames/s arrival rate while satisfying D < Ted as themaximum transmission delay of the standard CSMA/CA protocol with the default settings isequal to 0.17664s (Equation 6.3)
APPENDIX 219
(a) ∆R vs δ for dierent number of nodes N (L = 10 backos, frame arrivalrate = 1 frames/s)
(b) ∆R vs δ for dierent frame lengths L, (N = 40 and frame arrival rate= 1 frames/s)
(c) ∆R vs δ for dierent frame arrival rates λ, (N = 10 and L = 10backos)
Figure D.1: Normalised dierence in data transmission reliability ∆R with vary-ing δ
APPENDIX 220
D.2 Algorithms for Networks Deployed Only in
LTPM Applications
When a sensor network deployed entirely for the long term periodic monitoring, it
can operate only with the DTS mechanism. Therefore, all the parameters related
to the DTS scheduling in such networks (i.e., Nmax−ltpm, ndts−ltpm, BInwk−ltpm,
and Tdts−ltpm) can be completely determined using the monitoring requirements
m and Trpt of the underlying LTPM application as follows:
Nmax−ltpm: At the beginning of network initialisation, the network coordinator
determines (Nmax−ltpm) to be the maximum value of Nmax that validates the
following inequality.
Nmax ≤⌊
Trptκ(BImin − tbcn)
⌋(D.2.1)
where BImin and tbcn represent the minimum possible BI value of the IEEE
802.15.4 standard1, which can be obtained by setting BO = 0, and transmission
duration of beacon frames (including Rx-to-Tx turnaround), respectively. The
coecient κ represents the number of superframes required to transmit m frames,
and it can be found as the minimum value that satises
κ ≥⌈
mtltpm(BImin − tbcn)
⌉(D.2.2)
where tltpm represents the transmission duration of single LTPM data frame in-
cluding IFS.
BInwk−ltpm and ndts : Once Nmax−ltpm is calculated, the network coordinator
starts the association process. If the number of nodes associated is equal to N ,1Selecting BImin for BI gives higher granularity in time domain which in turn yields higher
values for Nmax
APPENDIX 221
the following inequality is applied to nd ndts−ltpm, BInwk−ltpm.
mtltpm < ndts(BInwk − tbcn) <TrptN
(D.2.3)
where ndts ∈ N+ and BInwk belongs to one of the 15 possible BI values of the
IEEE 802.15.4 standard. To minimise the energy wastage on beacon listening,
BInwk−ltpm is set to the maximum value of BInwk that validates the above Inequal-
ity. Thus, ndts−ltpm is the minimum ndts that can be associated with BInwk−ltpm
in (D.2.3).
Tdts−ltpm: Finally, the duration of the DTS Tdts−ltpm can be obtained as
Tdts−ltpm =
⌈m
ndts−ltpm
⌉tltpm. (D.2.4)
It is worthwhile to notice that Tdts−ltpm < (BInwk−ltpm − tbcn). Therefore, there
exists an unoccupied duration of each superframe that can be utilised for future
communications in node-association and/or retransmission of corrupted LTPM
data.
After computing the above parameters, the network coordinator constructs
the DTS schedule and transmits the initial superbeacon to begin the steady phase.
During the steady phase, the network follows the same procedure described in
Chapter 6.
D.3 Computation of Initialising-Parameters
Initialising-parameters Led, Lltpm, and m of the hybrid protocol for the SHM
application given in Table 6.3 can be determined as follows:
Led: The payload of an ED data frame carries the strain data generated during
a sampling instance. As there are three measuring axes, this payload equals to
6 bytes. After adding PHY and MAC layer headers of 6 and 13 bytes to this
APPENDIX 222
payload, an ED data frames becomes 25 bytes in total length. Because a single
backo-slot corresponds to 10 bytes in 2.4 GHz band, the length of an ED data
frame Led can be given as 2.5 backo-slots. After rounding o, this eventually
becomes 3 backo-slots.
Lltpm: The payload of a LTPM data frame can carry multiple acceleration
measurements due to `Store now, transmit later' behaviour of the LTPM ap-
plication. Therefore, to minimise the protocol overhead associated with data
transmission, the payload of a LTPM data frame is assumed to be at its maxi-
mum possible value allowed by the IEEE 802.15.4 standard (i.e., 104 bytes). By
adding PHY/MAC layer headers and then performing a basic rounding o, the
length of a LTPM data frame Lltpm nally becomes 12 backo-slots.
m: The total amount of LTPM data generated during a reporting period Θ
can be given as
Θ = fs × Tm × naxes × ls (D.3.5)
where fs, Tm, naxes, and ls represent the sampling frequency, measuring period,
number of measuring axes, and sample size of the LTPM application, respectively.
By substituting the relevant values from Table 6.3 into D.3.5, Θ can be found
as 252000 bytes. After dividing this amount of data by the payload of a single
LTPM data frame (i.e., 104 bytes) and then performing necessary rounding o,
m would become 2400 frames.