ANALYSIS &DESIGN OF JOINT PHY-MAC MODEL OF IEEE 802.15.4

ISSN: 2278 – 7798

International Journal of Science, Engineering and Technology Research (IJSETR)

Volume 2, Issue 9, September 2013

1706 www.ijsetr.org

ANALYSIS &DESIGN OF JOINT PHY-MAC

MODEL OF IEEE 802.15.4

Vishwanath Bijalwan1, Dr.Sanjay Singh

2 and Ashish Kumar

3

1,2Department of ECE, Uttaranchal University, Dehradun

3Department of EEE, DIET ,Rishikesh

ABSTRACT-In Recent years various researches were focused on wireless sensor network (WSN) .IEEE 802.15.4 is the best solution for low-rate, low power, energy efficient wireless

sensor networks application especially in industrial and home automation. IEEE 802.15.4 is widely used in ubiquitous networking among different wireless standard. The IEEE 802.15.4 specifies PHY and MAC (media access control) layer for LR-WPAN. IEEE 802.15.4 is widely employed in ubiquitous networking of wireless devices. We review and analyze a joint model of PHY & MAC layer of IEEE 802.15.4[4]. In this paper we present a model of Physical layer of IEEE 802.15.4, it includes path loss model, and this

interacts with MAC layer. Our model focus on MAC layer in which multiple non saturated sources/stations try to access channel and communicate with sink. Model is based on Markov chain, and M/M/1/k queuing model. Our analysis at PHY layer performs calculation and find out the probability of good frame reception (SNR) and radio (modulation and coding) parameters. Analysis is more focused on considering packet loss at PHY and MAC levels with concept of

transitional region approach [2]. Our analysis also deals with PHY layer implementation of IEEE 802.15.4 using MATLAB/Simulink

General Terms-PHY layer including path loss model of IEEE 802.15.4, CSMA-CA protocol

Keywords-IEEE 802.15.4,Markov chain modeling, Queuing

model, MATLAB model of IEEE 802.15.4, PHY & MAC layer.

I. INTRODUCTION

IEEE 802.15.4 is used to convey information over a short distance, requires no infrastructure and this feature

allows this to small, power efficient, and inexpensive

solution for a wide range of devices.LR-WPAN[10]

devices consists a PHY, which contains the RF

frequency transceiver with its low level control

mechanism, and MAC layer which provide access to

the physical channel for all types of data transfer. PHY

layer provides two types of services: The PHY data

service and PHY management service interfacing to

PLME-SAP. Transmission and reception of PHY

protocol data units (PPDU) is done by PHY data

service. Activation and deactivation of radio

transceiver, energy detection, Link quality indication,

channel selection, clear channel assessment (CCA),

transmitting and receiving packets are done by the PHY

layer. Whereas MAC sublayer provides two services : The MAC data service, which allows the transmission

and reception of MAC protocol data units (MPDUs)

across the PHY data service, and MAC management

service interfacing to (MLME-SAP). Beacon

management, channel access, GTS management, frame

validation, acknowledged frame delivery, association

and disassociation are the functions of MAC sublayer.

In this paper we present a MATLAB based model of

IEEE 802.15.4 in which all the parameters is to be

considered and shows the interaction between PHY and

MAC layer. “Transitional area” [2] approach is used in our analysis. Model is completely based on approach of

Zuniga and krishnamachari [3].We shows a general

MATLAB simulation model of PHY layer of IEEE

802.15.4 and implement a MATLAB code of PHY

layer that calculates good frame reception,and

modulation and coding parameters.

The paper is organized as following the first section

covering the introducing second and third section

describing the details of requirement and possible

benefits for the new designed MAC layer protocol for

energy efficiency subsequent we presented the model

based on IEEE802.15.4 model and addressed the issue of energy requirement. The last section of paper is

result and conclusion.

II. PHYSICAL LAYER OF IEEE

802.15.4

MATLAB based Simulink model is presented in our

work. Fig1. is transmitter part of IEEE 802.15.4. In

fig.2 receiver part is shown. Standard parameters

according to IEEE 802.15.4 are taken for designing of

Transceiver sections.

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Fig 1. Transmitter of IEEE 802.15.4

Actual data transfer takes place in physical layer that’s

why we concerned over PHY layer. In our analysis we

use, 2-ary technique, with initial seed 37 and sample

time 0.000004 we generate a random data, using

random data integer block, which is input data to

transmitter. Transmitter block is shown above in fig.1.

Data is modulated by using offset quadrature phase shift

keying technique. The whole system uses spread

spectrum technique for efficient data transmission, and

for security enhancement. AWGN channel is taken for

data transfer and receiver section is shown in fig.2.

,whole model shown in fig.3 given below.

Fig. 2 Receiver of IEEE 802.15.4

Fig.3 PHY layer of IEEE 802.15.4 using Simulink

PHY layer of IEEE 802.15.4 consists the following

processes 2.1.1 Data rate should of IEEE

802.15.4(2450) MHz should be 250 kbps. 2.1.2 2.45

GHz PHY employs a 16 ary quassi orthogonal

modulation technique. During each symbol period, 4

information bits are used for selecting one out of 16

nearly orthogonal PN sequence for transmission. Final

chip sequence is modulated on to the carrier using

OQPSK technique.

Fig.4 Modulation and spreading functions

2.1.3 Binary data contained in the PPDU is encoded

using modulation and spreading, and the four LSBs (b0,

b1, b2, b3) is mapped in to one data symbol and 4

MSBs(b4, b5, b6, b7) of each octet is mapped in to next

data symbol. Each octet of PPDU is processed in

accordance with fig.4 sequentially.

2.1.4 Each data symbol is mapped in to 32-chip PN

sequence according to a specified table. PN sequence

are related each other in accordance to cyclic

shift/conjugations. This process is known as symbol to

chip mapping.

2.1.5 Finally chip sequence representing each data

symbol is modulated with OQPSK technique using half

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pulse shaping. Using OQPSK technique, even-indexed

chips are modulated onto the in-phase (I) carrier and

odd-indexed chips on quadrature phase (Q). Nominally

chip rate is 2.0 Chips/s is 32 times the symbol rate,

because each data symbol is represented by 32-chip

sequence.

2.1.6 Transmitted power spectral density (PSD) should

have certain limits according to fig.5

Frequency Relative limits Absolute limits

f-fc > 3.5

MHz

-20 dB -30dBm

Fig.5 Transmit PSD limits

IEEE 802.15.4 on 2450 MHz PHY shall have symbol

rate 62.5 ksymbol/s and receiver sensitivity of -85dBm

or better.

III. PHY-MAC MODEL BASED ON

MARKOV MODEL AND QUEING

THEORY

The main theme behind to model PHY layer is based

upon Zuniga and Krishnamachari approach [3]. Our

approach is to identify cause of transitional region [2]

and their impact on performance, assuming static

interference/light traffic. For this, packet reception rate

as a function of distance is to be calculated. These

expressions based on lognormal path loss model,

channel shadowing variance, the modulation and coding

parameters.

3.1 Operation Details

The model is based on markov chain, which captures

the state of a station’s backoff stage, state of

retransmission counter and backoff counter. M/M/1/k

queuing theory is also used for model; it captures the

state of finite buffer in the station. Queuing model gives

throughput, and probability that station is idle

considering Markov chain outputs, while Markov chain

determines the steady state probability that station

senses the channel to transmit a frame and the

probability that a frame is in failure.

3.2 The Markov Chain Model

N stations want to communicate with sink.

The Markov chain is detailed in [8]. The probability

that a node tries a first carrier sensing to transmit a

frame, the probability that channel is busy during CCA1

and the probability that channel is busy during CCA2 is

denoted by , and β. These three probabilities are

related by a system of three nonlinear equations that

arises from finding the steady state probabilities of a

variant of the IEEE 802.15.4 markov chain. When a

node wants to send a frame, its backoff counter is

already 0 and it is not idle, that is why (1-Po) is taken

instead of that a node tries first carrier sensing to

transmit a frame. Po is the probability that station is

idle. The system considered is given by following

equations:

(3.2.1)

(3.2.2)

(3.2.3)

Where:

m= macMaxCSMABackoffs

n=macMaxFrameRetries

L is the length of data frame in slots.

is the length of acknowledgement in

slots.

is the number of neighbouring stations.

is the state where the state variables of

backoff stage counter, backoff counter and

retransmission counter are equal to 0[10 ].

The probability of collision , probability of loss

due to channel and radio setups and probability of

failed transmission is to be calculated using by

using above mechanism. In our analysis the losses is

due to low SNR and by errors due to modulation and

coding. Using these constraints a new approach of

transitional region [3] is to be considered for probability

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of good frame reception, for a given distance between

two nodes. Fig.6 is plotted between packet reception

rate versus the distance (meters). Three different areas

are shown in figure.

Probability of packet reception is 1(connected

area).

Probability of packet reception 0 (disconnected

area)

Probability of packet reception is between 0

and 1(transitional area).

The transitional is significant in size, and is generally

characterized by high-variance in reception rates and

asymmetric connectivity.

Fig.6 packet reception rate versus distance

(meters)

Probability of failure is given by expression

(3.2.4)

Where

(3.2.5)

is the probability of loss due to channel and radio

parameters (computed by the PHY model) and is

the probability of collision. Solving the system of

nonlinear equations to determine , , and

therefore . Then , and are used to determine

the mean MAC service time, or the mean time to

process a frame, also known as Expected time. A new

value of is calculated, in detail [11][15]. Every node

is modeled according to M/M/1/k queue and follows

poisson arrival process to receive a frame frames/s.

Queue utilization

(3.2.6)

Probability that there are i frames in the queue is

(3.2.7)

hence, value of :

(3.2.8)

The process continues until the value of converges to

a stable value. Once, taken stable value, all outputs

concerning queuing analysis can be evaluated for each

value of (per-node offered load). We take four outputs

for our study: the average wait time to receive a frame

(Eq.3.2.9), the failure probability (Eq.3.2.4), the

reliability of a node (the probability of good frame

reception) (Eq.3.2.10) and average throughput per node

(Eq.3.2.11).

(3.2.9)

(3.2.10)

(3.2.11)

is the probability of having full buffer, is the

probability that the frame is discarded due to channel

access failure, and is the probability that the packet

is discarded due to retry limits. L is the payload size and

is the application data size. Thus the complete

process of analysis can be summarized as

Give the PHY inputs and calculate Pe.

Give the MAC inputs and solving nonlinear

system to calculate , , and β.

Computing using and .

Computing using and Pe.

Computing Expected time using , ,and β.

Computing new Po using ET and .

Now a threshold value is to be checked, if new

po-old Po < ε (convergence) then computing

outputs.

Using above parameters with some specific values of

physical layer and Mac layer we made our analysis and

the complete process of simulation is summarized

below in chart form.

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A complete process can be summarized by a flow chart.

IV. OUTPUT AND RESULTS

Simulation is done under some input parameters used in

MAC and PHY layers. As shown in Table1 and Table

2, we take per-node offered load equal to 0.5frames/s

and increment it by 0.5 until it reaches to load of

50frames/s. Thus in fig 7, 8,9,10 and in 11 all the

outputs were shown. Important feature is concluded that

IEEE 802.15.4 network does not support heavy traffic.

Fig.7 Average per node throughput versus offered

load

Fig.8 Average per-node wait time versus per-node

offered load.

PHY INPUTS

MAC INPUTS

SOLVING NON

LINEAR

SYSTEMS:

COMPUTING

, and β

Computing

Computing using

and

Computing using

and

Computing ET using

, and β

Computing new

using ET and

Computing Outputs

New -Old < ε

convergence)

New -Old > ε

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Table 1: parameters used in MAC Layer

Parameters Values

N_stations 10

K 51 frames

data_rate 19.2kbits/s

W0 8

macMaxFrameRetries 3

macMacCSMAbackoffs 4

macMinBE 5

macMaxBE 3

L_application 800 bits

L_overhead 48 bits

L_ACK 88 bits

T_prop 222e-9 seconds

aUnitbackoffperiod 320e-6 seconds

L0 0

A 80 bits/slot

T_IFS 640e-6 seconds

macACKWaitDuration 1920e-6 seconds

aTurnaroundTime 192e-6 seconds

sensing_time 128e-6 seconds

Sigma_s 4

P0_tolerance e-10

Using input parameters both of PHY and MAC, The evolution of the average wait time[12], the

reliability, the average throughput, alpha & beta

probabilities, and instantaneous throughput versus the

offered load are represented for 10 nodes.

Table 2: Parameters values used at PHY layer

NOISE_FIGURE 23 dB

BW 30kHz

PATH_LOSS_EXPONENT 4

SHADOWING_STANDARD_DEVIATION 4

D0 1 meter

Prdbm 5 dB

NOISE 15 dB

Lambda 12.5 e-2 meters

DATA_RATE 19.2kbits/s

PREAMBLE_LENGTH 40 bits

FRAME_LENGTH 808 bits

Distmin 1 meter

Distmax 20 meter

Fig.9 Per-node reliability versus per-node offered

load

Fig. 10 Instantaneous per-node throughput versus

per-node offered load

Fig. 11 Alpha & beta probabilities versus offered

load

V. CONCLUSION

We have analyzed IEEE 802.15.4 functionalities at the

PHY and the MAC layers. Transitional region approach

is best simulated in our modeling. Modeling of

constraints that affect link quality viz. distance, output

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power, noise, asymmetry, and modulation and encoding

errors. PHY model expresses the link unreliability and

output for estimating the probability of transmitting

frame failure. MAC-PHY joint modeling better estimate

the wireless network parameters. Methodology involves

Markov chain, and M/M/1/k queuing theory. Also

MATLAB based modeling and implementation of PHY

layer of IEEE 802.15.4 provides better understanding of

PHY layer [16][17] parameters. Output shows more

packet loss when we consider the joint model (PHY-

MAC) instead of earlier approach, when packet loss is

considered only due to MAC parameters, so analysis is

more accurate.

VI. ACKNOWLEDGEMENTS

This research work is done under author’s Ph.D.

research. The authors like to express sincere gratitude to

Mr. Jitender joshi(Chancellor Uttaranchal University),

Dr. S.C Joshi ( Vice Chancellor, Uttaranchal

University ) ,Dr. Vijay Raj, Dr. Govind Prassad,, for

their kind help and support. We also extend our

heartfelt thanks to our well wishers and unmentioned

name. No amount of thanks can pay the debt of any

one.

Address for Correspondence:

Vishwanath Bijalawan Assistant professor, Department

of ECE, Uttaranchal University, Dehradun

VII. REFERENCES

[1] P. Park, P. Di Marco, P. Soldati, C. Fischione, and K. H.

Johansson, “A generalized Markov chain model for effective analysis

of slotted IEEE802.15.4,” in 2009 IEEE 6th International Conference

on Mobile Adhoc and Sensor Systems, pp. 130–139, IEEE, Oct. 2009.

[2] Zuniga, M. and Krishnamachari, B., “Analyzing the transitional

region in low power wireless links,” In Proceedings of the IEEE 1st

Annual Conference on Sensor and Adhoc Communications and

Networks (SECON), pp. 517–526, 2004.

[3] Zuniga, M., and Krishnamachari, B., “An analysis of unreliability

and asymmetry in lowpower wireless links,” ACM Trans. Sensor

Netw., 2007

[4] IEEE Std 802.15.4-2996, September, Part 15.4: Wireless Medium

Access Control (MAC) and Physical Layer (PHY) Specifications for

Low-Rate Wireless Personal Area Networks (WPANs), IEEE, 2006

[5] Park, P., Di Marco, P., Soldati, P., Fischione, C., and Johansson,

K.H., “A Generalized Markov Chain Model for Effective Analysis of

Slotted IEEE 802.15.4”, Mobile Adhoc and Sensor Systems, IEEE 6th

International Conference,” Macau, pp. 130-139, 2009.

[6] SGIP NIST Smart Grid Collaboration Site, PAP02: Wireless

Communications for the Smart Grid (6.1.5), Call for Input to Task6,

2010 [online]. Available from:

http://collaborate.nist.gov/twikisggrid/bin/view/SmartGrid/PAP02Wir

eless#Call_for_Input_to_Task_6.

[7] T. S. Rappaport, Wireless Communications: Principles and

Practice (2nd

Edition). Prentice Hall, 2002.

[8] M. Z. n. Zamalloa and B. Krishnamachari, “An analysis of

unreliability and asymmetry in low-power wireless links,” ACM

Transactions on Sensor Networks, vol. 3, pp. 7–es, June 2007.

[9] Neha Gandotra, Vishwanath bijalwan, “Coexistence model of

zigbee & IEEE 802.11b (WLAN) in ubiquitous network

environment” IJARCET volume 1, issue 4, june2012.

[10] Vijay Bhaskar Semwal , Vinay Bhaskar Semwal , Meenakshi

Sati and Dr.Shirshu Verma “Accurate location estimation of moving

object In Wireless Sensor network” International Journal of

Interactive Multimedia and Artificial Intelligence volume 1, issue 4 ,

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[11] Kumar, K. Susheel, Vijay Bhaskar Semwal, and R. C. Tripathi

"Real time face recognition using adaboost improved fast PCA

algorithm." IJAIA Vol.2, No.3, July 2011.

[12] Kulkarni U. V.& Shinde S. V."Fuzzy Classifier Based on

Feature-wise Membership Given by Using Artificial Neural Network"

IEEE 2013.

[13] Pradheepkumar Singravelu and Shekhar Verma, “Viability of

MPKC Scheme for WSN,” InternationalConference for Internet

Technology & Secured Transactions, London, Nov.2010.

[14]S.Pradheepkumar, V.Vijayalakshmi and G.Zayaraz,

“Implementation of Pseudo-Random Route-Driven ECDH Scheme

for HSN", special issue of IJCNIS, April 2010.

[15] Purnendu Shekhar Pandey, and K. S. Verma. "Contemporary

challenges and comprehensive modifications in existing mobile

IPv6." International Journal of Engineering, Science and

Technology 4.1 (2012): 129-134.

[16] Mr Purnendu Shekhar Pandey, and Neelendra Badal. "Viable

Modifications to Improve Handover Latency in MIPv6." International

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[17] Semwal V.B., Vikas V., Chakraborty P. & Nandi G.C (2013).

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(Premi2013).

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Vishwanath Bijalwan is working as

assistance professor at Uttarnachal

university Dehradun. He is person with lot

of potential. His research interest is

wireless sensor network, WiMax Wi-Fi,

bipedal robot, machine learning &

intelligent controller. He obtained B.Tech.

and M.Tech from DIT Dehradun.. So far he

has published 4 high quality research

paper and work on many MHRD funded project. He is carrying 5year

of research experience.

Dr. Sanjay Singh, PhD, M.tech,

MBA, received his Doctorate in

(Wireless sensor network) in

2012.He started his career as a

Testing & Quality control

Engineer in Optical fiber Industry.

After working many years in

industry, he entered the teaching profession.He is a member of IEEE,

IAENG, and UACEE. His area of interest is Wireless sensor network;

Digital Image processing, Optical fiber communication and

microcontrollers etc. He has published more than thirty research

papers in national & international reputed journals.and also attended

seminars, workshops and presented research papers in many

conferences. He is working as HOD ECE and Dean Academics at

Uttaranchal University.

Ashish kumar working as a Assistant professor at

DIET rishikesh in Electrical Department. His area

of interest is Neural network, Power system

analysis, and energy efficient wireless adhoc

network.

ANALYSIS &DESIGN OF JOINT PHY-MAC MODEL OF IEEE 802.15.4

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