Mobility Modeling for Efficient Data Routing in Wireless ... · Raza, N. Javaid, “DREEM-ME:...

Mobility Modeling for Efficient Data Routing in

Wireless Body Area Networks

By

Mr. Muhammad Moid Sandhu

CIIT/FA12-REE-049/ISB

MS Thesis

In

Electrical Engineering

COMSATS Institute of Information Technology

Islamabad – Pakistan

Fall, 2014

ii

Mobility Modeling for Efficient Data Routing in Wireless

Body Area Networks

A Thesis Presented to

COMSATS Institute of Information Technology, Islamabad

In partial fulfilment

of the requirement for the degree of

MS (Electrical Engineering)

By



Fall, 2014

iii


Body Area Networks

A Graduate Thesis submitted to Department of Electrical Engineering as partial

fulfilment of the requirement for the award of Degree of M.S (Electrical Engineering).

Name

Registration Number

Mr. Muhammad Moid

Sandhu


Supervisor: Dr. Nadeem Javaid,

Assistant Professor,

Center for Advanced Studies in Telecommunications (CAST),

COMSATS Institute of Information Technology (CIIT),

Islamabad Campus,

October, 2014.

iv

Final Approval

This thesis titled


Body Area Networks

By



Has been approved

For the COMSATS Institute of Information Technology, Islamabad

External Examiner: __________________________________ Dr. Muhammad Sher

Dean, Faculty of Basic and Applied Sciences,

IIU, Islamabad

Supervisor: ____________________________________________

Dr. Nadeem Javaid

Assistant Professor, Center for Advanced Studies in Telecommunications

(CAST),

CIIT, Islamabad

HoD:___________________________________________________

Dr. Shahid A. Khan

Professor, Department of Electrical Engineering, CIIT, Islamabad

v

Declaration

I Mr. Muhammad Moid Sandhu, CIIT/FA12-REE-049/ISB herebyxdeclare that I

havexproduced the work presented inxthis thesis, duringxthe scheduledxperiod of

study. I also declare that I havexnot taken anyxmaterial from anyxsource

exceptxreferred toxwherever due that amountxof plagiarism isxwithin

acceptablexrange. If a violationxof HEC rulesxon research hasxoccurred in

thisxthesis, I shall be liablexto punishablexaction under the plagiarismxrules of

the HEC.

Signature of the student:

Date: ________________

________________



vi

Certificate

It is certified that Mr. Muhammad Moid Sandhu, CIIT/FA12-REE-049/ISB has

carried out all the work related to this thesis under my supervision at the

Department of Electrical Engineering, COMSATS Institute of Information

Technology, Islamabad and the work fulfills the requirements for the award of the

MS degree.

Date: _________________

Supervisor:

____________________________ Dr. Nadeem Javaid

Assistant Professor

Head of Department:

____________________________

Dr. Shahid A. Khan

Professor, Department of Electrical Engineering,

vii

This thesis is dedicated to my parents. For their endless love, support and encouragement.

viii

ACKNOWLEDGMENT

Foremost, I would like to express my sincere gratitude to my supervisor Dr. Nadeem Javaid for

the continuous support of my MS thesis and research, for his patience, motivation, enthusiasm,

and immense knowledge. His guidance helped me in all the time of research and writing of this

thesis. Besides my supervisor, I would like to thank the rest of my thesis committee members for their

encouragement and insightful comments. My sincere thanks also goes to other faculty members of department of electrical engineering for

their continuous support and guidance.

Last but not the least, I would like to thank my family: my parents, for giving me love and

supporting me spiritually throughout my life.



ix

ABSTRACT

Mobility Modeling for Efficient Data Routing in Wireless Body Area

Networks

In recent years, Wireless Body Area Networks (WBANs) have achieved significant attention due

to their potential applications in health care. In these networks, mobility models of human body

and routing protocols largely affect the network lifetime. In this thesis, our main contribution is

the proposition of a mobility model for the analysis of mobile human body while the other

contributions are three proposed energy efficient routing protocols for WBANs. Mobility models

play significant role in analysis of WBANs as they provide information about the distance

between node and sink at any time instant. The distance between node and sink affects energy

consumption, delay and path loss. In subject to more realistic scenarios, we propose

mathematical models for five different postures; standing, sitting, walking, running, and laying.

Nodes have different movement pattern in all of these postures. Now coming towards the first

proposed routing protocol; Forwarding data Energy Efficiently with Load balancing (FEEL), in

which a forwarder node is selected which reduces the transmission distance between node and

sink, thereby reducing the energy consumption of nodes. In order to minimize propagation delay,

Electro Cardio Graphy (ECG) and glucose level measuring nodes directly send their data to the

sink. FEEL protocol is applicable for continuous monitoring of patients. However, continuous

monitoring of patients is unnecessary in some applications like, temperature monitoring, etc. So,

we also propose Reliable Energy Efficient Critical data routing (REEC) for critical data

transmission in WBANs. In REEC, two forwarder nodes are selected on the basis of cost

function and are used for relaying the data towards sink. In order to overcome the unbalanced

load problem on forwarder nodes, the selection of forwarder nodes is rotated in each round. We

also propose a novel routing protocol for Balanced Energy Consumption (BEC) and enhancing

the network lifetime in WBANs. In BEC, relay nodes are selected based on a cost function. The

nodes send their data to their nearest relay nodes to route it to the sink. Furthermore, the nodes

send only critical data when their energy becomes less than a specific threshold. In order to

distribute the load uniformly, relay nodes are rotated in each round based on a cost function.

Simulations show improved results of our proposed protocols as compared to the selected

existing protocols in terms of stability period, network lifetime and throughput.

x

LIST OF PUBLICATIONS

1. M. M. Sandhu, N. Javaid, M. Jamil, Z. A. Khan, M. Imran, M. Ilahi, M. A. Khan,

“Modeling Mobility and Psychological Stress based Human Postural Changes in Wireless

Body Area Networks”, Computers in Human Behavior, DOI: 10.1016/j.chb.2014.09.032,

2014.

2. S. Ahmed, M. M. Sandhu, N. Amjad, A. Haider, M. Akbar, A. Ahmad, Z. A. Khan, U.

Qasim, N. Javaid, “iMOD LEACH: improved MODified LEACH Protocol for Wireless

Sensor Networks”, Journal of Basic and Applied Scientific Research, 3(10)25-32, 2013.

3. A. Haider, M. M. Sandhu, N. Amjad, S. H. Ahmed, M. J. Ashraf, A. Ahmed, Z. A. Khan,

U. Qasim, N. Javaid, “REECH-ME: Regional Energy Efficient Cluster Heads based on

Maximum Energy Routing Protocol with Sink Mobility in WSNs”, Journal of Basic and

Applied Scientific Research, 4(1)200-216, 2014.

4. N. Amjad, M. M. Sandhu, S. H. Ahmed, M. J. Ashraf, A. A. Awan, U. Qasim, Z. A.

Khan, M. A. Raza, N. Javaid, “DREEM-ME: Distributed Regional Energy Efficient

Multi hop Routing Protocol based on Maximum Energy with Mobile Sink in WSNs”,

Journal of Basic and Applied Scientific Research, 4(1)289-306, 2014.

5. M. M. Sandhu, N. Javaid, M. Akbar, F. Najeeb, U. Qasim, Z. A. Khan, “FEEL:

Forwarding Data Energy Efficiently with Load Balancing in Wireless Body Area

Networks”, The 28th

IEEE International Conference on Advanced Information

Networking and Applications (AINA-2014), Victoria, Canada.

6. M. M. Sandhu, M. Akbar, M. Behzad, N. Javaid, Z. A. Khan, U. Qasim, “REEC:

Reliable Energy Efficient Critical data routing in wireless body area networks”, The 9th

International Conference on Broadband and Wireless Computing, Communication and

Applications (BWCCA 2014), Guangzhou, China.

7. M. M. Sandhu, M. Akbar, M. Behzad, N. Javaid, Z. A. Khan, U. Qasim, “Mobility Model

for WBANs”, The 9th

International Conference on Broadband and Wireless Computing,

Communication and Applications (BWCCA 2014), Guangzhou, China.

8. Mohsin Raza Jafri, Muhammad Moid Sandhu, Kamran Latif, Zahoor Ali khan, Ansar Ul

Haque Yasar, Nadeem Javaid, “Towards Delay-Sensitive Routing in Underwater

Wireless Sensor Networks”, The 5th

International Conference on Emerging Ubiquitous

Systems and Pervasive Networks (EUSPN-2014), Halifax, Nova Scotia, Canada.

9. Ashfaq Ahmad, Muhammad Babar Rasheed, Muhammad Moid Sandhu, Zahoor Ali

Khan, Ansar Ul Haque Yasar, Nadeem Javaid, “Hop Adjusted Multi-chain Routing for

Energy Efficiency in Wireless Sensor Networks”, The 5th

International Conference on

Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2014), Halifax, Nova

Scotia, Canada.

TABLE OF CONTENTS

1 Introduction 1

2 Related Work and Background 5

2.1 Mobility-supporting Adaptive Threshold-based Thermal-aware Energy-

efficient Multi-hop ProTocol (M-ATTEMPT) for WBANs . . . . . . 10

2.2 Stable Increased-throughput Multi-hop Protocol for Link Efficiency

(SIMPLE) in WBANs . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3 On Increasing Network Lifetime (OINL) in body area sensor net-

works using global routing with energy consumption balancing . . . 12

3 FEEL: Forwarding Data Energy Efficiently with Load Balancing

in Wireless Body Area Networks 14

3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2 Radio Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.3 FEEL: Proposed Protocol . . . . . . . . . . . . . . . . . . . . . . . 16

3.3.1 Deployment of Nodes . . . . . . . . . . . . . . . . . . . . . . 17

3.3.2 Start-up Phase . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.3.3 Selection of Forwarder Node . . . . . . . . . . . . . . . . . . 18

3.3.4 Scheduling Phase . . . . . . . . . . . . . . . . . . . . . . . . 18

3.3.5 Data Transmission Phase . . . . . . . . . . . . . . . . . . . . 18

3.4 Energy Consumption Analysis . . . . . . . . . . . . . . . . . . . . . 19

3.5 Simulation Results and Analysis . . . . . . . . . . . . . . . . . . . . 19

3.5.1 Network Lifetime . . . . . . . . . . . . . . . . . . . . . . . . 20

3.5.2 Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.5.3 Residual Energy . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.5.4 Path Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4 REEC: Reliable Energy Efficient Critical data routing in Wire-

less Body Area Networks 27

4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.2 Radio Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.3 REEC: Proposed Protocol . . . . . . . . . . . . . . . . . . . . . . . 29

xi

4.3.1 Deployment of Nodes . . . . . . . . . . . . . . . . . . . . . . 29

4.3.2 Start-up Phase . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.3.3 Forwarders’ Selection Phase . . . . . . . . . . . . . . . . . . 30




4.5 Experiments and Discussions . . . . . . . . . . . . . . . . . . . . . . 33

4.5.1 Stability Period and Network Lifetime . . . . . . . . . . . . 34

4.5.2 Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.5.3 Residual Energy . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.5.4 Path Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5 BEC: A Novel Routing Protocol for Balanced Energy Con-

sumption in Wireless Body Area Networks 38

5.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.2 Analysis of Energy Consumption . . . . . . . . . . . . . . . . . . . 39

5.3 BEC: The Proposed Protocol . . . . . . . . . . . . . . . . . . . . . 40

5.3.1 Radio Model . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5.3.2 Placement of Nodes . . . . . . . . . . . . . . . . . . . . . . . 41

5.3.3 Start-up Phase . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.3.4 Routing Phase . . . . . . . . . . . . . . . . . . . . . . . . . 42



5.4 Experiments and Discussions . . . . . . . . . . . . . . . . . . . . . . 43

5.4.1 Stability Period and Network Lifetime . . . . . . . . . . . . 44

5.4.2 Network Throughput . . . . . . . . . . . . . . . . . . . . . . 44

5.4.3 Residual Energy . . . . . . . . . . . . . . . . . . . . . . . . . 45

5.4.4 Path Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

6 Mobility Modeling for Wireless Body Area Networks 48

6.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

6.2 Mobility Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

6.2.1 Standing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.2.2 Sitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.2.3 Walking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

6.2.4 Running . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

6.2.5 Laying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

6.3 Impact of Mobility in WBANs . . . . . . . . . . . . . . . . . . . . . 58

6.3.1 Energy Consumption . . . . . . . . . . . . . . . . . . . . . . 58

xii

6.3.2 Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

6.3.3 Path loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

6.4 Implementation of Mobility Model in the Routing Protocols . . . . 60


6.6 Multi-hop Technique . . . . . . . . . . . . . . . . . . . . . . . . . . 61

6.7 Data Transmission using Forwarder Nodes . . . . . . . . . . . . . . 61

6.7.1 Initialization phase . . . . . . . . . . . . . . . . . . . . . . . 62

6.7.2 Forwarders’ selection phase . . . . . . . . . . . . . . . . . . 62

6.7.3 Scheduling phase . . . . . . . . . . . . . . . . . . . . . . . . 64

6.7.4 Data transmission phase . . . . . . . . . . . . . . . . . . . . 64

6.8 Simulation Results and Analysis . . . . . . . . . . . . . . . . . . . . 64

6.8.1 Network lifetime . . . . . . . . . . . . . . . . . . . . . . . . 64

6.8.2 Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

6.8.3 Residual energy . . . . . . . . . . . . . . . . . . . . . . . . . 66

6.8.4 Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

6.8.5 Path loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

6.8.6 Energy consumption . . . . . . . . . . . . . . . . . . . . . . 69

7 Conclusion and Future Work 73

8 References 75

9 List of Publications 83

xiii

LIST OF FIGURES

1.1 Components of a sensor node . . . . . . . . . . . . . . . . . . . . . 2

3.1 Deployment of nodes on the human body in FEEL . . . . . . . . . 15

3.2 Contents of HELLO message in FEEL . . . . . . . . . . . . . . . . 17

3.3 Comparison of stability period and network lifetime for case− 1 . . 21

3.4 Comparison of stability period and network lifetime for case− 2 . . 22

3.5 Comparison of network throughput (aggregated) for case− 1 . . . . 23

3.6 Comparison of network throughput (aggregated) for case− 2 . . . . 24

3.7 Comparison of residual energy for case− 1 . . . . . . . . . . . . . . 24

3.8 Comparison of residual energy for case− 2 . . . . . . . . . . . . . . 25

3.9 Comparison of path loss for case− 1 . . . . . . . . . . . . . . . . . 25

3.10 Comparison of path loss for case− 2 . . . . . . . . . . . . . . . . . 26

4.1 Deployment of nodes on the human body in REEC . . . . . . . . . 30

4.2 Flowchart of REEC . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.3 Comparison of stability period and network lifetime in REEC, SIM-

PLE and M-ATTEMPT . . . . . . . . . . . . . . . . . . . . . . . . 34

4.4 Comparison of network throughput (aggregated) in REEC, SIM-

PLE and M-ATTEMPT . . . . . . . . . . . . . . . . . . . . . . . . 35

4.5 Comparison of residual energy in REEC, SIMPLE and M-ATTEMPT 36

4.6 Comparison of path loss in REEC, SIMPLE and M-ATTEMPT . . 37

5.1 Placement of nodes on the human body and mechanism for path

selection in OINL and BEC . . . . . . . . . . . . . . . . . . . . . . 41

5.2 Format of the HELLO packet in BEC . . . . . . . . . . . . . . . . . 42

5.3 Comparison of stability period and network lifetime in BEC and

OINL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.4 Comparison of network throughput in BEC and OINL . . . . . . . 45

5.5 Comparison of residual energy in BEC and OINL . . . . . . . . . . 46

5.6 Comparison of path loss in BEC and OINL . . . . . . . . . . . . . . 47

6.1 Markov model for posture pattern selection . . . . . . . . . . . . . . 50

xiv

6.2 Human body in sitting position . . . . . . . . . . . . . . . . . . . . 52

6.3 Human body in walking position . . . . . . . . . . . . . . . . . . . 54

6.4 Human body in running position . . . . . . . . . . . . . . . . . . . 56

6.5 Human body in laying position . . . . . . . . . . . . . . . . . . . . 57

6.6 Value of ηe and ηk . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

6.7 Effect of distance on energy consumption of nodes . . . . . . . . . . 58

6.8 Effect of distance on delay . . . . . . . . . . . . . . . . . . . . . . . 59

6.9 Effect of distance on path loss . . . . . . . . . . . . . . . . . . . . . 60

6.10 Placement of nodes on the human body . . . . . . . . . . . . . . . . 62

6.11 Network flow tree in multi-hop routing scheme . . . . . . . . . . . . 63

6.12 Network flow tree in forwarder based routing scheme . . . . . . . . 65

6.13 Comparison of number of dead nodes in multi-hop and forwarder

based routing techniques . . . . . . . . . . . . . . . . . . . . . . . . 66

6.14 Comparison of stability period and network lifetime in multi-hop

and forwarder based routing techniques . . . . . . . . . . . . . . . . 67

6.15 Comparison of packets sent to sink (aggregated) in multi-hop and

forwarder based routing techniques . . . . . . . . . . . . . . . . . . 68

6.16 Comparison of dropped packets (aggregated) in multi-hop and for-

warder based routing techniques . . . . . . . . . . . . . . . . . . . . 69

6.17 Comparison of received packets (aggregated) in multi-hop and for-


6.18 Comparison of residual energy in multi-hop and forwarder based

routing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

6.19 Comparison of delay in multi-hop and forwarder based routing tech-

niques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

6.20 Comparison of path loss in multi-hop and forwarder based routing

techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

6.21 Comparison of energy consumption in multi-hop and forwarder based

routing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

6.22 Comparison of average energy consumption in multi-hop and for-


xv

LIST OF TABLES

3.1 Energy Parameters of Transceivers . . . . . . . . . . . . . . . . . . 16

3.2 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.3 Improvement in Percentage for case− 1 . . . . . . . . . . . . . . . . 23

3.4 Improvement in Percentage for case− 2 . . . . . . . . . . . . . . . . 23



4.3 Improvement in Percentage . . . . . . . . . . . . . . . . . . . . . . 37


5.2 Distances of nodes from the sink . . . . . . . . . . . . . . . . . . . . 41


5.4 Improvement in Percentage . . . . . . . . . . . . . . . . . . . . . . 47


xvi

Chapter 1

Introduction

1

Nowadays, traditional health care systems are facing challenges due to increase

in the elderly population and limited financial resources. The total health care

budget of Pakistan is Rs. 9.863 billion for the year 2013–14 [1] and is expected

to increase in the upcoming years. This appeals scientists and researchers to find

the best and economical solutions for health care. Remote monitoring of patients’

vital signs presents a solution to the increasing cost of health care. Therefore,

monitoring of human body and surrounding environment is important, especially

for patients, athletes, and soldiers.

Wireless Body Area Network (WBAN) is a subfield of Wireless Sensor Networks

(WSNs) in which different vital parameters of human body are monitored. WBAN

is used to solve the problems related to health care. It consists of small, low power,

and intelligent nodes deployed on/in/around the human body for monitoring and

diagnosis (note: we use the term sensors, nodes, and sensor nodes interchangeably

in this document). The components of a node are shown in fig. 1.1. These

nodes collect data from the human body and transmit via single-hop or multi-hop

mechanism to sink which further sends the collected data to medical server. The

medical specialist at a remote place can access the patients’ data. Nodes provide

flexibility in terms of data gathering and are cost effective. WBAN provides long

term health monitoring without affecting routine activities [2].

Power Source

Sensor Unit

Transceiver

Processor

Memory

ADC

Pro

toco

ls

Figure 1.1: Components of a sensor node

There are a number of applications of WBANs including real time health moni-

toring of patients. They are also used to monitor the soldiers in the field. The

sensors placed on the body measure different physiological parameters and send

data to the concerned authorities. Interactive gaming is an emerging application

of WBANs. The players can physically move their limbs and the sensors placed

on the body send data to the gaming device. It provides enhanced entertainment.

The sensors used in WBANs have limited energy. It is difficult to replace or

recharge the batteries very often. Therefore, it is necessary to use minimum energy

in order to increase the stability period and network lifetime. Other performance

parameters used in WBANs are network throughput, delay, pathloss, etc.

There are different routing protocols used to enhance the network lifetime. We

propose a high throughput and reliable routing protocol for WBANs having in-

2

creased stability period called Forwarding data Energy Efficiently with Load bal-

ancing (FEEL). We deploy eight nodes at different positions on the human body.

Two cases are considered for the placement of sink. In the first case, sink is placed

on the chest while in the second case, sink is placed on the wrist. Two sensors

measuring ECG and glucose levels communicate directly to the sink. They possess

critical data which is sent to the sink immediately without any delay. The other

six nodes communicate to the sink via forwarder node. All nodes are homogeneous

and have same specifications. This scheme uses energy efficiently and increases the

stability period and throughput of the network. FEEL is suitable for continuous

monitoring of patients.

However, some applications require only critical data. So, we propose Reliable

Energy Efficient Critical data routing (REEC) for efficient monitoring of patients

in WBANs. The proposed protocol selects two forwarders which collect the data

of other nodes, aggregate it, and route it to the sink. REEC routes only critical

data of the patients. We define critical data as the abnormal data that demands

immediate medical aid and treatment of the patient.

For long term health monitoring, we propose a new routing protocol for Balanced

Energy Consumption (BEC) in WBANs. In BEC, relay nodes are selected based

on a cost function. The nodes send their data to their nearest relay nodes to

route it to the sink. The nodes closer to the sink send their data directly to it.

Furthermore, the nodes send only critical data when their energy becomes less

than a specific threshold. In order to distribute the load uniformly, relay nodes

are rotated in each round based on a cost function.

The proposed protocols (FEEL, REEC and BEC) assume that human body is

static. On the other hand, several mobility models are proposed in literature for

WSNs and ad hoc networks. However, they are not suitable for WBANs due

to their different movement patterns. In general, the movements of nodes can

be classified into two categories; single and group mobility. In the former case,

there is no correlation between the movements of different nodes. In this scenario,

nodes move regardless the mobility pattern of other nodes in the network. In the

latter approach, however, nodes move in a group having a particular relationship

between them. In this case, nodes move relative to a reference which decides the

movement pattern of other nodes.

We propose a new mobility model for WBANs which considers different postures

of human body. There are different posture transition probabilities from one

state to another. We consider five different postures; standing, walking, running,

sitting, and laying. In each of these postures, nodes placed on human body have

3

different movement pattern. The nodes placed on the trunk of the body show little

movement as compared to nodes placed on limbs. Furthermore, nodes exhibit

different movement patterns during routine activities. We model the movement

pattern of nodes in different postures and implement the proposed model in two

routing protocols of WBANs. We study the impact of human mobility on the

functionality of routing protocols in WBANs.

The rest of the thesis is organized as follows: chapter 2 contains related work along-

with background and chapter 3 presents the proposed FEEL protocol. Chapter

4 describes the proposed REEC protocol and the proposed BEC protocol is dis-

cussed in chapter 5. The proposed mobility model for WBANs is presented in

chapter 6. Conclusion alongwith future work is given in chapter 7 and chapter 8

contains references.

4

Chapter 2

Related Work and Background

5

The routing protocols in WBANs use different mechanisms for data transmis-

sion like, single-hop and multi-hop communication. In single-hop communication,

nodes send their data directly to the sink. On the other hand, in multi-hop com-

munication, intermediate nodes are used to route data to the sink.

A. Ehyaie et al. [3] propose an upper bound on the number of relay nodes, sensors

and their distance from sink. The relay nodes are distributed on the human body

as a network. The sensors communicate to the relay nodes which further route

data to the sink. Authors in [4] give Energy-Aware WBAN Design (EAWD)

model. It gives the position and optimum number of relay nodes in WBANs.

Relay nodes are responsible for data collection from sensors and routing it towards

the sink. They propose integer linear programming for relay nodes for energy

efficient routing. Authors in [5] derive a propagation and radio model for energy

efficient communication in WBANs. They study energy efficiency on a line and

tree topologies using these models. They find that single-hop communication is

inefficient in WBANs.

A two tier hierarchical architecture for WBANs is presented in [6]. Authors present

an interference free routing protocol. Nodes send their data to Cluster Head

(CH). This scheme monitors multiple patients and routes their data to the Base

Station (BS). In [7], authors present an adaptive routing protocol. The priority

and vicinity of nodes is taken into account for the selection of parent node for

mobile human body. T. Watteyne et al. [8] formulate a self organization protocol

for BANs. Nodes are grouped into clusters which send their data through CH to

reduce energy consumption and increase the network lifetime. The protocol shows

that clustering based approach is suitable for WBANs. In [9], authors suggest a

WBAN protocol for monitoring the patients at home. The home server collects

the data from nodes deployed on the human body and routes it to the medical

server via internet. A distributed Wireless Body Area Sensor Network (WBASN)

for medical supervision is presented in [10]. This system contains three layers:

sensor network, mobile computing network, and remote monitoring network. It

collects and stores vital signs such as ECG, blood oxygen, body temperature, etc.

M. Quwaider et al. [11] present a routing protocol for WBANs, which counts

for changes in the network. It uses store and forward mechanism to increase the

probability of successful packet transmission. The location based packet routing

is developed in this protocol. DARE [12] uses multi-hop scheme to monitor the

patients in a ward of the hospital. Sensors attached to the patients send data to

the body relay. The body relay aggregates the received data and routs it to the

sink. Authors in [13] give THE-FAME to measure the fatigue in the soccer players.

They employ a composite parameter for fatigue measurement which consists of a

6

threshold parameter for lactic acid and distance covered. The implanted sensor

sends the data to the nearest sink deployed at the boundary of the field. Similarly,

authors in [14] present a routing protocol for fatigue measurement of a soldier.

Three sensors are attached to the body to measure temperature, heartbeat and

glucose level in the blood. Different scenarios are considered for the movement

of soldier. In [15], virtual groups are formed between doctors and nurses for

efficient patient monitoring. Virtual groups are formed and modified according to

the requirements of patients and doctors. Authors propose a new metric called

Quality of Health Monitoring.

G. R. Tsouri et al. [16] propose augmented efficiency for global routing in WBANs.

Augmented efficiency is a new link cost, designed for balanced energy consump-

tion in WBANs. Authors propose On Increasing Network Lifetime (OINL) in

BANs using global routing with energy consumption balancing. It causes sub-

stantial improvement in the network lifetime. Authors in [17] suggest a new cross

layer communication protocol for WBANs called Cascading Information retrieval

by Controlling Access with Distributed slot Assignment (CICADA). It consumes

less energy and is designed for mobile WBANs. Moreover, this protocol forms a

network tree in a distributive manner. This tree is used to route data to the sink

with guaranteed collision free access to the medium.

Energy-Balanced Rate Assignment and Routing (EBRAR) protocol is presented

in [18]. It is an energy efficient routing protocol in which routing is based on

the residual energy of nodes. As a result, instead of one fixed path, data is

intelligently sent through different routes by equally distributing the load among

the nodes. Authors in [19] focus on increasing the network lifetime by relaying

and cooperation techniques. First, the relay nodes perform relaying of traffic only

so that, more energy is available for communication purposes. Furthermore, the

relays cooperate in forwarding the data from nodes to the sink. Authors in [20]

suggest a scheme in which nodes are grouped into a number of clusters. There is

a CH in each cluster which is responsible for collecting the data from nodes. CH

aggregates the received data and sends it to the sink.

M. R. Senouci et al. [21] analyze different sensor network routing protocols and

propose a new technique for increased network lifetime. Experiments show that

their protocol can extend the network lifetime and can be very effective. Au-

thors in [22] propose clustering algorithm for WSNs named as Fast and Flexible

Unsupervised Clustering Algorithm (FFUCA). It gives low complexiy along with

optimal energy consumption. In [23], authors give the techniques for transmit-

ting the vital signs to the cloud. They propose energy efficient routing and data

security mechanisms. In [24], authors propose Markov decision process model to

7

study the charging and discharging of sensor’s battery. They also study the prop-

erties of optimal transmit policies. Authors in [25] propose an energy efficient

routing protocol for WSNs. They reduce the transmission power of nodes to save

energy. They also form a virtual back bone of high energy nodes to transmit data

efficiently.

Authors in [26] analyze the WBANs channels and bit error performances. They

attach the receiving antenna to the back side and evaluate its performance. S.

Ivanov et al. [27] use cooperation between WBANs and environmental sensors to

efficiently transmit data to the distant gateway. Their suggested technique gives

improved results in terms of packet loss, power consumption and delay. Authors

in [28] present a method to elect controlled nodes which inform any abnormal be-

haviour to the CH. It saves energy and gives a better dynamic approach. Authors

in [29] give routing algorithm based on global optimization cost function. Simu-

lation results show that the protocol gives improved results relative to previous

techniques. In [30], authors present evidence-based sensor coverage model. It is

close to reality and can be extended to tackle the issues related to deployment of

nodes. Simulations show that their model performs better than traditional mod-

els. Authors in [31–33] use clustering schemes to efficiently use the energy of nodes

in WSNs. Nodes send their data to the CH which further routes it to the sink.

Different challenges in body area networks are discussed in [34]. Energy efficiency

is a major challenge which is a big hindrance in widespread use of WBANs. Other

challenges are interference and reliability. Authors in [35] decrease the inter-BAN

interference by using cooperative scheduling. It results in increased throughput.

Their proposed technique gives better packet reception rate than other schemes. In

[36], authors propose Random Incomplete Coloring (RIC) in WBANs to overcome

the interference. It increases the throughput and reduces the energy consumption.

Simulations show that RIC efficiently reduces the interference in WBANs. Dif-

ferent issues in WBANs like, energy efficiency and packet delivery are discussed

in [37]. Authors present some techniques to overcome the issues related to the

successful delivery of packets to the sink. Authors in [38] use WBAN to moni-

tor different physiological parameters of human body. They deploy nodes on the

human body which send the real time data to the sink. The data received from

nodes is used to check the physical condition of the body. Authors in [39] dis-

cuss IntraBody Communication (IBC) for WBANs. In this scheme, body is used

as a communication medium. Signal travels in the body from transmitter to re-

ceiver. The advantage of this scheme is that it provides increased data security.

In [40], authors propose a method to efficiently use the energy of heterogeneous

sensor nodes to increase the network lifetime. Their proposed algorithm considers

8

the heterogeneity of nodes and requirement of the application. Their suggested

scheme saves energy of nodes. Authors in [41] use relay nodes to transmit data

from sensors placed on the human body. It saves the energy of nodes and increases

their lifetime. Simulations show that their scheme gives improved lifetime and bit

error rate. Authors in [42] present a method to monitor the position of nodes on

the human body. They continuously monitor the position of limbs of the human

body. Authors in [43] present collision avoidance protocol for reliable data delivery

in WBAN. They also propose security mechanism to restrict illegal access to the

network.

L. Yao et al. [44] present a secure mechanism for transmitting the vital parame-

ters to the control unit. They give ECG-signal based secure communication. It

gives security and confidentiality of data. Authors in [45] propose a method to

find a fault in the nodes in WBANs. In some situations, nodes become inactive

and cannot monitor the vital signs correctly. The suggested scheme finds the inac-

tive node and informs about any kind of abnormality in WBAN. In [46], authors

propose a thermal-aware protocol for routing the data of nodes. The path having

minimum distance is selected. Alternative paths are selected in case of hotspots;

nodes which are heated due to increased energy dissipation. Authors in [47] pro-

pose a new Media Access Control (MAC) protocol for reliable data delivery in

WBANs. They propose a channel access mechanism for increased throughput. In

[48], authors propose security mechanism for WBANs. They also design a micro-

controller to reduce the energy consumption. Experimental results show that their

scheme works better. Authors in [49] present interference avoidance scheme for

reliable data delivery. It is based on Carrier Sense Multiple Access with Collision

Avoidance (CSMA/CA) and Time Division Multiple Access (TDMA). It increases

the throughput in WBANs.

Authors in [50] present fair data collection scheme for WSNs. As nodes are located

at different distances from the sink, so fair data distribution leads to extended net-

work lifetime. In a WBAN, nodes are located close to each other and within the

communication range of each other. Therefore, efficient MAC layer protocols are

employed to avoid collision. Y. Zhang et al. [51] propose a priority-guaranteed

MAC protocol for WABNs. In this protocol, control channels are separated from

data channels. Priority-specific control channels are used for life-critical appli-

cations. Authors also present wakeup trigger mode to facilitate priority traffic.

Authors in [52] propose PLA-MAC for priority-based traffic in WBANs. The

sensed data is bifurcated according to their Quality-of-Service (QoS) (i.e., delay,

reliability and throughput) requirements and is assigned priorities. These priori-

ties determine the transmission schedule of packets. The superframe structure also

9

varies according to the amount of data causing minimum energy consumption.

Due to continuous movements of the human body, the link between the sink and

the node may not be connected all the time. The link breakages result in loss of

data. P. Ferrand et al. [53] describe a cooperative transmission scheme in WBANs

to overcome the disconnected links. They use a multi-hop scheme to ensure good

connectivity. In their proposed work, some sensors are elected to support the nodes

having bad links. This way, data is efficiently routed to the sink. Authors in [54]

propose an obesity control framework using WBANs. They propose software and

hardware architectures for obesity control. In their proposed framework, sensors

are placed on the human body. These sensors monitor different vital signs and

compare them with the predefined thresholds. If the sensed value exceeds the

threshold, the information is sent to a smart phone or a personal computer to

allow taking the appropriate action to prevent body harm.

In [55], authors place wearable sensors on the human body and study the link

behaviour in dynamic conditions. They record the link quality, packet delivery and

Received Signal Strength Indicator (RSSI) values in real-time. They also describe

the packet delivery and energy efficiency obtained by using dynamic routing and

adaptive transmission power schemes, respectively. Authors in [56] estimate the

lifetime of Health Monitoring Network (HMN) using probabilistic analysis. It is

important to estimate the lifetime of the network to replace/recharge the batteries

of nodes to continuously monitor the required parameters. In [57], authors use

wireless accelerometer sensor to determine the link performance and lost packets

for different runners and for different sensor locations. They conclude that sensors

placed on the wrist give best results.

In the following sections, we discuss some routing protocols in detail.

2.1 Mobility-supporting Adaptive Threshold-based Thermal-aware Energy-

efficient Multi-hop ProTocol (M-ATTEMPT) for WBANs

In M-ATTEMPT [58], the high data rate nodes are placed near the sink on the

human body. Whereas, the nodes having low data rate are placed away from

sink. The nodes near the sink have more energy than the other nodes in M-

ATTEMPT. The protocol operation is categorized into different phases. In the

initialization phase, all nodes broadcast hello messages. This hello packet contains

the information about neighbors and distance from sink in terms of hop-counts.

In the routing phase, routes with minimum hops are selected for data transmission

from nodes to the sink. In case of critical data, the nodes send their data directly

10

to the sink. If two routes are available then route with minimum hop counts is

selected. The low data rate nodes send their data to the nearest high data rate

nodes which send the aggregated data to the sink. In M-ATTEMPT, single-hop

and multi-hop communication is utilized to enhance the network lifetime. After

the route selection, TDMA slots are assigned to the nodes. All the nodes transmit

data in their scheduled time slots.

In M-ATTEMPT, nodes are categorized into different levels according to their data

rates as parent nodes, first-level child nodes and second-level child nodes. During

the movement of the human body, if a child node moves away from its parent node,

it can associate to another nearest parent node to save energy. Due to excessive

energy consumption, a node may get heated which is known as hot-spot. In this

case, alternative paths are selected until the node returns to its original normal

state. However, nodes deplete their energy quickly resulting in shorter stability

period and lack of critical data transmission from some nodes. We propose new

protocols to overcome the deficiencies in M-ATTEMPT. We compare the proposed

protocols with M-ATTEMPT and discuss different performance metrics in detail

in chapters 3 and 4.

2.2 Stable Increased-throughput Multi-hop Protocol for Link Efficiency

(SIMPLE) in WBANs

In SIMPLE [59], eight nodes are placed at different positions on the human body

with sink at the waist. The working of SIMPLE protocol is divided into different

phases. In the initial phase, sink broadcasts a short information packet to inform

the nodes about its position on the human body. Each node broadcasts a packet

which contains the node ID, its residual energy value and its location. In the

next phase, a forwarder node is selected which routes the data of other nodes,

thus saving their energy. The forwarder is selected based upon its distance from

sink and its residual energy status. The node having minimum distance from

sink and having maximum residual energy value is selected as a forwarder. All the

corresponding nodes send data to the forwarder node which aggregates the received

data and routes it to the sink. Furthermore, the nodes having critical data send

their data directly to the sink to observe minimum delay. In the scheduling phase,

forwarder node assigns TDMA based time slots to its children nodes. All the

nodes transmit data in their scheduled time slots to avoid any collision and loss

of data. In this way, data is efficiently routed from nodes to sink.

However, routing load is not uniformly distributed among all the nodes in SIMPLE

11

protocol. The placement of sink is also an important parameter as it greatly affects

the throughput. In addition, the human comfort level must also be taken into

account when deciding the position of sink. We propose new routing protocols to

overcome the above mentioned drawbacks. Therefore, we compare the proposed

protocol with SIMPLE and discuss different performance parameters in detail in

chapters 3 and 4.

2.3 On Increasing Network Lifetime (OINL) in body area sensor net-

works using global routing with energy consumption balancing

In OINL, global routing based on Dijkstras algorithm is used to enhance net-

work lifetime in WBANs. A link-cost function is also proposed for enhancing the

network lifetime. In OINL, link-cost information is periodically gathered at the

Access Point (AP) in the form of channel attenuation. All the routing calcula-

tions are performed at the AP as it has more energy than nodes. The channel

attenuation for the selected link between nodes j and k is given as:

αj,k =RSSI

Ptx

(2.1)

Where, RSSI denotes the received signal strength at node k and Ptx is the trans-

mitted power. The energy of nodes used thus far is calculated using eq. 2.2.

Eji = Ej

i−1 +RSSITαj,k

(2.2)

Here, j denotes the node ID and i is the current round. Eji is the accumulated

energy of node j at round i and αj,k is the attenuation of the selected link. RSSIT

is the predefined target RSSI level. The link cost C ij,k is computed as:

C ij,k =

RSSITαj,k

×

1 +(

Ek

i

Emin

i

)

2

(2.3)

The link-cost function is derived by dividing the accumulated energy of node i

with the minimum energy across all nodes, Emini . The ratio is then raised to the

power of M ≥ 0, which reflects the effect of balanced energy consumption. The

nodes send the sensed data through relay nodes having minimum link-cost. In

this way, nodes consume energy in a balanced way which enhances the network

lifetime.

However, the drawback of this scheme is that it burdens the nodes near the sink (or

12

AP). The network lifetime can further be improved by using direct transmission

of nodes near the sink. We propose a new technique which gives improved perfor-

mance than OINL with reduced computational overhead as discussed in chapter

5.

13

Chapter 3

FEEL: Forwarding Data Energy Efficiently with

Load Balancing in Wireless Body Area Networks

14

3.1 Motivation

WBANs monitor human health with limited energy resources. In these network,

different routing schemes are used to route data towards sink which further sends

data to the medical server or other monitoring station. M-ATTEMPT uses multi-

hop communication for normal data delivery to sink. Nodes communicate directly

to the sink for routing critical data. However, they deplete their energy quickly

resulting in shorter stability period and lack of critical data from some nodes.

SIMPLE uses a cost function for forwarder node selection which prolongs the

stability period. However, load is not uniformly distributed among all the nodes.

The placement of sink is also an important parameter as it greatly affects the

throughput. In addition, the human comfort level must also be taken into account

when deciding the position of sink. SIMPLE and M-ATTEMPT protocols are

discussed in chapter 2 in detail.

8

6

1

2

3

4

7 5

Sink

Node

Figure 3.1: Deployment of nodes on the human body in FEEL

The radio model used for calculating the energy consumption of nodes in discussed

in the next section in detail.

15

3.2 Radio Model

There are different radio models in the literature. We use first order radio model

given in [60]. The equations for first order radio model are given as:

ETX(k, d) = ETXelect(k) + εamp(k, d) (3.1)

ETX(k, d) = ETXelect.k + εamp.k.d2 (3.2)

ERX(k, d) = ERXelect(k) = ERXelect.k (3.3)

Where, ETX is the energy consumed in transmission process and ERX is the energy

consumed by the receiver. ETXelect and ERXelect are the energies required to run

the electronic circuit of transmitter and receiver respectively. εamp is the energy

required by the amplifier circuit, k is the packet size whereas d is the distance

between transmitter and receiver.

In WBANs, the communication medium is human body which contributes attenu-

ation to the radio signals. Therefore a path loss coefficient parameter n is included

in the radio model. Equation for the transmitter energy consumption is:

ETX(k, d) = ETXelect.k + εamp.k.dn (3.4)

The energy parameters depend upon the hardware of the system. We consider two

transceivers, Nordic nRF 2401A and Chipcon CC2420 , which are used frequently

in WBAN technology. The energy parameters for these transceivers are shown in

table 3.1.

Table 3.1: Energy Parameters of Transceivers

Parameter nRF 2401A CC2420 Units

DC current (TX) 10.5 17.4 mADC current (RX) 18 19.7 mA

Min. supply voltage 1.9 2.1 VETXelect 16.7 96.9 nJ/bitERXelect 36.1 172.8 nJ/bitεamp 1.97 271 nJ/bit/mn

3.3 FEEL: Proposed Protocol

In this section, we discuss a novel routing protocol for WBANs. Uniform energy

consumption of nodes is important for long term health monitoring in WBANs.

16

We propose FEEL, a new routing protocol with improved stability period and

throughput. The following subsections give detail of the proposed protocol.

3.3.1 Deployment of Nodes

In FEEL, we deploy eight homogeneous nodes on the human body. Node 8 is

ECG and node 7 is glucose level sensor. These two nodes send their data directly

to the sink. We use two different topologies for the placement of sink on the

human body. In the first case sink is placed on the chest while in the second case

it is placed on the wrist. We place the sink on the chest and wrist to study the

performance of the proposed protocol. We study the impact of sinks placement

on energy consumption of nodes. Fig. 3.1 shows the placement of nodes and sinks

on the human body. It also shows the distances of nodes from sinks.

3.3.2 Start-up Phase

In the initial phase sink broadcasts a HELLO message containing following three

types of information.

• Location of sink.

• Location of neighbours.

• Information about possible routes to the sink.

The nodes receive this HELLO packet and update their routing table. They also

send information about their IDs and residual energy status to the sink. Fig. 3.2

shows the contents of HELLO message.

Figure 3.2: Contents of HELLO message in FEEL

17

3.3.3 Selection of Forwarder Node

In this section, we present the selection criteria of forwarder node. In order to

save energy and balance the energy consumption of the network, FEEL selects a

new forwarder in each round. As sink knows the residual energy of all nodes, it

broadcasts the ID of the node having maximum residual energy to make it the the

forwarder node.

Forwardernode = Nodemax(R.E) (3.5)

Where R.E is the residual energy of a node. Residual energy is calculated by

subtracting the consumed energy from initial energy.

Energyresidual = Energyinitial −Energyconsumed (3.6)

The node having maximum residual energy is selected as a forwarder node. All

the neighboring nodes send their data to the forwarder node. The forwarder

node aggregates the received data and routs it to the sink. In the next round,

again a new forwarder node is selected based upon the residual energy. In this

way, forwarder node rotates uniformly and all the nodes get a chance to become a

forwarder. Therefore, energy is consumed more uniformly as compared to SIMPLE

and M-ATTEMPT resulting in increased stability period and throughput.

3.3.4 Scheduling Phase

In this phase, forwarder node assigns Time Division Multiple Access (TDMA)

based time slots to its children nodes. All nodes send their data to the forwarder

node in their allocated time slots. Proper scheduling of nodes minimizes their

energy consumption.

3.3.5 Data Transmission Phase

All other nodes except ECG and glucose level measuring nodes send their data

to the forwarder. The forwarder node aggregates the received data and routs it

to the sink. Nodes measuring ECG and glucose level communicate directly to the

sink as they have critical data. If a node possesses energy less than a threshold

(γ), it communicates directly to the sink. In addition, it does not further take part

in the selection of forwarder. This is done to save the data aggregation energy of

nodes. If a node has shorter distance to the sink than forwarder node, it routs its

data directly to the sink.

18

3.4 Energy Consumption Analysis

In this section, we develop equations for single-hop and multi-hop communications.

Energy consumed for single-hop communication is:

ESH = ETX (3.7)

ETX is the transmission energy as given by:

ETX = k × (Eelect + εamp)× d2 (3.8)

Where, Eelect is the energy consumed by electronic circuit.

Now, energy consumed during multi-hop communication is given by:

EMH = k[m× (ETX) + (m− 1)× (ERX + Eda)] (3.9)

Here, ERX is the reception energy and m is the number of nodes.

3.5 Simulation Results and Analysis

In order to verify the performance of FEEL protocol, simulations are performed in

MATLAB. We study the performance of the proposed protocol in comparison with

SIMPLE and M-ATTEMPT. The initial energy of all nodes is same i.e. 0.5 J. In

simulation, we ignore the sensing energy consumed by the nodes. Simulations are

performed five times and average results are plotted. Table 3.2 shows the values

of different parameters used in simulation.

We evaluate different performance metrics of the proposed protocol. Introduction

to some of the metrics is given below.

A. Network Lifetime

It is the total time till the death of last node. It represents time for which

the network operates. In WBANs, a protocol is required to offer maximum

network lifetime.

B. Stability Period

It is the time before the death of the first node. It is an important parameter

in WBANs.

C. Throughput

Throughput is the number of packets successfully received at sink.

19

D. Residual Energy

It is the difference of initial energy and consumed energy.

E. Path Loss

It is the difference between transmitted power and received power. It is

represented in decibel (dB).

Table 3.2: Simulation Parameters

Parameter Value Units

ERXelect 36.1 nJ/bitETXelect 16.7 nJ/bitεamp 1.97 nJ/bit/m2

Eda 5 nJ/bitdo 0.1 mγ 0.1 J

Packet size (k) 4000 bitsFrequency (f) 2.4 GHz

Initial energy (Eo) 0.5 J

3.5.1 Network Lifetime

Figs. 3.3 and 3.4 show the stability period and network lifetime of FEEL protocol.

Our protocol selects the forwarder node on the basis of residual energy of nodes.

So, energy is consumed in a balanced way. As a result, stability period of FEEL

protocol is increased. In SIMPLE, the nodes closer to the sink have more chance to

become forwarder node. So energy is consumed in an imbalanced way, decreasing

the stability period. FEEL has stability period of about 5428 rounds and network

lifetime of 7486 rounds in the first case. In the second case, the stability period is

increased to 5635 rounds. It is due to the fact that sink is closer to most of the

nodes in this case. As a result less distance between nodes and sink causes less

energy consumption of nodes. So, the stability period is increased.

3.5.2 Throughput

It shows the number of packets successfully received at sink. WBANs require max-

imum data reception at the sink with minimum packets dropped. We use Random

Uniformed Model [61] for packet drop calculation. The status of communication

link can be good or bad depending upon the probability. We suppose the proba-

bility of link status to be good is 0.7. FEEL protocol achieves higher throughput

than M-ATTEMPT and SIMPLE as shown in figs. 3.5 and 3.6. Throughput de-

pends upon the number of nodes which are alive. More nodes send more packets

20

0 1000 2000 3000 4000 5000 6000 7000 80000

2

4

6

8

10

12

81%

39%

Rounds

Num

ber

of d

ead

node

s

FEELSIMPLEM−ATTEMPT

Figure 3.3: Comparison of stability period and network lifetime for case− 1

so throughput increases. As the stability period of M-ATTEMPT and SIMPLE is

less, so less number of nodes send packets resulting in less throughput. Whereas,

the FEEL protocol has longer stability period, so more nodes send packets result-

ing in increased throughput. Throughput of the FEEL protocol is even higher in

second case due to increased stability period.

3.5.3 Residual Energy

The residual energy of the network is shown in figs. 3.7 and 3.8. The FEEL

protocol uses multi-hop communication for data transmission to the sink. All

nodes except 7 and 8, transmit their data to the forwarder node which routs it to

the sink. The forwarder node is selected at the start of each round. The selection

of new forwarder in each round saves energy. In FEEL protocol a new forwarder

node is selected in each round, removing the burden of data transmission from a

single node. In M-ATTEMPT and SIMPLE, nodes die early due to heavy traffic

load and non-uniform load distribution.

3.5.4 Path Loss

Path loss shows the difference in the transmitted and received power represented

in decibels (dBs). The posture of human body affects the signal. As a result path

loss shows different behaviour during the movement of human body. There are

21

0 1000 2000 3000 4000 5000 6000 7000 80000

2

4

6

8

10

12

78%

38%

Rounds

Num

ber

of d

ead

node

s


Figure 3.4: Comparison of stability period and network lifetime for case− 2

different models used to estimate the path loss. It is a function of distance and

frequency as expressed in [62] and shown as:

PL(f, d) = PLo + 10.n.log10

(

d

do

)

+Xσ (3.10)

Where, PLo is path loss at reference distance do and n is path loss exponent. The

distance between transmitter and receiver is d, X is a gaussian random variable

and σ is the standard deviation.

Path loss at reference distance do is given as:

PLo = 10.log10

(

4.π.doλ

)2

(3.11)

Where, λ is the wavelength of electromagnetic waves.

Figs. 3.9 and 3.10 show the path loss in each round. In simulation, we use a fixed

frequency of 2.4 GHz from ISM band. We use path loss coefficient of 3.38 and

standard deviation of 4.1. FEEL has lower path loss as shown in the figs. 3.9 and

3.10. In the proposed protocol, path loss decreases after 4000 rounds. It is due

to the fact that some nodes die after 4000 rounds. So less number of nodes have

lower path loss. FEEL protocol has lower path loss than M-ATTEMPT.

The improvement (%) provided by the FEEL protocol to M-ATTEMPT and SIM-

PLE is shown in tables 3.3 and 3.4.

22

0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1

1.5

2

2.5

3

3.5x 10

4

Rounds

Pac

kets

rec

eive

d at

sin

k


Figure 3.5: Comparison of network throughput (aggregated) for case− 1

Table 3.3: Improvement in Percentage for case− 1

Parameter Improvement (%) Improvement (%)

in M-ATTEMPT in SIMPLEStability period 153 22Network lifetime 0.5 0.2Throughput 72 7

Average residual energy 7 0.2Average path loss 19 0.000247

Table 3.4: Improvement in Percentage for case− 2


in M-ATTEMPT in SIMPLEStability period 162 27Network lifetime -0.005 -0.0083Throughput 93 20

Average residual energy 1.08 0.0098Average path loss 17 -0.278

23

0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1

1.5

2

2.5

3

3.5x 10

4

Rounds

Pac

kets

rec

eive

d at

sin

k


Figure 3.6: Comparison of network throughput (aggregated) for case− 2

0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1

1.5

2

2.5

3

3.5

4

Rounds

Res

idua

l Ene

rgy

(J)


Figure 3.7: Comparison of residual energy for case− 1

24

0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1

1.5

2

2.5

3

3.5

4

Rounds

Res

idua

l Ene

rgy

(J)


Figure 3.8: Comparison of residual energy for case− 2

0 1000 2000 3000 4000 5000 6000 7000 80000

50

100

150

200

250

300

350

400

450

Rounds

Pat

h Lo

ss (

dB)


Figure 3.9: Comparison of path loss for case− 1

25

0 1000 2000 3000 4000 5000 6000 7000 80000

50

100

150

200

250

300

350

400

450

Rounds

Pat

h Lo

ss (

dB)


Figure 3.10: Comparison of path loss for case− 2

26

Chapter 4

REEC: Reliable Energy Efficient Critical data

routing in Wireless Body Area Networks

27

4.1 Motivation

The routing schemes in WBANs use different data transmission mechanisms like,

single-hop, multi-hop, minimum-hop, etc. In single-hop routing scheme, distant

nodes die faster than the nodes nearer to the sink. On the other hand, in multi-

hop and minimum-hop routing schemes, the nearer nodes die earlier as they have

more data to route than the distant nodes. M-ATTEMPT uses multi-hop scheme

for routing data from sensor nodes to sink. It is a thermal aware routing pro-

tocol which selects a new route after a hotspot detection. However, the hotspot

detection causes more energy consumption. SIMPLE overcomes the deficiencies

in M-ATTEMPT. It selects a new forwarder in each round that receives and ag-

gregates the data of other nodes and routes it to the sink. However, this protocol

burdens the single forwarder node by routing all the data through it. In SIMPLE,

nodes send all the data (normal and critical) which is unnecessary in most of

the scenarios in WBANs. Therefore, we present REEC which sends only critical

data and avoids the transmission of redundant data. SIMPLE and M-ATTEMPT

protocols are discussed in chapter 2 in detail.

The radio model used for calculating the energy consumption of nodes in discussed

in the next section in detail.

4.2 Radio Model

There are different radio models in the literature. We use first order radio model

given in [52]. The equations for first order radio model are given below:

∆TX(κ, ℓ) = ∆TXelect(κ) + εamp(κ, ℓ) (4.1)

∆TX(κ, ℓ) = ∆TXelect.κ+ εamp.κ.ℓ2 (4.2)

∆RX(κ, ℓ) = ∆RXelect(κ) = ∆RXelect.κ (4.3)

Where ∆TX is the energy consumed in transmission process. ∆RX is the energy

consumed by the receiver. ∆TXelect and ∆RXelect are the energies required to run

the electronic circuit of transmitter and receiver, respectively. εamp is the energy

required by the amplifier circuit whereas κ is the packet size. The distance be-

tween transmitter and receiver is represented by ℓ.

In WBANs, the communication medium is human body which introduces attenu-

ation to the radio signals. Therefore a path loss coefficient parameter n is included

28

in the radio model. Equation for the transmitter energy consumption is:

∆TX(κ, ℓ) = ∆TXelect.κ+ εamp.κ.ℓn (4.4)

Energy parameters depend upon the hardware of the system. We consider two

transceivers Nordic nRF 2401A and Chipcon CC2420 that are used frequently in

WBANs. The energy parameters for these transceivers are enlisted in table 4.1.




Supply voltage (min.) 1.9 2.1 V∆TXelect 16.7 96.9 nJ/bit∆RXelect 36.1 172.8 nJ/bitεamp 1.97 271 nJ/bit/mn

4.3 REEC: Proposed Protocol

In this section, we describe the proposed routing protocol. One of the major

challenges in WBANs is to increase the network lifetime for continuous monitoring

of patients. REEC consumes energy efficiently that leads to increased network

lifetime. The detail is given in the following subsections.

4.3.1 Deployment of Nodes

In the proposed protocol, we deploy eight sensors on the human body. All nodes

are homogeneous i.e. having equal initial energy. In REEC, we place the sink

at the centre of human body. We choose abdomen for the placement of sink as

it is less mobile (as compared to limbs) and has same distance from head and

foot. The sink is placed on the abdomen of the human body as shown in fig. 4.1.

The information from sink is sent to the physician via internet for inspection and

diagnosis. It is also sent to the ambulance service office for immediate help in case

of emergency. Medical server stores the patients’data for future purposes. The

whole scenario of WBAN is shown in fig. 4.1.

29

EC

G

sen

so

r

Pu

lse

rate

sen

so

r

EM

G a

nd

mo

tion s

ensors

Sink

1 2

4 3

5

7

6

8

Physician

Medical server

Ambulance

PDA

Laptop

Assessment and treatment

Information

Figure 4.1: Deployment of nodes on the human body in REEC


In this phase, sink broadcasts a short information packet which contains the lo-

cation of sink on the human body. Each node receives this packet and stores the

location of the sink. Afterwards, each node broadcasts a packet which contains

the ID of node, its location and residual energy status. In this way, all nodes are

updated with the location of neighbouring nodes and the sink.

4.3.3 Forwarders’ Selection Phase

In this section, we present the selection criteria of the forwarder nodes. The

complete set of nodes A is given by:

A = {1, 2, 3, 4, 5, 6, 7, 8} (4.5)

30

In order to consume the energy efficiently, REEC uses cost function ξ to select

new forwarders in each round. The ξ is calculated as:

ξ(i) =

(

ℓ(i)

ℜ(i)

)

∀i ∈ A (4.6)

Here, ℓ is the distance between the node and sink and ℜ is the residual energy of

node. The node having minimum value of ξ is selected as forwarder. We consider

two sets of nodes as:

α = {1, 2, 3, 4} (4.7)

β = {5, 6, 7, 8} (4.8)

∴

α ( A (4.9)

β ( A (4.10)

α ∩ β = ø (4.11)

A = α ∪ β (4.12)

In REEC, two forwarders are selected in each round, one from α and second from

β. The Ψα is selected from α and Ψβ is selected from β. The total number of

nodes is ℵ.

Ψα = ℵmin(ξ(i)) ∀i ∈ α (4.13)

Ψβ = ℵmin(ξ(i)) ∀i ∈ β (4.14)

The node having minimum value of ξ is selected as a forwarder node. Sink broad-

casts the IDs of Ψα and Ψβ after calculating ξ. The nodes from α send their data

to Ψα whereas nodes from β send their data to Ψβ . The forwarder nodes aggregate

the data of all the nodes and route it to the sink. In the next round, again two new

forwarder nodes are selected based upon ξ. In this way, forwarder nodes rotate

and all the nodes get a chance to become a forwarder. Therefore, energy is con-

sumed more efficiently than in SIMPLE and M-ATTEMPT resulting in increased

lifetime and throughput. In REEC, the routing load is shared between the two

forwarders which results in efficient energy consumption of nodes. The forwarders

are selected dynamically which results in fair load distribution. They collect the

data from distant nodes and save their energy. Furthermore, the two forwarders

are located in the upper and lower parts of the human body and collect data from

their corresponding nodes as shown in fig. 4.1.

31


In this phase, forwarder nodes assign Time Division Multiple Access (TDMA)

based time slots to their corresponding nodes. The nodes send their data to the

forwarders Ψα or Ψβ in their allocated time slots. Proper scheduling of nodes

minimizes their energy consumption. It also avoids collision to achieve better

network throughput.


The initial energy ∆o of all nodes is 0.5 J. The nodes send only critical data. The

forwarder nodes aggregate the received data and route it to the sink. If a node

possesses energy less than a threshold τ , it communicates directly to the sink. In

addition, it does not further take part in the selection of forwarder. This is done

to avoid energy consumption in data aggregation. If a node has shorter distance

to the sink than forwarder, it routes its data directly to the sink. The nodes from

α send their data to Ψα and nodes from β send their data to Ψβ. The flowchart

of the proposed protocol is shown in fig. 4.2.


In this section, we develop equations for single-hop and multi-hop communications.

Energy consumed for single-hop communication is:

∆SH = ∆TX (4.15)

Here, ∆TX is the transmission energy as given by:

∆TX = κ× (∆elect + εamp)× ℓ2 (4.16)

The energy consumed during multi-hop communication is given by:

∆MH = κ[ℵ × (∆TX) + (ℵ − 1)× (∆RX +∆da)] (4.17)

Where, ∆da is the data aggregation energy and ℵ is the number of nodes in the

network.

32

Start

If node from If critical value If critical value

Send data to

Scan

Body

Send data to

If 0<energy(J) If 0<energy(J)

Send data to the

sink

End

Yes

YesYes

YesYes

NoNo

If dis_sink dis_ �If dis_sink dis_ �

NoNoYes

Yes

dis_sink: Distance of node from sink

dis_ : Distance of node from

dis : Distance of node from

No

No No

If energy(J )> If energy(J )>

Yes Yes

Yes Yes

NoNo

Figure 4.2: Flowchart of REEC

4.5 Experiments and Discussions

In order to verify the performance of REEC, simulations are performed five times

and average results are plotted. Table 4.2 presents the simulation parameters. We

ignore the sensing energy consumed by the nodes in simulation. We assume that

the probability of critical data is 70%. In the simulation of REEC, we set the

value of τ as 20% of ∆o.

We study the performance of the proposed protocol in comparison with SIMPLE

and M-ATTEMPT. Different performance metrics of REEC are evaluated and are

discussed in the following subsections.

33



∆TXelect 36.1 nJ/bit∆TXelect 16.7 nJ/bitεamp 1.97 nJ/bit/mn

∆da 5 nJ/bitℓo 0.1 mκ 4000 bitsν 2.4 GHz∆o 0.5 J

4.5.1 Stability Period and Network Lifetime

The network lifetime of the proposed protocol is shown in fig. 4.3. Our protocol

selects two forwarders in each round which aggregate the data of other nodes and

route it to the sink. The proposed protocol has 25% and 159% improved stability

period than SIMPLE and M-ATTEMPT, respectively. It shows that energy of

all the nodes is consumed uniformly. Due to efficient energy usage, the proposed

protocol also achieves the high network lifetime of about 10767 rounds.

0 2000 4000 6000 8000 10000 120000

2

4

6

8

10

12

Rounds

No.

of d

ead

node

s

REECSIMPLEM−ATTEMPT

Figure 4.3: Comparison of stability period and network lifetime in REEC, SIMPLEand M-ATTEMPT

34

4.5.2 Throughput

Throughput is the number of packets successfully received at sink. In WBANs,

routing protocols are needed which give high network throughput for reliable mon-

itoring of the patients, elderly peopole, etc. REEC consumes energy efficiently

resulting in longer network lifetime. The nodes are alive for longer time and send

more packets that leads to increased throughput. We use Random Uniform Model

[53] for packet drop calculation. The status of communication link can be good

or bad depending upon the probability. We suppose the probability of link status

to be good is 0.7. The proposed protocol gives better throughput than SIMPLE

and M-ATTEMPT as shown in fig. 4.4.

0 2000 4000 6000 8000 10000 120000

0.5

1

1.5

2

2.5

3

3.5x 10

4

Rounds

Pac

kets

rec

eive

d at

sin

k


Figure 4.4: Comparison of network throughput (aggregated) in REEC, SIMPLE andM-ATTEMPT


The residual energy of the network is shown in fig. 4.5. The forwarder nodes Ψα

and Ψβ receive the data of their corresponding nodes and route it to the sink. As

nodes send critical data to the nearest forwarder node, so less energy is consumed

and they stay alive for longer time. In REEC, the energy of nodes depletes slowly

as shown in fig. 4.5.

35

0 2000 4000 6000 8000 10000 120000

0.5

1

1.5

2

2.5

3

3.5

4

Rounds

Res

idua

l ene

rgy

(J)


Figure 4.5: Comparison of residual energy in REEC, SIMPLE and M-ATTEMPT

4.5.4 Path Loss

Path loss is the difference between the transmitted and received power represented

in decibels (dbs). The posture of the human body affects the electromagnetic

signals. As a result, path loss shows different behaviour along different body

parts. There are different models used to estimate the path loss. Path loss is a

function of distance and frequency as shown below:

Γ(ν, ℓ) = Γo + 10.n.log10

(

ℓ

ℓo

)

+Xσ (4.18)

Where, Γo is path loss at reference distance ℓo and n is path loss exponent. The

distance between transmitter and receiver is ℓ and ν is the frequency. X is a

gaussian random variable and σ is the standard deviation [63].

Path loss at reference distance ℓo can be expressed as:

Γo = 10.log10

(

4.π.ℓoλ

)2

(4.19)

Here, λ is wavelength of electromagnetic waves.

Fig. 4.6 shows the path loss in each round for the proposed protocol. In simula-

tion, we use a fixed ν of 2.4 GHz from ISM band. We use the values of n and σ

as 3.38 and 4.1, respectively.

The improvement in percentage provided by the proposed protocol to M-ATTEMPT

36

0 2000 4000 6000 8000 10000 120000

50

100

150

200

250

300

350

400

450

500

Rounds

Pat

h Lo

ss (

dB)


Figure 4.6: Comparison of path loss in REEC, SIMPLE and M-ATTEMPT

and SIMPLE is shown in table 4.3.

Table 4.3: Improvement in Percentage


in M-ATTEMPT in SIMPLEStability period 159 25Network lifetime 45 44Throughput 94.4 22

Average residual energy 25.3 30.4Average path loss 41 44

37

Chapter 5

BEC: A Novel Routing Protocol for Balanced

Energy Consumption in Wireless Body Area

Networks

38

5.1 Motivation

In WBANs, balanced energy consumption of nodes helps to monitor the vital signs

of the human body for increased time period. OINL has increased network lifetime

due to the balanced energy consumption of nodes. It collects link-cost periodically

at the sink, where all routing decisions are performed. In OINL, nodes send the

data via routes that have minimum cost. The cost function of OINL is given as:

C ij,k =

RSSITαj,k

×

1 +(

Ek

i

Emin

i

)M

2

(5.1)

Where, RSSIT is the target RSSI value required to achieve reliable communi-

cation and αj,k is the channel attenuation for the link between j and k. Eki is

the accumulated energy of node i at round k and Emini is the minimum accu-

mulated energy across all nodes. In eq. 5.1, M ≥ 0 which shows the effect of

imbalanced energy consumption. Eq. 5.1 transforms to conventional cost function

when M = 0, which is the power required to transverse a link regardless of the

accumulated energy of nodes. OINL protocol is discussed in chapter 2 in detail.

However, one deficiency of OINL is that it results in increased energy consumption

of nodes in data reception and aggregation. As data is routed through shortest

path, so intermediate nodes may be involved in data reception and aggregation.

It results in increased energy consumption of nodes near the sink.




Supply voltage (min.) 1.9 2.1 VETXelect 16.7 96.9 nJ/bitERXelect 36.1 172.8 nJ/bitεamp 1.97 271 nJ/bit/mn

5.2 Analysis of Energy Consumption

In WBANs, nodes consume different amount of energy in single-hop and multi-hop

communications. Energy consumption in a single-hop communication is given as:

Esh = ETX (5.2)

39

Where, ETX is the transmission energy which is calculated as:

ETX = (εamp + Eelect)× s× d2 (5.3)

Where, εamp is the energy consumed by the amplifier and Eelect is the energy

consumed by the electronic circuit. The packet size is denoted by s and d shows

the distance between the node and the sink.

On the other hand, energy consumption in multi-hop communication is given as:

Emh = s× n

[

ETX + (EDA + ERX)×(n− 1)

n

]

(5.4)

In eq. 5.4, n is the number of hops and EDA is the energy consumed in data

aggregation. ERX is the energy consumed in data reception and we assume that

ETX = ERX .

5.3 BEC: The Proposed Protocol

In this section, we discuss the proposed routing protocol. The detail is given in

the following subsections.

5.3.1 Radio Model

A number of radio models are proposed in the literature. We use first order radio

model [64] given as:

ETX(s, d) = ETXelect(s) + εamp(s, d) (5.5)

ETX(s, d) = ETXelect.s+ εamp.s.d2 (5.6)

ERX(s, d) = ERXelect(s) = ERXelect.s (5.7)

In WBANs, the human body contributes attenuation to the radio signals. There-

fore, a path loss coefficient parameter n is included in the radio model. The

expression for the energy consumption is given as:

ETX(s, d) = ETXelect.s+ εamp.s.dn (5.8)

Different types of sensors are available for the monitoring of physiological param-

eters of human body in WBANs. Table 5.1 shows the energy parameters of two

40

transceivers which are widely used in WBAN technology.

5.3.2 Placement of Nodes

In BEC, eight nodes are placed on the human body. All nodes have equal initial

energy (i.e. nodes are homogeneous). The sink is placed on the chest of the human

body as shown in fig. 5.1. Table 5.2 shows the distances between the nodes and

8

6

1

2

3

4

7 5

Node

Sink

1

2

3

4

OINL

BEC

Figure 5.1: Placement of nodes on the human body and mechanism for path selectionin OINL and BEC

the sink.

Table 5.2: Distances of nodes from the sink

Node Distance (m)

1 0.75172 0.74073 0.45284 0.44025 0.32026 0.32027 0.10008 0.0500

41


In this phase, the sink broadcasts a HELLO packet to all the nodes. Each node

receives this packet and stores the location of the sink. Then each node broadcasts

a packet which contains the ID of a node, its location and the value of the residual

energy. In this way, all nodes are updated with the location of neighbouring nodes,

position of the sink and possible routes to the sink. Fig. 5.2 depicts the format of

the HELLO packet.

t

Positionation ation

Figure 5.2: Format of the HELLO packet in BEC

5.3.4 Routing Phase

In this phase, nodes select their path to the sink. The nodes closer to the sink

send their data directly to the sink. However, the nodes far away from the sink use

intermediate (relay) nodes to route the data. The mechanism of the path selection

for the proposed protocol is shown in fig. 5.1.

OINL selects the path with minimum attenuation and more nodes are involved (see

fig. 5.1) which results in more energy consumption in the form of data reception

and aggregation, so nodes die quickly. On the other hand, the proposed protocol

selects a path with suitable number of intermediate nodes and successfully routes

the data to the sink. This way, less energy is consumed and nodes stay alive for

a long time. The cost function used in the proposed routing scheme is given as:

C(i) =1

R.E(i)(5.9)

Where, C(i) is the cost of node i. In eq. 5.9, R.E(i) represents the residual energy

of node i. In BEC, a node having minimum cost is selected as a relay node. In

this way, balanced energy consumption results in increased network lifetime.

There is a trade off between having only critical (emergency) data for long term

and normal (continuous) data for small time period. As critical patients need

42

immediate medical treatment, therefore, critical data is routed without any hin-

drance. In BEC, we implement reactive routing when the energy of nodes decreases

below a threshold (τ).


In this phase, the sink assigns Time Division Multiple Access (TDMA) based time

slots to all the nodes. All the nodes use the same frequency band and transmit

their data in different time slots. The nodes send their data in their scheduled

time slots to avoid any collision.


The initial energy of all nodes is the same (i.e. Eo = 0.5 J). The nodes sense

the vital parameters of the human body and send data to the sink continuously.

However, after the nodes are left with energy less than τ , the proposed protocol

uses reactive routing. Therefore, human vital parameters are monitored for long

term.

5.4 Experiments and Discussions

In order to verify the performance of the proposed protocol, simulations are per-

formed five times and average results are plotted. Table 5.3 shows the simulation

parameters. We ignore the sensing energy consumed by the nodes in our simu-

lation. Furthermore, we assume that 30% of the data is critical. We study the

performance of the proposed protocol in comparison with OINL. The following

subsections contain the detail of different performance parameters.




EDA 5 nJ/bitτ 0.1 Jdo 0.1 ms 4000 bitsf 2.4 GHzEo 0.5 J

43

5.4.1 Stability Period and Network Lifetime

The stability period is the time from the start of the network till the death of the

first node. On the other hand, network lifetime shows the time from the start of

the network till the death of the last node.

The proposed routing scheme selects relay nodes on the basis of cost value. The

node having the minimum cost value is selected as a relay node for data trans-

mission. Therefore, nodes exhibit a uniform energy consumption which increases

the network lifetime. Our proposed protocol sends data to the sink by consuming

less energy. BEC has 49% improved network lifetime than OINL. It shows that

the energy of all the nodes is efficiently consumed. Due to efficient energy usage,

the proposed protocol achieves increased network lifetime. Fig. 5.3 shows the

comparison of the stability period and the network lifetime. It is evident that the

BEC achieves improved stability period and network lifetime.

Figure 5.3: Comparison of stability period and network lifetime in BEC and OINL

5.4.2 Network Throughput

The throughput is the number of packets successfully received at the sink per

unit time. The proposed protocol consumes energy efficiently resulting in longer

network lifetime. The nodes are alive for longer time and send more packets that

leads to increased throughput. In this work, we use a random uniformed model

[64] for packet drop calculation. The status of the communication link can be

good or bad depending upon the probability. We assume the probability of 0.7 for

44

the link status to be good. BEC offers increased throughput than OINL as shown

in fig. 5.4. The throughput of the proposed protocol decreases after 5245 rounds.

It is due to the fact that nodes are left with energy less than τ and only critical

data is routed. Therefore, BEC has increased the network lifetime at the cost of

lower throughput after 5245 rounds (see figs. 5.3 and 5.4).

4000 5000 6000 7000 8000 9000 100002

2.2

2.4

2.6

2.8

3

3.2

3.4

3.6

3.8

4x 10

4

Rounds

Pac

kets

rec

eive

d at

sin

k

OINLBEC

Figure 5.4: Comparison of network throughput in BEC and OINL


The residual energy of the network in the proposed routing scheme is shown in

fig. 5.5. The intermediate nodes receive the data of their corresponding nodes and

route it to the sink. As nodes send critical data to the nearest forwarding nodes,

so less energy is consumed and they can stay alive for longer time. Fig. 5.5 shows

that initially OINL and BEC have the same residual energy. However, after 5245

rounds the proposed scheme offers better residual energy curve than OINL due to

reactive routing strategy.

5.4.4 Path Loss

Path loss is the difference between the transmitted and received power represented

in decibels (dBs). The posture of the human body affects the electromagnetic

signals. As a result, the path loss shows different behaviours along different body

45

4000 5000 6000 7000 8000 9000 100000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Rounds

Res

idua

l ene

rgy

(J)

OINLBEC

Figure 5.5: Comparison of residual energy in BEC and OINL

parts. There are different models used to estimate the path loss which is a function

of distance and frequency as:

PL = PLo + 10.n.log10

(

d

do

)

+ σs (5.10)

Where, PLo is the path loss at reference distance do and n is the path loss ex-

ponent. The distance between the transmitter and the receiver is d and σs is the

standard deviation [64].

The path loss at reference distance do can be expressed as:

PLo = 10.log10

(

4.π.doλ

)2

(5.11)

Here, λ is the wavelength of the electromagnetic waves.

In our simulation, we use a fixed frequency (f) of 2.4 GHz from Industrial, Sci-

entific and Medical (ISM) radio band. We use the values of n and σs as 3.38 and

4.1, respectively.

Fig. 5.6 shows the path loss in each round for OINL and BEC. We observe that

after 5245 rounds the path loss exhibits continuous fluctuations. These fluctua-

tions are due to reactive routing in which data is not sent if it is not critical (i.e.

normal data). In this way, there is no path loss in some rounds and the path loss

curve goes to zero (see fig. 5.6). The improvement in the percentage provided by

46

4000 5000 6000 7000 8000 9000 100000

50

100

150

200

250

300

350

400

450

500

Rounds

Pat

h lo

ss (

dB)

OINLBEC

Figure 5.6: Comparison of path loss in BEC and OINL

BEC as compared to OINL is shown in table 5.4.

Table 5.4: Improvement in Percentage

Parameter Improvement (%) in OINL

Stability period 47.55Network lifetime 49

Network throughput 0.6Average residual energy 26.35

Average path loss 32.81

47

Chapter 6

Mobility Modeling for Wireless Body Area

Networks

48

6.1 Motivation

Mobility models have a big impact on the accuracy of simulations in WBANs.

Although a number of mobility models for ad-hoc networks are proposed in existing

literature, they are not suitable for WBAN because of its limited area and small

communication range.

• The models in WABNs use certain mobility models (like RPGM, etc.) for

moving the logical center of the group and the individual nodes. It is not

necessary that all the nodes in WBANs follow the logical center.

• The model in [65] does not specifically implement different postures of human

body. Postures are of great importance in WBANs as the network topology

may entirely change due to their changes.

6.2 Mobility Modeling

In WBANs, nodes are deployed on the human body to monitor different physio-

logical parameters like, blood pressure, temperature, heart beat level, etc. These

nodes send their sensed data to the sink placed on the chest of the human body.

The distances between nodes and sink are constant in static position. However,

as the human body is mobile in reality, so, the distance between node and sink

changes. Mobility models of WSNs are not suitable for WBANs due to limited

area and small communication range in the later. Furthermore, they do not con-

sider different postures of the human body. In this work, we consider different

postures and propose a method to calculate the distances between nodes and sink

when the human body is in motion.

We devise a mechanism consisting of two phases; (i) Posture selection phase and

(ii) Nodes’ movement phase. In the posture selection phase, a posture of the hu-

man body is selected like, standing, sitting, laying, walking, and running. The

probability of posture change can be determined from real human mobility traces.

However, we take probabilities of different postures from [65] as shown in fig. 6.1.

Markov chain in the figure shows the probability of posture change from one state

to another after a fixed time interval defined by the user. After posture change,

the new position of nodes is selected in the second phase. We assume that sink

is placed on the chest of human body and all positions of nodes are measured

relative to it. The following sections discuss the different postures of human body

in detail.

49

LAY SIT STAND WALK RUN

0.5 0.4 0.3 0.2 0.1

0.1 0.1 0.2

0.1 0.1 0.2

0.2

0.4

0.2

0.3

0.4

0.3

0.7

0.3

Figure 6.1: Markov model for posture pattern selection

6.2.1 Standing

In this position, the distances between nodes and sink are constant as body is in

static position.

6.2.2 Sitting

In this posture, we assume that the human body is sitting on a chair. In this

position, there is little movement of trunk of the human body. Most of the time,

the human arms and legs exhibit motion in three dimensions. We calculate the

positions of nodes placed on arms and legs. As nodes placed on arms show similar

behaviour, so, we calculate the position of a single node placed on elbow. Similarly,

we calculate the position of node placed on knee. The node e is placed on the

elbow while node k is placed on the knee.

The normal position of e in sitting position is given as:

Pe = P (ρe, θe, φe). (6.1)

Where, ρe is the radial distance, θe is polar angle and φe is azimuthal angle of

e from sink. During movement of the human arm, the maximum and minimum

distances between e and sink in sitting position are ρemax and ρemin, respectively.

So, the difference between these distances is:

de = ρemax − ρemin. (6.2)

We form a sphere at a distance of ρemax+ρemin

2from sink as shown in fig. 6.2. This

sphere has a radius of de2. Now, during movement, the node e will always lie in

50

this sphere. The new position of node e is calculated using the following equation.

ρe(t) = ρe(t− 1) + (ηe × rand(1)× ζe). (6.3)

Where, ηe is given as:

ηe =

[−1 0] if ρe(t) = ρemax

[0 1] if ρe(t) = ρemin

[−1 1] if ρemin < ρe(t) < ρemax

(6.4)

From eq. 6.3, it is clear that the new position of a node depends upon the previous

position. A random number is added to the current location to find new location.

If the new position of node goes out of bound then ηe will decrement the distance

between node and center of the sphere. On the other hand, if the distance between

node and sink approaches ρemin, then ηe will be positive and it increases the

distance (see eq. 6.4).

In eq. 6.3, ζe is the step size which can be adjusted according to the application.

Its value is always greater than zero. As the main concern in WBANs is the

distance between nodes and sink, so, we will not calculate other parameters like,

θe and φe. It should be kept in mind that these values will also change according

to eq. 6.3.

Now, we discuss the movement of node k placed on the knee of the human body.

The normal position of k in sitting position is given as:

Pk = P (ρk, θk, φk). (6.5)

Here, ρk is the normal distance of k from sink. θk and φk represent the polar and

azimuthal angles, respectively. During movement of human body, the maximum

and minimum distances between k and sink are denoted by ρkmax and ρkmin,

respectively.

dk = ρkmax − ρkmin. (6.6)

We form a sphere at a distance of ρkmax+ρkmin

2from sink as shown in fig. 6.2. This

sphere has radius of dk2. The node k always lie in this sphere during movement.

The new position of k is calculated using the following equation.

ρk(t) = ρk(t− 1) + (ηk × rand(1)× ζk). (6.7)

51

The value of ηk is calculated using:

ηk =

[−1 0] if ρk(t) = ρkmax

[0 1] if ρk(t) = ρkmin

[−1 1] if ρkmin < ρk(t) < ρkmax

(6.8)

The new position of k is calculated using eq. 6.7 where ηk is a random number

which is calculated using eq. 6.8. ζk represents the step size and is adjusted

according to the application. Its value is always greater than zero.

Sphere of

Radius

de/2

Sphere of

Radius

dk/2

Figure 6.2: Human body in sitting position

6.2.3 Walking

During walking, the arms and legs of human show repetitive and similar movement

patterns. When the left arm moves forward, the right leg also moves in the

forward direction. Similarly, right arm and left leg are synchronized. This defined

trajectory helps to efficiently model the mobility of human body. When the body

moves from static position, the new position of sink is given as:

Ps = P (ρs, θs, φs). (6.9)

52

Where, ρs is calculated as:

ρs(t) = ρo + tu. (6.10)

Where, u denotes the speed of the human and t is the time after which we are

calculating new position. ρo denotes the initial position of sink.

The normal position of node e is given as:

Pe = P (ρe, θe, φe). (6.11)

Let us denote the distances between sink and node in forward and backward

positions by ρfronte and ρbacke , respectively. We assume their magnitudes are same.

So, we form a curve between ρfronte and ρbacke as shown in fig. 6.3. The node e will

move along this curve and its position at any time t is calculated as:

ρe(t) = ρe(t− 1) + ηede. (6.12)

Where, de is calculated as:

de = ρfronte − ρe = ρbacke − ρe. (6.13)

The value of ηe changes with time as shown in fig. 6.6. Its value ranges from 0 to

1.

Now, we see the movement of nodes placed on legs. The normal position of node

k is given as:

Pk = P (ρk, θk, φk). (6.14)

During walking, the legs move in the forward and backward directions. We denote

the distances between sink and node k in forward and backward directions by ρfrontk

and ρbackk , respectively. We assume that these two distances are same and form a

curve between them as shown in fig. 6.3. The moving node k always lies on this

curve. The new position of k is calculated as:

ρk(t) = ρk(t− 1) + ηkdk. (6.15)

In the above equation, dk is calculated as:

dk = ρfrontk − ρk = ρbackk − ρk. (6.16)

The value of ηk changes with time as shown in fig. 6.6.

53

��

��

��

��

Figure 6.3: Human body in walking position

6.2.4 Running

In the running position, there are repetitive movements of certain limbs of the

body such as arms and legs, similar to walking position. The arms and legs

undergo continuous movements in forward and backward directions. We find the

position of nodes placed on arms and legs of the human body during running. In

the running position, sink also changes its position in each time interval.

The new position of sink at time t is given as:

Ps = P (ρs, θs, φs). (6.17)

Where, ρs is calculated as:

ρs(t) = ρo + tu. (6.18)

Where, ρo is the initial position of sink, u is its speed and t the time after which

its new position is calculated. The normal position of node e in running position

is given as:

Pe = P (ρe, θe, φe). (6.19)

During running, the node e moves in the forward and backward direction con-

tinuously. Let ρfronte and ρbacke denote the distances of sink from node in forward

and backward directions. We assume that both of these distances are same. So,

54

we form a curve centered at ρe having length of ρfronte − ρe in each direction (i.e.

forward and backward) as shown in fig. 6.4. During running, the nodes always lie

on this curve. The position of e at any time instant is calculated as:

ρe(t) = ρe(t− 1) + ηede. (6.20)

In the above equation, de is calculated as:

de = ρfronte − ρe = ρbacke − ρe. (6.21)

The value of ηe changes with time as shown in fig. 6.6.

Now, we discuss the movement of node k placed on right knee. The normal position

of node k is given as:

Pk = P (ρk, θk, φk). (6.22)

Let ρfrontk and ρbackk denote the distances between sink and node in forward and

backward directions. We assume that these two distances are equal. We form a

curve centered at ηk having length of ρfrontk −ρk in each direction (i.e. forward and

backward) as shown in fig. 6.4. The node always moves along this curve during

motion. Its position at any time t is calculated as:

ρk(t) = ρk(t− 1) + ηkdk. (6.23)

In the above equation, dk is calculated as:

dk = ρfrontk − ρk = ρbackk − ρk. (6.24)

The value of ηk changes with time as shown in fig. 6.6.

6.2.5 Laying

In the laying position, nodes placed on the trunk of the human body are minimally

mobile. On the other hand, nodes placed on arms and legs are mobile. The normal

position of node e placed on the elbow is given as:

Pe = P (ρe, θe, φe). (6.25)

Let us denote the minimum and maximum distances between sink and node e by

ρemin and ρemax, respectively. So, we make a sphere at distance of ρemax+ρemin

2from

sink and having radius of ρemax−ρemin

2as shown in fig. 6.5. Now, the position of

55

��

��

��

��

Figure 6.4: Human body in running position

node is calculated using following equation:

ρe(t) = ρe(t− 1) + (ηe × rand(1)× ζe). (6.26)

Where, ηe is given as:

ηe =

[−1 0] if ρe(t) = ρemax

[0 1] if ρe(t) = ρemin

[−1 1] if ρemin < ρe(t) < ρemax

(6.27)

In eq. 6.26, ζe is the step size and its value is always greater than zero. Now, we

calculate the new position of node k in laying position. The normal position of

node k in standing position is given as:

Pk = P (ρk, θk, φk). (6.28)

During laying, the nodes placed on legs show random mobility. Let the maximum

and minimum distances between sink and node k are denoted by ρkmax and ρkmin

respectively. We form a sphere at distance of ρkmax+ρkmin

2from sink and having

radius of ρkmax−ρkmin

2, as shown in fig. 6.5. The new position of node is calculated

56

as:

ρk(t) = ρk(t− 1) + (ηk × rand(1)× ζk). (6.29)

Here, the value of ηk is calculated as:

ηk =

[−1 0] if ρk(t) = ρkmax

[0 1] if ρk(t) = ρkmin

[−1 1] if ρkmin < ρk(t) < ρkmax

(6.30)

In eq. 6.29, ζk is the step size and its value is always greater than zero. It

determines the distance covered in a single time interval. In laying position, larger

value of ζk is selected as nodes move suddenly to larger distances and, after longer

time intervals.

Sphere radius

Sphere radius

(ρemax- ρemin)/2

(ρkmax- ρkmin)/2

Figure 6.5: Human body in laying position

time

1

��

Figure 6.6: Value of ηe and ηk

57

6.3 Impact of Mobility in WBANs

During the movement of human body, nodes move to different positions and there-

fore, distance between sink and node changes. It affects the energy consumption

of nodes, propagation delay and path loss of the signal. In the following sections

we discuss them in detail.

6.3.1 Energy Consumption

As shown in the mobility model, the distance between nodes and sink changes

with the movement of human body. As a result, transmission energy consumption

of nodes changes as given in [60]:

ETX(k, d) = ETX−elec × k + ǫamp × k × d2. (6.31)

Where, ETX is the transmission energy, ETX−elec is the energy required to run

the electronic circuit and ǫamp is the energy required to run the amplifier. k is

the packet size and d is the distance between sink and node. It is clear from eq.

5.31 and fig. 6.7 that as the distance between node and sink increases, the energy

consumption also increases.

0 0.5 1 1.5 22

2.05

2.1

2.15

2.2

2.25

2.3

2.35x 10

−4

Distance between node and sink (m)

Ene

rgy

cons

umpt

ion

of n

odes

(J)

Figure 6.7: Effect of distance on energy consumption of nodes

58

6.3.2 Delay

It is time required by a signal to reach from source to destination. Distance

between sink and node affects the delay as:

delay =d

c. (6.32)

Where, d is the distance between sink and node and c is the speed of electromag-

netic waves. Delay increases with the increase in distance as shown in fig. 6.8.

0 0.5 1 1.5 20

1

2

3

4

5

6

7x 10

−9


Del

ay (

s)

Figure 6.8: Effect of distance on delay

6.3.3 Path loss

Path loss is the reduction in power density of a wave as it propagates through

space. It depends on distance as given in [62]:

PL(f, d) = PLo + 10nlog10

(

d

do

)

+ σs . (6.33)

Where, PLo is the path loss at reference distance do and n is the path loss exponent

whose value varies from 4 to 7 for human body. d is the distance between node

and sink (transmitter and receiver) and σs is the standard deviation.

59

PLo is given as:

PLo = 10log10

(

4πd

λ

)2

. (6.34)

Similarly, it can be written as:

PLo = 10log10

(

4πdf

c

)2

. (6.35)

Where, f is the frequency, λ is the wavelength of the propagating wave and c is the

speed of light. We use frequency of 2.4 GHz from ISM band. Path loss increases

with the increase in distance as shown in fig. 6.9.

It is obvious from the above discussion that distance between nodes and sink

0 0.5 1 1.5 20

100

200

300

400

500

600


Pat

h lo

ss (

dB)

Figure 6.9: Effect of distance on path loss

affects the energy consumption, delay and path loss. So, in order to find these pa-

rameters correctly, we propose and implement the mobility model in our protocol.

In this way, it gives more accurate and realistic results.

6.4 Implementation of Mobility Model in the Routing Protocols

Wireless Body Area Network (WBAN) consists of nodes placed on the human

body to monitor different vital signs like heart rate, glucose level, blood oxygen

level, etc. We propose two new routing protocols and discuss their advantages and

disadvantages. We discuss their functionality in the following sections in detail.

60


Energy consumed in single-hop communication is given as:

ESH = ETX . (6.36)

Where, ETX is the transmission energy which is calculated as:

ETX = k × (ETXelect + εamp)× d2 . (6.37)

k is packet size, ETXelect is energy consumed by the electronic circuit, εamp is the

amplification energy and d is the distance between transmitter and receiver.

Energy consumed in multi-hop communication is given as:

EMH = k × (h× ETX + (h− 1)(ERX + EDA)) . (6.38)

Where, h is the number of hops, ERX is the energy consumed in receiving the data

and EDA is the data aggregation energy.

6.6 Multi-hop Technique

In multi-hop routing technique, data is transmitted using neighbouring nodes.

Fig. 6.10 shows the placement of nodes on human body. In multi-hop scheme,

node 4 sends data to node 1 and node 3 sends data to node 2. Similarly, nodes

7 and 8 send their data to nodes 5 and 6 respectively. The receiving nodes (i.e.

nodes 1, 2, 5 and 6) send the aggregated data to sink. If these receiving nodes

become dead then the other nodes send their data directly to the sink as shown in

fig. 6.11. In this scheme, the far away nodes send data to their neighboring nodes

and thus save energy. However, the drawback of this scheme is that nodes near

the sink are burdened with heavy load. They consume extra energy in aggregating

and receiving the data from other nodes. In this way, they deplete their energy

soon, and become dead nodes.

6.7 Data Transmission using Forwarder Nodes

In this routing technique, forwarder nodes are selected in each round. These

forwarders receive data from their respective group members and forward it to the

sink. Fig. 6.10 shows the placement of nodes on the human body. We discuss this

protocol in the following sections in detail.

61

2

8

7

6

5

Sink

Node

34

1

Figure 6.10: Placement of nodes on the human body

6.7.1 Initialization phase

In this phase, sink broadcasts a HELLO message containing the following infor-

mation:

• Location of sink.

• Location of neighbors.

• Information about all possible routes to the sink.

All nodes receive this HELLO message and update their routing table.

6.7.2 Forwarders’ selection phase

In this phase, forwarders are selected to route the data of other nodes. We divide

N number of nodes into two sets; A and B, based on their distance from sink,

which are given as:

N = {1, 2, 3, 4, 5, 6, 7, 8} (6.39)

62

2

8

7

6

5

e

e

8

7

6

5

34

1

Normal data routing Routing after the death of nodes near the sink

Figure 6.11: Network flow tree in multi-hop routing scheme

A = {1, 2, 3, 4} (6.40)

B = {5, 6, 7, 8} (6.41)

In the forwarders’ selection phase, two forwarder nodes are selected (one from each

group) on the basis of cost functions C.FA and C.FB, which are calculated as:

C.FA =d(i)

R.E(i). i ∈ A (6.42)

C.FB =d(i)

R.E(i). i ∈ B (6.43)

The node having minimum value of C.FA is selected as a forwarder node from

group A. Similarly, the node having minimum value of C.FB is selected as for-

warder node from group B. These forwarder nodes collect the data from their

respective group members and send it to the sink.

63

6.7.3 Scheduling phase

In the scheduling phase, forwarders assign Time Division Multiple Access (TDMA)

based time slots to their children nodes. All the nodes transmit in their scheduled

time slots to avoid collision.

6.7.4 Data transmission phase

In the data transmission phase, nodes transmit data to their respective forwarder

nodes in their scheduled time slots. Forwarder nodes receive data from their

children nodes, aggregate it and route it to the sink. If a node has less energy

than a threshold (τ), it does not take part in forwarders’ selection and routes its

data directly to the sink. This is incorporated to save the data aggregation energy

of low energy nodes. If a node has less distance to the sink than forwarder then it

routes its data directly to the sink. Fig. 6.12 shows the network tree for forwarder

based routing technique. In the initial rounds, nodes send data to their respective

forwarders which route it to the sink. However, after some rounds, some nodes

may have less energy than others as shown in fig. 6.12. For example, if node 5

has less energy than τ , it sends its data directly to the sink and all other nodes

route their data through forwarder nodes.

6.8 Simulation Results and Analysis

We simulate the proposed protocols and analyze their results. Table 5.1 shows

the simulation parameters and their values. We implement the proposed mobility

model in the two routing protocols and assume the values of ρe and ρk as 0.15

and 0.20, respectively. We ignore the sensing energy consumed by the nodes in

simulation. The initial energy (Eo) of all nodes is 0.5 J. The simulations are run

five times and their average results are plotted.

6.8.1 Network lifetime

Network lifetime represents the time from the start of network till the death of

last node. On the other hand, the time from start of the network till the death

of first node is called stability period. Fig. 6.13 shows the comparison of number

of dead nodes and fig. 6.14 shows the comparison of stability period and network

lifetime. Forwarders based routing protocol has larger stability period and network

lifetime. It is due to the fact that new forwarders are selected in each round and

64

2

8

7

6

5

rder of set A

34

rder

τ

2

8

7

6

5

34

tree

in initial rounds

tree after

the death of node 8

Figure 6.12: Network flow tree in forwarder based routing scheme

the load is uniformly distributed to all the nodes. On the other hand, in multi-

hop routing protocol, nodes near the sink are heavily burdened and consume more

energy in the form of reception and data aggregation energy. As a result, these

nodes die quickly. Multi-hop routing protocol has stability period of 1191 rounds

and network lifetime of about 2500 rounds. On the other hand, forwarders based

routing scheme has stability period of 3913 rounds and network lifetime of 6878

rounds as shown in fig. 6.14.

6.8.2 Throughput

Throughput shows the number of packets successfully received at sink. A protocol

having longer network lifetime sends more packets to the sink and have higher

throughput. Fig. 6.15 shows the number of packets sent to the sink in the multi-

hop and forwarders based routing protocols. As forwarders based routing protocol

has longer network lifetime (see fig. 6.14 ), so, it sends more packets to the sink. All

of the sent packets are not successfully received at sink. We use random uniformed

model [64] to calculate the number of dropped and received packets. The status

65




EDA 5 nJ/bit/signaldo 0.1 mτ 0.2 Jk 4000 bitsf 2.4 GHzEo 0.5 J

0 1000 2000 3000 4000 5000 6000 7000 80000

1

2

3

4

5

6

7

8

Rounds

No.

of d

ead

node

s

Multi−hopForwarder based

Figure 6.13: Comparison of number of dead nodes in multi-hop and forwarder basedrouting techniques

of the communication link can be good or bad. We assume the probability of 0.7

for link to be good. Figs. 6.16 and 6.17 show the number of packets dropped and

successfully received at sink, respectively. It is clear from fig. 6.17 that multi-

hop routing technique continue sending packets to sink till 2500 rounds whereas

forwarders based routing technique sends data to the sink till 6878 rounds.

6.8.3 Residual energy

Comparison of residual energy of the multi-hop and forwarders based routing

protocols is shown in fig. 6.18. As nodes near the sink consume more energy in

multi-hop routing, so, they deplete their energy soon. On the other hand, nodes in

66

Figure 6.14: Comparison of stability period and network lifetime in multi-hop andforwarder based routing techniques

the forwarder based routing protocol consume less energy and stay alive for longer

time. Fig. 6.18 shows the gradual decrease in the residual energy of forwarder

based routing protocol. Whereas, in the multi-hop routing protocol the residual

energy decreases more quickly.

6.8.4 Delay

Delay is the time required by a signal to reach from source to destination. It varies

according to the distance between source and destination as given in eq. 6.32. Fig.

6.19 shows the delay for multi-hop and forwarders based routing protocols. It is

clear from the figure that multi-hop routing protocol has less delay as compared to

forwarders based routing protocol. It is due to the reason that in multi-hop routing

technique, nodes send their data using neighbouring nodes. As these neighbouring

nodes are located at a small distance, therefore, less delay is occurred. On the other

hand, in forwarders based routing protocol, nodes send their data to a forwarder

which can be located at a large distance. As new forwarders are selected in each

round, therefore, they may be located far away from other nodes. As a result,

large delay will occur due to larger distance between source and destination. The

fluctuations in delay are due to the different distances between nodes and sink as

the body is mobile. During routine activities, body exhibits different postures and

the distances between nodes and sink vary according to that posture. As delay

depends on distance, so, it changes in each round. Furthermore, delay starts

decreasing after 3913 rounds in forwarders based routing protocol as nodes start

dying and therefore, less number of nodes have lower cumulative delay. Similarly,

67

0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 10

4

Rounds

No.

of p

acke

ts s

ent t

o si

nk


Figure 6.15: Comparison of packets sent to sink (aggregated) in multi-hop and for-warder based routing techniques

delay in multi-hop routing decreases after 1191 rounds due to the death of four

nodes (see fig. 6.13 ) as shown in fig. 6.19.

6.8.5 Path loss

Path loss is the reduction in power density of an electromagnetic wave as it propa-

gates through a medium. It depends on distance and frequency as give in eq. 6.33.

Fig. 6.20 shows the path loss of the two proposed protocols. Forwarders based

routing protocol has more path loss than multi-hop routing. This is due to the

larger distance between nodes and their corresponding forwarders, as they change

in each round. The fluctuations in path loss are due to varying distances between

nodes and sink as the human body is mobile. During daily activities, body shows

different postures and distances between nodes and sink vary according to that

posture. Path loss starts decreasing after 3913 rounds due to the death of some

nodes in forwarders based routing. Similarly, path loss in multi-hop routing de-

creases after 1191 rounds due to the death of four nodes (see fig. 6.13 ) as shown

in fig. 6.20.

68

0 1000 2000 3000 4000 5000 6000 7000 80000

2000

4000

6000

8000

10000

12000

14000

Rounds

No.

of d

ropp

ed p

acke

ts


Figure 6.16: Comparison of dropped packets (aggregated) in multi-hop and forwarderbased routing techniques

6.8.6 Energy consumption

Fig. 6.21 shows the comparison of energy consumed in each round in multi-

hop and forwarders based routing protocols. In multi-hop routing protocol, more

energy is consumed in the form of reception and data aggregation. On the other

hand, in the forwarders based routing technique, energy is consumed uniformly

as forwarders are changed in each round. As load is uniformly distributed on

all the nodes, so, they consume less energy and remain alive for longer time. In

multi-hop routing technique, less energy is consumed after 1191 rounds due to the

lower number of alive nodes. Similarly, energy consumption in forwarders based

routing protocol decreases after 3913 rounds due to the death of some nodes. The

fluctuations in energy consumption are due to different distances between nodes

and sink during the movement of human body. As we have implemented the

proposed mobility model, so, coordinates of nodes change in each round according

to the selected posture. The comparison of average energy consumption in multi-

hop and forwarders based routing technique is shown in fig. 6.22. It shows that

multi-hop routing scheme consumes more energy as compared to forwarders based

routing technique.

69

0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1

1.5

2

2.5

3

3.5x 10

4

Rounds

No.

of p

acke

ts r

ecei

ved

at s

ink


Figure 6.17: Comparison of received packets (aggregated) in multi-hop and forwarderbased routing techniques

0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1

1.5

2

2.5

3

3.5

4

Rounds

Res

idua

l ene

rgy

(J)


Figure 6.18: Comparison of residual energy in multi-hop and forwarder based routingtechniques

70

0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1

1.5

2

2.5x 10

−8

Rounds

Del

ay (

s)


Figure 6.19: Comparison of delay in multi-hop and forwarder based routing techniques

0 1000 2000 3000 4000 5000 6000 7000 80000

100

200

300

400

500

600

Rounds

Pat

h lo

ss (

dB)


Figure 6.20: Comparison of path loss in multi-hop and forwarder based routing tech-niques

71

0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1

1.5

2

2.5x 10

−3

Rounds

Ene

rgy

cons

umpt

ion

(J)


Figure 6.21: Comparison of energy consumption in multi-hop and forwarder basedrouting techniques

Multi−hop routing Forwarders based routing0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Ave

rag

e e

ne

rgy

con

sum

ptio

n p

er

rou

nd

(m

J)

Figure 6.22: Comparison of average energy consumption in multi-hop and forwarderbased routing techniques

72

Chapter 7

Conclusion and Future Work

73

In this thesis, we have proposed a mobility model for human body and three new

energy efficient routing protocols; FEEL, REEC and BEC. The proposed mobility

model considers different postures of human body such as: standing, walking,

running, sitting and laying. The proposed mobility model shows that nodes have

different movement pattern in each of these postures. The distance between node

and sink significantly affects the energy consumption, delay, and path loss. Due to

efficient forwarder selection, our first proposed protocol, FEEL, minimizes energy

consumption of nodes thereby increasing the stability period. Energy efficient

forwarder selection makes FEEL protocol more suitable for continuous monitoring

of patients in comparison to the selected routing protocols. However, performance

of FEEL degrades whenever critical data reporting based applications are taken

into consideration. REEC, our second proposed protocol, is specifically designed

to meet this requirement. It selects two forwarder nodes to route data to the

sink. Our third proposed scheme, BEC, enhances the network lifetime using relay

nodes. In BEC, nodes send their data to intermediate nodes which route it to the

sink. The nodes closer to the sink send their data directly to it. In the proposed

scheme, relay nodes are selected dynamically based on a cost function. The nodes

send only critical data when their energy becomes less than a specific threshold.

Therefore, nodes do not deplete their energy quickly and stay alive for a longer

period of time. Simulation results show that FEEL and REEC have 27% and 25%

improved stability period as compared to SIMPLE, respectively. Similarly, BEC

achieves 49% improved network lifetime than OINL scheme.

In future, we will deploy the sensor nodes on human body and monitor the move-

ment of different body parts. Furthermore, development of probabilistic posture

transition model based on real human mobility traces is under consideration.

74

Chapter 8

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82

Chapter 9

List of Publications

83

1. M. M. Sandhu, N. Javaid, M. Jamil, Z. A. Khan, M. Imran, M. Ilahi, M. A.

Khan, “Modeling Mobility and Psychological Stress based Human Postural

Changes in Wireless Body Area Networks”, Computers in Human Behavior,

DOI: 10.1016/j.chb.2014.09.032, 2014.

2. S. Ahmed, M. M. Sandhu, N. Amjad, A. Haider, M. Akbar, A. Ahmad, Z. A.

Khan, U. Qasim, N. Javaid, “iMOD LEACH: improved MODified LEACH

Protocol for Wireless Sensor Networks”, Journal of Basic and Applied Sci-

entific Research, 3(10)25–32, 2013.

3. A. Haider, M. M. Sandhu, N. Amjad, S. H. Ahmed, M. J. Ashraf, A. Ahmed,

Z. A. Khan, U. Qasim, N. Javaid, “REECH-ME: Regional Energy Efficient

Cluster Heads based on Maximum Energy Routing Protocol with Sink Mo-

bility in WSNs”, Journal of Basic and Applied Scientific Research, 4(1)200–

216, 2014.

4. N. Amjad, M. M. Sandhu, S. H. Ahmed, M. J. Ashraf, A. A. Awan, U.

Qasim, Z. A. Khan, M. A. Raza, N. Javaid, “DREEM-ME: Distributed

Regional Energy Efficient Multi-hop Routing Protocol based on Maximum

Energy with Mobile Sink in WSNs”, Journal of Basic and Applied Scientific

Research, 4(1)289–306, 2014.

5. M. M. Sandhu, N. Javaid, M. Akbar, F. Najeeb, U. Qasim, Z. A. Khan,

“FEEL: Forwarding Data Energy Efficiently with Load Balancing in Wire-

less Body Area Networks”, The 28th IEEE International Conference on

Advanced Information Networking and Applications (AINA-2014), Victoria,

Canada.

6. M. M. Sandhu, M. Akbar, M. Behzad, N. Javaid, Z. A. Khan, U. Qasim,

“REEC: Reliable Energy Efficient Critical data routing in wireless body area

networks”, The 9th International Conference on Broadband and Wireless

Computing, Communication and Applications (BWCCA 2014), Guangzhou,

China.

7. M. M. Sandhu, M. Akbar, M. Behzad, N. Javaid, Z. A. Khan, U. Qasim,

“Mobility Model for WBANs”, The 9th International Conference on Broad-

band and Wireless Computing, Communication and Applications (BWCCA

2014), Guangzhou, China.

8. Mohsin Raza Jafri, Muhammad Moid Sandhu, Kamran Latif, Zahoor Ali

Khan, Ansar Ul Haque Yasar, Nadeem Javaid, “Towards Delay-Sensitive

Routing in Underwater Wireless Sensor Networks”, The 5th International

Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-

84

2014), Halifax, Nova Scotia, Canada.

9. Ashfaq Ahmad, Muhammad Babar Rasheed, Muhammad Moid Sandhu, Za-

hoor Ali Khan, Ansar Ul Haque Yasar, Nadeem Javaid, “Hop Adjusted

Multi-chain Routing for Energy Efficiency in Wireless Sensor Networks”,

The 5th International Conference on Emerging Ubiquitous Systems and Per-

vasive Networks (EUSPN-2014), Halifax, Nova Scotia, Canada.

85

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