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
Mr. Muhammad Moid Sandhu
CIIT/FA12-REE-049/ISB
Fall, 2014
iii
Mobility Modeling for Efficient Data Routing in Wireless
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
CIIT/FA12-REE-049/ISB
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
Mobility Modeling for Efficient Data Routing in Wireless
Body Area Networks
By
Mr. Muhammad Moid Sandhu
CIIT/FA12-REE-049/ISB
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: ________________
________________
Mr. Muhammad Moid Sandhu
CIIT/FA12-REE-049/ISB
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.
Mr. Muhammad Moid Sandhu
CIIT/FA12-REE-049/ISB
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
4.3.4 Scheduling Phase . . . . . . . . . . . . . . . . . . . . . . . . 32
4.3.5 Data Transmission Phase . . . . . . . . . . . . . . . . . . . . 32
4.4 Energy Consumption Analysis . . . . . . . . . . . . . . . . . . . . . 32
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
5.3.5 Scheduling Phase . . . . . . . . . . . . . . . . . . . . . . . . 43
5.3.6 Data Transmission Phase . . . . . . . . . . . . . . . . . . . . 43
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
6.5 Energy Consumption Analysis . . . . . . . . . . . . . . . . . . . . . 61
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-
warder based routing techniques . . . . . . . . . . . . . . . . . . . . 70
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-
warder based routing techniques . . . . . . . . . . . . . . . . . . . . 72
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
4.1 Energy Parameters of Transceivers . . . . . . . . . . . . . . . . . . 29
4.2 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.3 Improvement in Percentage . . . . . . . . . . . . . . . . . . . . . . 37
5.1 Energy Parameters of Transceivers . . . . . . . . . . . . . . . . . . 39
5.2 Distances of nodes from the sink . . . . . . . . . . . . . . . . . . . . 41
5.3 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 43
5.4 Improvement in Percentage . . . . . . . . . . . . . . . . . . . . . . 47
6.1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 66
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
FEELSIMPLEM−ATTEMPT
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
FEELSIMPLEM−ATTEMPT
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
Parameter Improvement (%) Improvement (%)
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
FEELSIMPLEM−ATTEMPT
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)
FEELSIMPLEM−ATTEMPT
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)
FEELSIMPLEM−ATTEMPT
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)
FEELSIMPLEM−ATTEMPT
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)
FEELSIMPLEM−ATTEMPT
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.
Table 4.1: Energy Parameters of Transceivers
Parameter nRF 2401A CC2420 Units
DC current (TX) 10.5 17.4 mADC current (RX) 18 19.7 mA
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
4.3.2 Start-up Phase
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
4.3.4 Scheduling Phase
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.
4.3.5 Data Transmission Phase
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.
4.4 Energy Consumption Analysis
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
Table 4.2: Simulation Parameters
Parameter Value Units
∆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
REECSIMPLEM−ATTEMPT
Figure 4.4: Comparison of network throughput (aggregated) in REEC, SIMPLE andM-ATTEMPT
4.5.3 Residual Energy
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)
REECSIMPLEM−ATTEMPT
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)
REECSIMPLEM−ATTEMPT
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
Parameter Improvement (%) Improvement (%)
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.
Table 5.1: Energy Parameters of Transceivers
Parameter nRF 2401A CC2420 Units
DC current (TX) 10.5 17.4 mADC current (RX) 18 19.7 mA
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
5.3.3 Start-up Phase
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).
5.3.5 Scheduling Phase
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.
5.3.6 Data Transmission Phase
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.
Table 5.3: Simulation Parameters
Parameter Value Units
ERXelect 36.1 nJ/bitETXelect 16.7 nJ/bitεamp 1.97 nJ/bit/m2
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
5.4.3 Residual Energy
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
Distance between node and sink (m)
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
Distance between node and sink (m)
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
6.5 Energy Consumption Analysis
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
Table 6.1: Simulation Parameters
Parameter Value Units
ERXelect 36.1 nJ/bitETXelect 16.7 nJ/bitεamp 1.97 nJ/bit/m2
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
Multi−hopForwarder based
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
Multi−hopForwarder based
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
Multi−hopForwarder based
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)
Multi−hopForwarder based
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)
Multi−hopForwarder based
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)
Multi−hopForwarder based
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)
Multi−hopForwarder based
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
References
75
[1] http : //finance.gov.pk/budget/abs 2013 14.pdf
(Accessed on 16-MAY-2014)
[2] E. Jovanov, A. Milenkovic, C. Otto, and P. C. De Groen, “A wireless body
area network of intelligent motion sensors for computer assisted physical
rehabilitation,” Journal of NeuroEngineering and rehabilitation, vol. 2, no.
1, p. 6, 2005.
[3] A. Ehyaie, M. Hashemi, and P. Khadivi, “Using relay network to increase
life time in wireless body area sensor networks,” World of Wireless, Mobile
and Multimedia Networks & Workshops, WoWMoM, pp. 1–6, 2009.
[4] J. Elias and A. Mehaoua, “Energy-aware topology design for wireless body
area networks,” IEEE International Conference on Communications (ICC),
pp. 3409–3410, 2012.
[5] B. Braem, B. Latre, I. Moerman, C. Blondia, E. Reusens, W. Joseph, L.
Martens, and P. Demeester, “The need for cooperation and relaying in short-
range high path loss sensor networks,” International Conference on Sensor
Technologies and Applications (SensorComm), pp. 566–571, 2007.
[6] B. Chen, J. P. Varkey, D. Pompili, J.-J. Li, and I. Marsic, “Patient Vi-
tal Signs Monitoring using Wireless Body Area Networks,” Proceedings of
the 2010 IEEE 36th Annual Northeast Bioengineering Conference, pp. 1–2,
2010.
[7] S.-H. Seo, S. Gopalan, S.-M. Chun, K.-J. Seok, J.-W. Nah, and J.-T. Park,
“An energy-efficient configuration management for multi-hop wireless body
area networks,” 2010 3rd IEEE International Conference on Broadband Net-
work and Multimedia Technology (IC-BNMT), pp. 1235–1239, 2010.
[8] T. Watteyne, I. Auge-Blum, M. Dohler, and D. Barthel, “Anybody: a self-
organization protocol for body area networks,” Proceedings of the ICST 2nd
international conference on Body area networks, p. 6, 2007.
[9] C. A. Otto, E. Jovanov, and A. Milenkovic, “A WBAN-based system for
health monitoring at home,” 3rd IEEE/EMBS International Summer School
on Medical Devices and Biosensors, pp. 20–23, 2006.
[10] C. Wang, Q. Wang, and S. Shi, “A distributed wireless body area network for
medical supervision,” 2012 IEEE International Instrumentation and Mea-
surement Technology Conference (I2MTC), pp. 2612–2616, 2012.
[11] M. Quwaider and S. Biswas, “On-body packet routing algorithms for body
sensor networks,” Proceedings of the 2009 First International Conference on
76
Networks & Communications, pp. 171–177, 2009.
[12] A. Tauqir, N. Javaid, S. Akram, A. Rao, and S. Mohammad, “Distance
Aware Relaying Energy-efficient: DARE to Monitor Patients in Multi-hop
Body Area Sensor Networks,” 2013 Eighth International Conference on Broad-
band and Wireless Computing, Communication and Applications (BWCCA),
pp. 206–213, 2013.
[13] S. Akram, N. Javaid, A. Tauqir, A. Rao, and S. Mohammad, “THE-FAME:
THreshold Based Energy-Efficient FAtigue MEasurement for Wireless Body
Area Sensor Networks Using Multiple Sinks,” 2013 Eighth International
Conference on Broadband and Wireless Computing, Communication and Ap-
plications (BWCCA), pp. 214–220, 2013.
[14] N. Javaid, S. Faisal, Z. Khan, D. Nayab, and M. Zahid, “Measuring Fatigue
of Soldiers in Wireless Body Area Sensor Networks,” 2013 Eighth Interna-
tional Conference on Broadband and Wireless Computing, Communication
and Applications (BWCCA), pp. 227–231, 2013.
[15] S. Ivanov, C. Foley, S. Balasubramaniam, and D. Botvich, “Virtual groups
for patient WBAN monitoring in medical environments,” IEEE Transactions
on Biomedical Engineering, vol. 59, no. 11, pp. 3238–3246, 2012.
[16] G. R. Tsouri, A. Prieto, and N. Argade, “On increasing network lifetime in
body area networks using global routing with energy consumption balanc-
ing,” Sensors, vol. 12, no. 10, pp. 13088–13108, 2012.
[17] B. Latre, B. Braem, I. Moerman, C. Blondia, E. Reusens, W. Joseph, and P.
Demeester, “A low-delay protocol for multihop wireless body area networks,”
Fourth Annual International Conference on Mobile and Ubiquitous Systems:
Networking & Services, MobiQuitous, pp. 1–8, 2007.
[18] N. Ababneh, N. Timmons, J. Morrison, and D. Tracey, “Energy-balanced
rate assignment and routing protocol for body area networks,” 26th Inter-
national Conference on Advanced Information Networking and Applications
Workshops (WAINA), pp. 466–471, 2012.
[19] E. Reusens, W. Joseph, B. Latre, B. Braem, G. Vermeeren, E. Tanghe, L.
Martens, I. Moerman, and C. Blondia, “Characterization of on-body commu-
nication channel and energy efficient topology design for wireless body area
networks,” IEEE Transactions on Information Technology in Biomedicine,
vol. 13, no. 6, pp. 933–945, 2009.
[20] A. A. Abbasi and M. Younis, “A survey on clustering algorithms for wireless
77
sensor networks,” Computer communications, vol. 30, no. 14, pp. 2826–
2841, 2007.
[21] M. R. Senouci, A. Mellouk, H. Senouci, and A. Aissani, “Performance eval-
uation of network lifetime spatial-temporal distribution for WSN routing
protocols,” Journal of Network and Computer Applications, vol. 35, no. 4,
pp. 1317–1328, 2012.
[22] S. Fouchal, D. Mansouri, L. Mokdad, J. Ben-Othman, and M. Ioualalen,
“Clustering wireless sensors networks with FFUCA,” 2013 IEEE Interna-
tional Conference on Communications (ICC), pp. 6438–6443, 2013.
[23] J. Wan, S. Ullah, C. Lai, M. Zhou, X. Wang, and C. Zou, “Cloud-enabled
wireless body area networks for pervasive healthcare,” IEEE Network, vol.
27, no. 5, pp. 56–61, 2013.
[24] M.-A. Koulali, A. Kobbane, M. El Koutbi, H. Tembine, and J. Ben-Othman,
“Dynamic power control for energy harvesting wireless multimedia sensor
networks,” EURASIP Journal on Wireless Communications and Network-
ing, vol. 2012, no. 1, pp. 1–8, 2012.
[25] J. Ben-Othman, K. Bessaoud, A. Bui, and L. Pilard, “Self-stabilizing algo-
rithm for efficient topology control in Wireless Sensor Networks,” Journal of
Computational Science, vol. 4, no. 4, pp. 199–208, 2013.
[26] S.-H. Han and S. K. Park, “Performance analysis of wireless body area net-
work in indoor off-body communication,” IEEE Transactions on Consumer
Electronics, vol. 57, no. 2, pp. 335–338, 2011.
[27] S. Ivanov, D. Botvich, and S. Balasubramaniam, “Cooperative wireless sen-
sor environments supporting body area networks,” IEEE Transactions on
Consumer Electronics, vol. 58, no. 2, pp. 284–292, 2012.
[28] D. Mansouri, L. Mokdad, J. Ben-othman, and M. Ioualalen, M, “Detect-
ing DoS attacks in WSN based on clustering technique,” 2013 IEEE Wire-
less Communications and Networking Conference (WCNC), pp. 2214–2219,
2013.
[29] A. Mellouk, S. Hoceini, and S. Zeadally, “A state-dependent time evolving
multi-constraint routing algorithm,” ACM Transactions on Autonomous and
Adaptive Systems (TAAS), vol. 8, no. 1, p. 6, 2013.
[30] M. R. Senouci, A. Mellouk, L. Oukhellou, and A. Aissani, “An Evidence-
Based Sensor Coverage Model,” IEEE Communications Letters, vol. 16, no.
9, pp. 1462–1465, 2012.
78
[31] N. Amjad, M. Sandhu, S. Ahmed, M. Ashraf, A. Awan, U. Qasim, Z. Khan,
M. Raza, and N. Javaid, “DREEM-ME: Distributed Regional Energy Effi-
cient Multi-hop Routing Protocol based on Maximum Energy with Mobile
Sink in WSNs,” Journal of Basic and Applied Scientific Research (JBASR),
vol. 4, no. 1, pp. 289–306, 2014.
[32] A. Haider, M. Sandhu, N. Amjad, S. Ahmed, M. Ashraf, A. Ahmed, Z. Khan,
U. Qasim, and 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 (JBASR), vol. 4,
no. 1, pp. 200–216, 2014.
[33] S. Ahmed, M. Sandhu, N. Amjad, A. Haider, M. Akbar, A. Ahmad, Z. Khan,
U. Qasim, and N. Javaid, “iMOD LEACH: improved MODified LEACH Pro-
tocol for Wireless Sensor Networks,” Journal of Basic and Applied Scientific
Research (JBASR), vol. 3, no. 10, pp. 25–32, 2013.
[34] B. Johny and A. Anpalagan, “Body Area Sensor Networks: Requirements,
Operations, and Challenges,” IEEE Potentials, vol. 33, no. 2, pp. 21–25,
2014.
[35] L. Wang, C. Goursaud, N. Nikaein, L. Cottatellucci, and J. Gorce, “Cooper-
ative scheduling for coexisting body area networks,” IEEE Transactions on
Wireless Communications, vol. 12, no. 1, pp. 123–133, 2013.
[36] S. H. Cheng and C. Y. Huang, “Coloring-Based Inter-WBAN Scheduling for
Mobile Wireless Body Area Networks,” IEEE Transactions on Parallel and
Distributed Systems, vol. 24, no. 2, pp. 250–259, 2013.
[37] A. Boulis, D. Smith, D. Miniutti, L. Libman, and Y. Tselishchev, “Chal-
lenges in body area networks for healthcare: the MAC,” IEEE Communica-
tions Magazine, vol. 50, no. 5, pp. 100–106, 2012.
[38] U. Mitra, B. A. Emken, S. Lee, M. Li, V. Rozgic, G. Thatte, H. Vath-
sangam, D. Zois, M. Annavaram, S. Narayanan, et al., “KNOWME: A case
study in wireless body area sensor network design,” IEEE Communications
Magazine, vol. 50, no. 5, pp. 100–106, 2012.
[39] M. Seyedi, B. Kibret, D. T. Lai, and M. Faulkner, “A survey on intrabody
communications for body area network applications,” IEEE Transactions on
Biomedical Engineering, vol. 60, no. 8, pp. 2067–2079, 2013.
[40] D. Zois, M. Levorato, and U. Mitra, “Energy–Efficient, Heterogeneous Sen-
sor Selection for Physical Activity Detection in Wireless Body Area Net-
79
works,” IEEE Transactions on Signal Processing, vol. 61, no. 7, pp. 1581–
1594, 2013.
[41] C.-S. Lin and P.-J. Chuang, “Energy-efficient two-hop extension protocol for
wireless body area networks,” IET Wireless Sensor Systems, vol. 3, no. 1,
pp. 37–56, 2013.
[42] G. Lo, S. Gonzalez, and V. Leung, “Wireless body area network node local-
ization using small-scale spatial information,” IEEE Journal of Biomedical
and Health Informatics, vol. 17, no. 3, pp. 715–726, 2013.
[43] S. Ullah, M. Imran, andM. Alnuem, “A Hybrid and Secure Priority-Guaranteed
MAC Protocol for Wireless Body Area Network,” International Journal of
Distributed Sensor Networks, vol. 2014, p. 7–pp, 2014.
[44] L. Yao, B. Liu, G. Wu, K. Yao, and J. Wang, “A biometric key establish-
ment protocol for body area networks,” International Journal of Distributed
Sensor Networks, vol. 2011, p. 10–pp, 2011.
[45] Y.-S. Jeong, H.-W. Kim, and J. H. Park, “Visual Scheme Monitoring of Sen-
sors for Fault Tolerance on Wireless Body Area Networks with Cloud Service
Infrastructure,” International Journal of Distributed Sensor Networks, vol.
2014, p. 7–pp, 2014.
[46] M. M. Monowar, M. Mehedi Hassan, F. Bajaber, M. A. Hamid, and A.
Alamri, “Thermal-Aware Multiconstrained Intrabody QoS Routing for Wire-
less Body Area Networks,” International Journal of Distributed Sensor Net-
works, vol. 2014, p. 14–pp, 2014.
[47] J. hyuk Kim, C. ki Hong, and S. bang Choi, “Optimal allocation of random
access period for wireless body area network,” Journal of Central South
University, vol. 20, no. 8, pp. 2195–2201, 2013.
[48] G. Selimis, L. Huang, F. Masse, I. Tsekoura, M. Ashoue, F. Catthoor, J.
Huisken, J. Stuyt, G. Dolmans, J. Penders, and H. D. Groot, “A lightweight
security scheme for wireless body area networks: design, energy evaluation
and proposed microprocessor design,” Journal of medical systems, vol. 35,
no. 5, pp. 1289–1298, 2011.
[49] E.-J. Kim, S. Youm, T. Shon, and C.-H. Kang, “Asynchronous inter-network
interference avoidance for wireless body area networks,” The Journal of Su-
percomputing, vol. 65, no. 2, pp. 562–579, 2013.
[50] M. A. Hamid, M. M. Alam, M. S. Islam, C. S. Hong, and S. Lee, “Fair
data collection in wireless sensor networks: analysis and protocol”, Annals
80
of Telecommunications, vol. 65, no. 7-8, pp. 433-446, 2010.
[51] Y. Zhang, and G. Dolmans, “Priority-guaranteed MAC protocol for emerging
wireless body area networks”, Annals of Telecommunications, vol. 66, no.
3-4, pp. 229-241, 2011.
[52] I. Anjum, N. Alam, M. A. Razzaque, M. M. Hassan, and A. Alamri, “Traffic
priority and load adaptive MAC protocol for QoS provisioning in body sensor
networks”, International Journal of Distributed Sensor Networks, vol. 2013,
Article ID 205192, 9 pages, 2013. doi:10.1155/2013/205192
[53] P. Ferrand, M. Maman, C. Goursaud, J.-M. Gorce, L. Ouvry, “Performance
evaluation of direct and cooperative transmissions in body area networks”,
Annals of Telecommunications, vol. 66, no. 3-4, pp. 213-228, 2011.
[54] N. A. Alrajeh, J. Lloret, and A. Canovas, “A Framework for Obesity Con-
trol Using a Wireless Body Sensor Network”, International Journal of Dis-
tributed Sensor Networks, vol. 2014, Article ID 534760, 6 pages, 2014.
doi:10.1155/2014/534760
[55] R. H. Jacobsen, K. Kortermand, Q. Zhang, and T. S. Toftegaard, “Under-
standing Link Behavior of Non-intrusive Wireless Body Sensor Networks”,
Wireless Personal Communications, vol. 64, no. 3, pp. 561-582, 2012.
[56] F. A.-Ntim, and K. E. Newman, “Lifetime estimation of wireless body area
sensor networks using probabilistic analysis”, Wireless Personal Communi-
cations, vol. 68, no. 4, pp. 1745-1759, 2013.
[57] H. A. Sabti, and D. V. Thiel, “Node Position Effect on Link Reliability
for Body Centric Wireless Network Running Applications”, IEEE Sensors
Journal, vol. 14, no. 8, 2014.
[58] N. Javaid, Z. Abbas, M. Fareed, Z. Khan, and N. Alrajeh, “M-attempt: A
new energy-efficient routing protocol for wireless body area sensor networks,”
Procedia Computer Science, vol. 19, pp. 224–231, 2013.
[59] Q. Nadeem, N. Javaid, S. Mohammad, M. Khan, S. Sarfraz, and M. Gull,
“SIMPLE: Stable Increased-throughput Multi-hop Protocol for Link Effi-
ciency in Wireless Body Area Networks,” 2013 Eighth International Confer-
ence on Broadband and Wireless Computing, Communication and Applica-
tions (BWCCA), pp. 221–226, 2013.
[60] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient
communication protocol for wireless microsensor networks,” Proceedings of
81
the 33rd Annual Hawaii International Conference on System Sciences, pp.
10–pp, 2000.
[61] Q. Zhou, X. Cao, S. Chen, and G. Lin, “A solution to error and loss in
wireless network transfer,” International Conference on Wireless Networks
and Information Systems, WNIS’09, pp. 312–315, 2009.
[62] E. Reusens, W. Joseph, G. Vermeeren, and L. Martens, “On-body measure-
ments and characterization of wireless communication channel for arm and
torso of human,” 4th international workshop on wearable and implantable
body sensor networks (BSN 2007), pp. 264–269, 2007.
[63] T. S. Rappaport, “Wireless communications: principles and practice,” Pren-
tice Hall PTR New Jersey, vol. 2, 1996.
[64] A. Ahmad, N. Javaid, U. Qasim, M. Ishfaq, Z. A. Khan, and T. A. Alghamdi,
“RE-ATTEMPT: A New Energy-Efficient Routing Protocol for Wireless
Body Area Sensor Networks, International Journal of Distributed Sensor
Networks, vol. 2014, Article ID 464010, 9 pages, 2014. doi:10.1155/2014/464010.
[65] M. Nabi, M. Geilen, and T. Basten, “MoBAN: A configurable mobility model
for wireless body area networks,” Proceedings of the 4th International ICST
Conference on Simulation Tools and Techniques, pp. 168–177, 2011.
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