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Incentivizing Cooperation in Mobile Ad Hoc Networks: An
Experiment, A Coalition Game Theory Model, and OLSR
Integration
Amr E. Hilal
Dissertation submitted to the Faculty of the
Virginia Polytechnic Institute and State University
in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
in
Computer Engineering
Allen B. MacKenzie, Chair
Luiz A. DaSilva
Yiwei Thomas Hou
Sedki Mohamed Riad
Hanif D. Sherali
July 26, 2013
Blacksburg, Virginia
Keywords: Mobile ad hoc networks, Cooperation, Mobility, Coalition Game Theory
Copyright 2013, Amr E. Hilal
Incentivizing Cooperation in Mobile Ad Hoc Networks: An Experiment, A
Coalition Game Theory Model, and OLSR Integration
Amr E. Hilal
(ABSTRACT)
Although smart mobile devices have only come into prominence recently, they have
quickly become a necessity in the modern world. In 2012, more than 450 million new
smartphones are expected to be purchased around the world, exceeding, for the first time,
purchases of laptops and desktop PCs combined in a single year. That, in addition to the
increasing processing power and low cost of these emerging mobile devices, creates an in-
creasing demand for mobile applications that work in infrastructure-supported environments
like WiFi and cellular networks as well as infrastructure-less environments like ad hoc net-
works. Therefore, the behavior of mobile devices in such scenarios should be a continued
focus of research.
Several factors contribute to the observed behavior of nodes in Mobile Ad-hoc Networks
(MANETs). For example, nodes may act selfishly to preserve their limited energy resources.
This selfishness may be detrimental to network performance. Therefore, cooperation be-
tween peers is necessary to keep these MANETs operational. Beside the need for actively
encouraging cooperation by providing incentives, passive encouragement is also needed to
overcome the effect of factors that limit cooperation, including malicious behavior, environ-
mental obstruction, and mobility.
The contribution of this work is to provide a cooperation model in MANETs that is
capable of surviving topology distortions caused by mobility, and is operable in practical
distributed scenarios. Towards this goal, we first provide a study of the topology character-
istics of MANETs based on real experiments. We study the node degree, link stability, and
link symmetry of these networks, and, based on our observations, we suggest a two-state
Markov model to model link state in such networks, demonstrating the superiority of this
model over the widely-used disk model with mobility. We conclude from this study that
both mobility and channel fluctuations have a significant influence on the network topology,
which makes it important to study cooperation in scenarios where the topology is changing
rapidly.
Based on experimental observations of a real network, we propose a coalition game model
for cooperation in MANETs that shows that stable, effective coalitions can be maintained,
even in the face of a dynamic network topology. We provide an initial evaluation of the model
using a centralized simulation approach. We use the notion of reachability to evaluate the
proposed model, and we simulate the model under different speeds and node densities. Our
simulations show that reachability can be sustained at stable levels despite the deterioration
caused by mobility. In addition, we show that our cumulative coalition formation approach
gives good results in terms of reachability level and computational complexity. We also show
that our proposed model achieves a fair payoff distribution among participating nodes.
Motivated by the promising results of our centralized simulation approach, we take a
further step towards more practical evaluation. We integrate the cooperation model with
an existing MANET routing protocol, OLSR, and evaluate it in this distributed environ-
ment. We modify and augment the OLSR messaging mechanism to enable the exchange of
the coalition information required to keep the model operating. Beside ensuring that the
reachability gain is still attained and the coalition structure is stable, we study the effect
of the extra control traffic overhead incurred by the model. We compare deliverability over
the network with and without the cooperation model. Although our results show that the
cooperation model incurs an average overhead exceeding 100% of that incurred by OLSR in
high density scenarios, it shows better reliability in delivering traffic especially among selfish
nodes in low and average density scenarios.
Counter to what is commonly assumed in the literature, this study shows that coop-
eration can be be maintained in a distributed manner without causing significant traffic
overhead to MANETs run by proactive routing protocols. Due to the simplicity, several
iii
extensions can be applied to enhance the performance of the proposed model and diversify
its usage. We propose these extensions at the end of this dissertation.
iv
Dedication
To humanity, may my work provide a little step towards a better life.
v
Acknowledgments
At the first place, it is the grace of Allah (God), by which this work has been accomplished.
All praise is due to Allah, I thank him and seek his help, guidance, and forgiveness for the
coming steps in my life.
The road towards my Ph.D. degree started when I was in my beloved home country,
Egypt. So, I would like to thank my dear parents, who not only supported me through this
journey, but also suffered from missing me along these years. May I be the righteous son
they wish, though, I will never be able to fulfill my debt to them.
I would like to thank my dearest wife, who was so patient and tolerate, and never
complained about my long working days. She was always there for me and my kids, the
coolness of my eyes. May Allah help me to be a good husband and a good father.
In my opinion, a PhD degree is not only about being experienced in an area of research,
rather, it is about being a good thinker, hence the philosophy part. For that purpose, a
mindful mentor, who teaches how to think about a problem more than the tools to solve it,
is a must. I’d like express my gratitude to my faculty advisor Prof. Allen MacKenzie for his
help and guidance throughout my PhD journey, I did learned a lot from him. Besides, he
was always understanding for the balance required from a family man doing a Ph.D., and
the delays caused by the unpredictability of research work.
I’d like to thank my committee members for their insightful comments on my work, I did
benefitted from them. Special thanks to Prof. Sherali, who was always willing to help me
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and was always responsive to my questions and requests. My thanks also go to Prof. Luiz
DaSilva, Prof. Tom Hou, and Prof. Sedki Riad for agreeing to serve on my Ph.D. committee
and for devoting time to reviewing the manuscript.
Finally, a word of truth, I was really blessed by being in this great university, Virginia
Tech, in this wonderful town, Blacksburg, and within this friendly and helpful community.
An important thing that I am eager to take back to my people is to help them learn more
about themselves while they walk the way of life. I found many opportunities in Virginia
Tech and in the surrounding community that helped me discover things in my personality
that I did not know before. In academic terms, I got the dots connected and saw new
trends. However, I believe there is still more to discover, that was just a start. Thanks dear
community.
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Attribution
Some contributions helped in the research behind chapter 3 of this dissertation, A brief
description of these contributions is included here.
Michael S. Thompson, Ph.D., Electrical and Computer Engineering, Virginia Tech is
currently a professor of Electrical and Computer Engineering at Bucknell University. Dr.
Thompson assisted in the development of the software used in collecting data during the
MANIAC Challenge and participated in discussions regarding the analysis of this data.
viii
Contents
1 Introduction 1
1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Scope of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Future Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.5 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Literature Review 8
2.1 MANETs Cooperation Schemes . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.1 Credit-Based Cooperation Schemes . . . . . . . . . . . . . . . . . . . 9
2.1.2 Reputation Based Cooperation Schemes . . . . . . . . . . . . . . . . 10
2.1.3 Other Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Game Theory Cooperation Models . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Non-cooperative Models . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.2 Cooperative Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Coalition Game Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.1 The Core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.2 Shapley Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4 Coalition Game Models in MANETs . . . . . . . . . . . . . . . . . . . . . . 17
3 MANET Characterization 19
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
ix
3.2 Simulation vs. Experimentation . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 The MANIAC Challenge - A Real MANET . . . . . . . . . . . . . . . . . . 23
3.4 Topology Characterization of an ExperimentalMANET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.4.1 Reachability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.4.2 Node Degree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.4.3 Link Symmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.4.4 Clustering Coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.5 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.6 Experimental vs. Simulation Results . . . . . . . . . . . . . . . . . . . . . . 31
3.6.1 Node Degree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.6.2 Link Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.6.3 Link Symmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.7 Markov Model for Link Status . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.7.1 Improved Link Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.7.2 More Real MANET Experiments . . . . . . . . . . . . . . . . . . . . 42
3.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4 A Coalition Game Model for Cooperation in MANETs 46
4.1 Effect of Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.2 Introduction and Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.3 Coalition Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4 Stability of Coalitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5 Model Evaluation: A Centralized Approach 54
5.1 Simulation setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.2 Simulation Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
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5.3.1 Reachability Restoration . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.3.2 Reachability Convergence . . . . . . . . . . . . . . . . . . . . . . . . 59
5.3.3 Fairness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.3.4 Coalitional Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6 A Distributed Approach: OLSR Integration 70
6.1 Preliminary Choices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6.1.1 Why proactive protocol? . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.1.2 Why OLSR? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
6.2 Requirements Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.2.1 Design Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.2.2 Functional Requirements . . . . . . . . . . . . . . . . . . . . . . . . . 72
6.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.3.1 Message Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.3.1.1 Original structure of OLSR Messages . . . . . . . . . . . . . 73
6.3.1.2 Modified OLSR Messages . . . . . . . . . . . . . . . . . . . 75
6.3.1.3 New Message COOP . . . . . . . . . . . . . . . . . . . . . . 76
6.3.2 Stability Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.3.2.1 Choosing Best Cooperation Deal . . . . . . . . . . . . . . . 77
6.3.2.2 Coalition Numbering . . . . . . . . . . . . . . . . . . . . . . 78
6.3.2.3 Merger Concurrency Control . . . . . . . . . . . . . . . . . 79
6.4 Simulation setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
6.5 Simulation Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.6 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.6.1 Reachability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.6.2 Coalition Information Accuracy . . . . . . . . . . . . . . . . . . . . . 86
6.6.3 Control Traffic Overhead . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.6.4 Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
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6.6.5 Summary of Simulation Observations . . . . . . . . . . . . . . . . . . 92
6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
7 Conclusions and Future Work 94
7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
7.2 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
7.3 Future Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
7.3.1 Weighting Parameter for Benefit and Cost of Cooperation . . . . . . 97
7.3.2 OLSR Route Calculation Based on Coalition Information . . . . . . . 99
7.3.3 Control Overhead Reduction . . . . . . . . . . . . . . . . . . . . . . . 99
Bibliography 102
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List of Figures
2.1 A Mobile Ad hoc Network (MANET). . . . . . . . . . . . . . . . . . . . . . 8
3.1 Largest connected component in MANIAC competitions. . . . . . . . . . . . 26
3.2 Node Degree in MANIAC competitions. . . . . . . . . . . . . . . . . . . . . 27
3.3 Symmetric Links in MANIAC Competitions. . . . . . . . . . . . . . . . . . . 28
3.4 Node Degree - MANIAC vs. Simulation. . . . . . . . . . . . . . . . . . . . . 33
3.5 Link Stability - MANIAC vs. Simulation. . . . . . . . . . . . . . . . . . . . . 34
3.6 Histogram shapes for link up-time for increasing mobility speeds. . . . . . . 35
3.7 Two-state Markov model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.8 Modified Markov model - MANIAC07 vs. Simulations. . . . . . . . . . . . . 40
3.9 Modified Markov model - MANIAC09 vs. Simulations. . . . . . . . . . . . . 41
3.10 Modified Markov model - STARA vs. Simulations. . . . . . . . . . . . . . . 44
4.1 Benefit coalition X gains by merging with coalition Y. . . . . . . . . . . . . . 50
5.1 Average reachability deterioration and improvement. . . . . . . . . . . . . . 57
5.2 Average reachability compared to average deterioration over all speeds. . . . 58
5.3 Average number of reachability convergence cycles. . . . . . . . . . . . . . . 61
5.4 Distribution of reachability convergence cycles - low node density (500 node/km2). 62
5.5 Distribution of reachability convergence cycles - high node density (2500node/km2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.6 Average reachability level at convergence. . . . . . . . . . . . . . . . . . . . . 64
5.7 Average reachability progression with time - low node density (250 node/km2). 65
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5.8 Average reachability progression with time - high node density (2500 node/km2). 66
5.9 Average reachability, maximum coalition size, and coverage - 4 m/s. . . . . . 68
6.1 OLSR HELLO message. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
6.2 OLSR TC message. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
6.3 Modified OLSR HELLO message. . . . . . . . . . . . . . . . . . . . . . . . . 76
6.4 Modified OLSR TC message. . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.5 New introduced message COOP. . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.6 Simulated node structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.7 Average reachability vs. node density for different network sizes, speeds, andtraffic loads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.8 Average accuracy of collected coalition information vs. node density at 2 m/sspeed and 0.2 packet/second traffic load. . . . . . . . . . . . . . . . . . . . . 87
6.9 Average completeness of collected coalition information vs. node density at 2m/s speed and 0.2 packet/second traffic load. . . . . . . . . . . . . . . . . . 87
6.10 Maximum coalition size vs. node density at 2 m/s speed and 0.2 packet/secondtraffic load. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6.11 Maximum coalition coverage vs. node density at 2 m/s speed and 0.2 packet/secondtraffic load. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6.12 Average traffic overhead vs. node density at 2 m/s speed and 0.2 packet/secondtraffic load. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
6.13 Average packet delivery ratio vs. node density for different network sizes at 2m/s speed and 0.2 packet/second traffic load. . . . . . . . . . . . . . . . . . 91
7.1 Size of the cooperation model’s share of TC messages. . . . . . . . . . . . . . 100
7.2 Size of the TC messages share in overhead incurred by OLSR and Cooperationmodel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
7.3 Optimized structure of the modified TC message. . . . . . . . . . . . . . . . 101
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List of Tables
3.1 Clustering Coefficient in MANIAC competitions - asymmetric links not included. 29
3.2 Clustering Coefficient in MANIAC competitions - asymmetric links included. 30
3.3 MANET Simulation parameters . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.4 Estimated transition probabilities for the Markov model in the MANIAC sim-ulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.5 Parameters of the MANIAC simulations. . . . . . . . . . . . . . . . . . . . . 39
3.6 Mean Square Error values for simulated MANETs with and without Markovmodel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.7 Summary of STARA simulation parameters. . . . . . . . . . . . . . . . . . . 43
5.1 Average dispersion of node’s reachability and average dispersion of every par-ticular node’s reachability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
6.1 Simulation setup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.2 Simulation parameters and scenarios . . . . . . . . . . . . . . . . . . . . . . 83
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Chapter 1
Introduction
There is no doubt that mobile technology is changing the way we live and work. The
world is adopting mobile devices and mobile applications quickly, and the rate of adoption
is accelerating. There is a growing expectation that mobile services be available everywhere:
at home, at work, and on the go. This immense demand motivates further research effort to
study and improve quality of mobile service.
1.1 Background and Motivation
Mobile services can be provided in infrastructure-supported environments like WiFi and
cellular networks as well as infrastructureless environments like ad hoc networks. In both
cases, the behavior of mobile devices will greatly influence the quality of the provided ser-
vices. Several factors contribute to the observed characteristics of mobile environments. For
example, mobile nodes may act selfishly, by refraining from relaying a neighbor’s traffic, to
preserve their limited energy resources. This selfishness can have a devastating impact on
network performance, and potentially bring the whole network down [1] [2] [3]. On the other
hand, malicious nodes may deliberately disrupt the operation of the network by flooding,
1
2
spoofing, or denying network services [4] [5]. Both selfish and malicious behavior can be a
serious threat for mobile networks, especially in infrastructureless environments where all
nodes share the responsibility of administering the network. Therefore, cooperation between
peers is necessary to keep such networks operational.
A primary goal of cooperation between mobile devices is to get traffic delivered to desti-
nations within time or quality constraints required by applications. In addition, cooperation
can help utilize resources (e.g. energy, bandwidth, and time) more efficiently so that service
can be extended to more users. In the research literature, cooperation in wireless mobile
environments has been studied from several different perspectives. From a physical layer per-
spective, mobile nodes may cooperate to enhance global or individual bit error rate (BER)
or data rate [6]. From a network layer perspective, cooperation may help improve packet
delivery ratio or packet delay [7] [8]. In this work, we focus on promoting cooperation in
mobile ad hoc networks (MANETs) from a network layer perspective, and we target selfish
(rather than malicious) behavior.
1.2 Scope of Work
A wireless ad hoc network is a network that forms between peers for a common purpose when
no infrastructure is available. Hence, data is exchanged by the sole effort of communicating
nodes, serving as routers to relay data for each other. The nature of infrastructureless
communication in ad hoc networks obligates participating nodes to work together in order
to accomplish network goals.
Although the promise of MANETs has been apparent for years, questions remain about
the viability of a commercially-deployed MANET comprising heterogeneous nodes. Several
factors may affect the characteristics of real MANETs. These include node mobility, obstruc-
tions in the deployment area, and policies followed by peers in dealing with others’ traffic. As
a result of these factors, there is uncertainty as to whether a large-scale distributed ad-hoc
3
network comprising different platforms from different vendors and controlled by different
parties is even viable.
Much of the research on MANETs has used simulations and testbeds, while actual
deployment of large-scale MANETs has been limited primarily to military applications.
Moreover, most of these simulations are based on simplifying assumptions that do not reflect
actual MANET environments [9]. These assumptions include symmetric communication
links, free space propagation, and random mobility models [10].
We begin this work with a preliminary study of MANETs characteristics in real ex-
periments and compare the behavior with that predicted by simulations. We use network
traces obtained from two real experiments: The Mobile Ad-hoc Network Interoperability and
Cooperation (MANIAC) Challenge [11], an indoor experiment, and an experiment done at
Dartmouth University to compare four mobile ad hoc routing protocols [12]. In our prelimi-
nary study, we seek to answer the following questions: Do real MANETs characteristics differ
from those assumed in simulation? To what extent? What is the impact on the resulting
research? How can we improve that?
Motivated by our study of MANET characteristics, which found that both mobility and
channel fluctuations can have a significant influence on topology, we dedicate the rest of this
work to study how to establish and maintain cooperation in MANET scenarios where the
network topology is changing rapidly. Studying cooperation in ad hoc networks using game
theory provides a deep and comprehensive understanding [8] to the problem, but mobility
has not been thoroughly addressed in the literature. We use coalitional game theory to
model cooperation in MANETs. In coalitional game theory, players form alliances to act
cooperatively. As a formed coalition grows in size, it covers a larger area of the scene. This
helps to better accommodate topology changes as more coalition members will be available
around to help disconnected members to stay connected to the coalition. To be effective,
the formed coalitions should be stable and maintainable [13]. Along these line, we seek to
answer the following questions: Can coalitions in MANETs remain stable given the frequent
4
topology changes observed in real scenarios? How much overhead will be required to maintain
the coalitional structure?
1.3 Contributions
The contributions of this work fall into two main parts, yet are connected. In the first part,
we provide a study of the topology characteristics of MANETs based on a real experiment
described in [11]. The contributions of this part include:
• We present a characterization of the network topology of an experimental MANET in
terms of reachability, average node degree, link symmetry, and clustering coefficient.
We use node degree, link stability, link symmetry, and clustering coefficient to compare
between real and simulated MANETs.
• We evaluate some differences between real and simulated MANETs through simulations
that mimic the environment of the experimental MANETs. We use node degree, link
stability, and link symmetry to compare between the real and simulated MANETs.
• We suggest a two-state Markov model of link state, and show that it can better simulate
MANETs than the widely-used disk model with mobility.
From this study we conclude that both mobility and channel fluctuations can have a
significant influence on topology, which makes it important to study cooperation in scenarios
where the network topology is changing rapidly. The research in this part of the dissertation
has been published in collaboration with the MANIAC challenge organizing team in [14] and
[15].
5
In the second part of this dissertation, we propose a solution to mitigate the effect
of topology changes in MANETs. We do that by incentivizing group cooperation among
MANET nodes so that frequent disconnections caused by topology changes are mitigated
by the availability of surrounding peers that agreed to cooperate a priori based on expected
shared benefits. Our contributions in the second part can be summarized as follows:
• We propose a coalition game model for cooperation in MANETs that uses the notion
of reachability as a base for payoff evaluation. We show analytically that the model
satisfies the pairwise stability condition.
• We evaluate the proposed model in a centralized approach simulation. We show that
stable coalitions can be maintained even in the face of frequent topology changes. The
simulations also show that the model is capable of restoring reachability deteriora-
tion caused by topology changes with different node moving speeds, and with a fair
distribution of payoff.
• We integrate the cooperation model with an existing MANET routing protocol, OLSR,
to prove that it can be implemented in a distributed manner. We modify and augment
the OLSR messaging mechanism to support coalition formation and maintenance in
the cooperation model. We simulate the integrated system with random traffic and
show that the cooperation model improves traffic deliverability as compared to selfish
behavior, even though it incurs additional control traffic overhead.
Our study shows that the overhead incurred from incentivizing cooperation in MANETs
distributively can be tolerated in some scenarios for a higher reliability in delivering ex-
changed traffic. This centralized evaluation of the proposed model has been published in
[16]. The distributed integration with OLSR is under preparation for submission as a journal
article.
6
1.4 Future Extensions
The presented work in this dissertation can be extended from two dimensions, the cooper-
ation model and the implementation. The cooperation model can be extended to accom-
modate different ways of evaluating cooperation benefit and cost within the confines of the
reachability definition. The model implementation can be extended to enhance quality of
routes and reduce traffic overhead. These extensions can be summarized as follows:
• Introduce a parameter to the cooperation model to weight the cost of cooperating with
one additional node with respect to the benefit of connecting to one additional node.
This parameter will give an additional degree of freedom to the model to better reflect
cooperation in different environments.
• Allow OLSR to use the available coalition information to build more reliable routes.
For example, OLSR can be modified to give priority in choosing MPRs to coalition
mates such that resulting routes involve more cooperative nodes.
• Further reduce control traffic overhead incurred by the cooperation model. This can be
achieved by using a reduced form of nodes’ information, for instance node’s addresses,
since this information has already been exchanged via OLSR. This can significantly
reduce overhead because nodes are main constituent of the exchanged coalitions infor-
mation.
1.5 Outline
This dissertation is organized in seven chapters. In the next chapter, we provide a literature
review. Chapter 3 provide details of our study of MANET topology characterization. This
chapter includes an overview of topology characterization of a real MANET, a comparison
to simulated MANETs, and a suggested Markovian link-state model for simulation. Chapter
7
4 introduces the coalition formation game model of cooperation. An evaluation of the model
based on a centralized simulation is provided in Chapter 5, and a distributed implementation
with OLSR is described in Chapter 6. We conclude this study and suggest future work in
Chapter 7.
Chapter 2
Literature Review
A wireless ad hoc network is a self-organized network that is formed by peer nodes for a
common purpose when no infrastructure is available to support such a network. Hence, data
is exchanged by the sole effort of communicating nodes, serving as routers to relay data for
each other. The nature of infrastructure-less communication in ad hoc networks requires
Figure 2.1: A Mobile Ad hoc Network (MANET).
8
9
participating nodes to cooperate to keep the network operational. However, some nodes
may refrain from forwarding data packets for other nodes to preserve their limited energy
resources. Such selfish behavior can have a devastating impact on network performance,
and potentially bring the whole network down. In addition to selfishness, other factors can
degrade quality of cooperation. These factors include mobility and environmental obstruc-
tions.
In this chapter, we review the research work on encouraging cooperation in mobile ad hoc
networks (MANETs). We focus on research that aimed to overcome selfishness by providing
incentives for participants to cooperate. We review the different cooperation approaches
that have been proposed in the literature, and focus on game theory as the domain of our
contribution in this dissertation.
2.1 MANETs Cooperation Schemes
Most of the work in the literature on encouraging or enforcing cooperation in MANETs can
be divided into two major categories: credit-based (also known as price-based) [17] [18] [19]
[20] [21] [22] [23], and reputation-based [2] [3] [24] [25] [26] [27] [28] [29] [30] [31] [32] [1] [33].
The exchanged credit in credit-based systems and the distribution of reputation information
and the reliance (in some cases) on promiscuous listening in reputation-based systems raise
some doubts regarding scalability of these approaches [34].
2.1.1 Credit-Based Cooperation Schemes
In credit-based systems , nodes use virtual currency to pay for relay services, so nodes must
obtain sufficient credit by providing service to other nodes. The virtual currency can take
several forms ranging from points to actual money. For example, the authors in [20], [21]
use a security module at each node called the Nuglet counter. This module decreases the
10
node’s Nuglet reserve when it sends out its own packets and increases it when it forwards a
packet for others. However, a node must manipulate its activity to maintain positive reserve
of Nuglets. The proposed credit system in [18] follows a similar logic except that a node can
gain more credits using real money, at a variable rate to the virtual money, based on the
current performance of the system.
A challenging issue is how to initially allocate credits to nodes. The authors in [17]
assume that a user receives an initial endowment of one unit when it enters the system. The
user’s credit balance is then adjusted by transferring credits equal to the congestion costs to
each of the downstream resources. Another concern is how to prevent credit manipulation.
The proposed system in [18] uses a central service that clears all the transactions and charges
a fine for fake or denial service requests.
Although credit-based systems provide a good stimulus for cooperation, they can be
sometimes unfair. Nodes that do receive few forwarding requests because of their location,
for instance on the boundary of the network, may starve for credit to satisfy their need to send
their own traffic. In addition, credit-based systems fail to enforce continuous cooperation
of wealthy nodes that accrue a lot of credits, then refrain from forwarding any more traffic
because they have enough credit for future. This applies also to nodes that do not need to
send packets for some time, even if not wealthy. These nodes can choose to refrain from
providing forwarding service for others, too.
2.1.2 Reputation Based Cooperation Schemes
In reputation-based systems, a node decides whether to respond to a forwarding request
based on how cooperative the requestor was to others. This decision is made by maintaining
reputation information about other nodes in the network by tracking their behavior towards
others. Reputation information is usually shared using periodically exchanged messages.
A node will be cooperative with nodes that have a good reputation, while nodes with a
11
bad reputation will be punished. Reputation information can be collected on two levels of
trustworthiness: first-hand and second-hand.
In second-hand reputation systems [3] [2] [26] [31], nodes use direct and indirect obser-
vations to build their reputation information bases. Indirect observations are those conveyed
to a node via its neighbors, not collected by its own effort. Promiscuous listening to neigh-
bors’ behavior is sometimes used to collect indirect reputation information like in [3] and
[2]. However, reliance on information conveyed by others poses the threat of cheating. The
authors in [26] use a one-way hash chain [35] to ensure message integrity, and use a proce-
dure suggested in [36] for broadcast authentication. In [3], direct and indirect reputation
observations are not treated the same. Indirect observations can take only positive values,
hence malicious broadcasting of negative feedback is prevented. A validation mechanism is
employed, though, to ensure the accuracy of the indirect observations by comparing to ex-
pected values. Although second-hand systems incur the overhead of exchanging reputation
information, they can detect and report misbehavior faster than first-hand systems.
In first-hand systems [24] [27], a node relies only on its own observations to evaluate
the cooperation of other nodes. The objective of limiting reputation information exchange
is to avoid trust management overhead. In [27], an acknowledgement-based mechanism is
proposed to maintain reputation information in MANET source routing protocols like DSR
[37]. In this scheme, missing acknowledgements indicates misbehaving nodes, which triggers
the routing protocol to avoid the accused link in future routes calculations. The findings in
[27] and [24] show that, in many scenarios, they do as well as second-hand schemes.
2.1.3 Other Schemes
Recently, other research in the literature has tried to avoid the disadvantages of both credit-
based and reputation-based schemes. In [38], the authors use game theory to analyze co-
operation incentives provided by the types of cooperation schemes, and propose a hybrid
12
system that provides strong incentives to encourage cooperation while ensuring quick and
effective detection of selfish nodes.
In other work, researchers focused on horizontal improvements by enhancing a feature
that is shared by most of the schemes in the literature. In [39], the proposed scheme tries to
avoid the need to maintain memory of past interactions. That is to avoid tracking available
credit and reputation information in credit-based and reputation-based systems, respectively.
The proposed model aims to tag cooperative nodes in the network. Since cooperative nodes
will gain higher payoffs than selfish ones, other nodes will tend to join the cooperative
group, assuming that nodes are rational. Subsequently, cooperative nodes will take over the
population.
In addition to the above schemes, game theory has been a useful tool to study cooperation
in MANETs. In the next sections, we provide an introduction to basic concepts in game
theory and their application to encouraging cooperation in wireless networks. We, then, shed
more light on coalition game theory, and review the research work in the area of cooperation
modeling in MANETs.
2.2 Game Theory Cooperation Models
Game theory is a branch of applied mathematics that studies mutual interactions of multiple
players who look for best strategies to maximize their gain in response to others’ strategies
[40] [41]. Studying cooperation in MANETs using game theory provides a more comprehen-
sive understanding of the process. In a game, a node decides whether to cooperate or not
based on its evaluation of the prospective benefits and costs of cooperation and the expected
strategies of other nodes in the network. A node’s preferences are expressed in the form of a
utility function that can include all the factors that contribute to its satisfaction. The utility
function reflects the node’s objectives as it selects an action in response to the actions of
other players.
13
Several research works in the literature use game theory as a tool to model, analyze,
and evaluate existing cooperation models in MANETs [8] [38] [42] [43] [44] [45]. In game
theory-based schemes, the researchers model the cooperation process as a game where the
participants follow some strategy to exchange benefits with others. The ultimate goal of
these games is to reach an equilibrium point where every one is satisfied and no one desires
to change its cooperation strategy. However, these models need to accommodate the frequent
changes in network topology [16].
In the literature, game theory is used to model cooperation with two approaches: coop-
erative and non-cooperative games. In non-cooperative games [46] [47] [48] [49] [50], nodes
act unilaterally, while nodes in cooperative games may form coalitions and make decisions
based on collaborative strategies [51] [52] [53] [54]. All the nodes in a cooperative game may
end up gathering into a single coalition, denoted the grand coalition, but the grand coalition
is not always the best outcome for the game’s participants.
2.2.1 Non-cooperative Models
In non-cooperative games, nodes react individually to others’ actions based on the assessment
of their own benefit. A well-known strategy for non-cooperative cooperation is TIT-FOR-
TAT (TFT), which has been a winning strategy for the iterated prisoner’s dilemma [55].
TFT is a simple reciprocal strategy that basically relies on establishing an equivalent re-
lation with an opponent, in which a player starts by being cooperative, then replicates his
opponent’s previous action in terms of cooperativeness. A variation of the TFT strategy is
Generous TIT-FOR-TAT (GTFT), in which a player may give a chance to his opponent if
he misbehaves rather than directly replicating his actions, hence the generosity [56]. The
authors in [47], use a variant of TFT that avoids aggressive punishment to misbehaving
nodes, compared to previous work [57] [43].
Reaching a Nash equilibrium point in a non-cooperative game could be desirable because
it guarantees some stability among rational players [40]. A non-cooperative game is said to be
14
in a Nash equilibrium state if no single player can be better off by changing its own strategy
while the others remain unchanged. However, if more than one player colluded, they might
be better off by changing their strategies, but this is outside the scope of non-cooperative
games. In [46], the authors investigate equilibrium conditions for packet forwarding strategies
in wireless ad hoc networks, but they restrict their study to static configurations, i.e., no
mobility.
2.2.2 Cooperative Models
In cooperative games, players from within the game cooperate to achieve common benefits.
The most common form of cooperative games is coalitional games, in which nodes form
coalitions that share benefits and follow common strategies. These coalitions compete with
each others as opposed to individuals in non-cooperative games. Since coalition members
follow agreed-on strategies to obtain shared benefits, there is an interest in the value of
a coalition as an entity, which is the total amount of utility it can obtain as a whole, as
compared to the payoff every member obtains by affiliating with a coalition [58]. The way
the coalition value is divided among coalition members distinguishes transferable utility
games (TU) from non-transferable utility games (NTU).
In TU games, there is no restriction on the way utility can be divided among coalition
members. The clearest example of a unit of transfer for utility is money. Resource allocation
in wireless networks is modeled as transferable utility game in [53], and grand coalition
is shown to be stable in many case. On the other hand, the payoff an individual player
obtains in an NTU game depends on some factors, among them coalition structure and
formation sequence. In [54], the authors model cooperative spectrum sensing in cognitive
radio networks as a non-transferable coalitional game, and use a simple merge and split
algorithm to optimize coalition formation.
We provide a more detailed discussion of features and solution concepts of coalitional
game problems in the next section.
15
2.3 Coalition Game Theory
The most common form of coalition games is the characteristic form that was first intro-
duced by Von Neuman and Morgenstern in 1944 [59]. A coalition game is said to be in the
characteristic form, Γ = (N, v), if the utility value of any coalition C ⊆ N , is independent
of the coalitions/stucture formed among the players outside C, i.e., players in N \ C [58],
but depends only on the members of C. The characteristic function v associates with every
coalition C ⊆ N a real number quantifying the value of C.
After bringing individual players together to join a coalition, an important question will
be how to maintain that cooperation and prevent coalitions from dissolving. Maintaining
cooperation ties among rational players requires essentially satisfying two conditions:
• Stability: No individual or group of players will be better off by leaving the coailtion.
• Fairness: Every player obtains a fair payoff share according to the employed payoff
distribution criteria.
Studying stability and fairness properties of the coalitions is critical in coalitional game
theory. There are several solution concepts in literature that help studying these properties
[60] [61]. These concepts apply to games in which the grand coalition forms, and to sub-
games in cases where grand coalition is not achievable or desirable. Among these solution
concepts, the most common ones are the core and Shapley value, described below.
2.3.1 The Core
The concept of the core is probably the most important concept for studying coalitional
games. As the name indicates, the core gives stability to a game if it exists. In essence,
the core of a coalitional game is the set of payoff allocations that guarantees that no groups
of players (including individual nodes, i.e., singletons) will be better off by separating from
16
their coalitions [58]. As mentioned before, this definition applies to the grand coalition or to
smaller coalitions by studying them as separate sub-games.
For a TU coalitional game in characteristic form, Γ = (N , v), the core of Γ is the set of
payoff vectors x:
C(Γ) =
{x ∈ RN :
∑i∈N
xi = v(N ) and∑i∈S
xi ≥ v(S),∀S ⊆ N
}(2.1)
According to the above definition, if such payoff allocation can be found, then the grand
coalition should to be stable and optimal solution for the game. However, if the core is empty,
that does not mean that the grand coalition can not be stable. In addition, even if the core
is proven not empty, it might not be easy to find it. Moreover, it might be challenging to
select a fair allocation if the core is huge. Therefore, despite its simplicity and popularity,
the core is not an appropriate solution concept for all coalitional games.
2.3.2 Shapley Value
The Shapley value is a solution concept introduced by Lloyd Shapely in 1953 [62] to deal
with TU games. This technique associates with every coalitional game Γ = (N , v) a unique
payoff vector, known as Shapley value φ(v) that is calculated based on a set of predefined ax-
ioms. These axioms represent desirable characteristics in the payoff distribution to guarantee
fairness. These axioms are [58]:
1. Efficiency:∑
i∈N φi(v) = v(N ).
2. Symmetry: If player i and player j are such that v(S ∪ {i}) = v(S ∪ {j}) for every
coalition S not containing player i or player j, then φi(v) = φj(v).
3. Dummy: If player i is such that v(S) = v(S ∪{i}) for every coalition S not containing
i, then φi(v) = 0.
17
4. Additivity: If u and v are characteristic functions, then φ(u+ v) = φ(v + u) = φ(u) +
φ(v).
The efficiency axiom basically means group rationality. The symmetry and dummy
axioms are meant to ensure fairness, where two players should obtain the same payoff if they
both have the same marginal contribution, while a player who contributes nothing should
get nothing. The additivity axiom asserts the the uniqueness of the Shapley value over the
space of all coalitional games.
The unique payoff vector that satisfies these axioms for a coalitional game (N , v), φi(v),
is:
φi(v) =∑
S⊆N\{i}
|S|! (N − |S| − 1)!
N ![v(S ∪ {i} − v(S)] (2.2)
Although the Shapley value identifies a unique and fair payoff allocation in coalitional
games, it may be computationally expensive to compute it, especially in games with large
numbers of players.
2.4 Coalition Game Models in MANETs
There are several papers in the literature that use coalitional game theory to study cooper-
ation in wireless ad hoc networks, but mobility is given insufficient attention. The authors
in [63] seek to maximize the network lifetime by encouraging nodes to cooperate to optimize
transmission power to deliver packets over shared routes. They use the concept of Shapely
value to fairly allocate the cooperation payoff among coalition members such that forwarding
load is balanced over coalition members. Although the results of this work were promising,
the simulations had only stationary nodes. Hence, the results did not show the performance
in a MANET with mobile nodes.
18
In [64], the authors study the incentives to form coalitions in MANETs based on coop-
erative game theory. They use the user’s desire to be connected to as many other users as
possible as the incentive to form coalitions and exchange forwarding services with coalition
peers. They measure the benefit of joining a coalition in terms of the number of newly
accessible users and the cost of joining as the cost for activating a link with the coalition.
Accordingly, the formed coalitions are trees of activated links, which are shown to be highly
stable. Although mobility is an inherent feature in MANETs, the authors explicitly consider
only purposeful deactivation of links, and do not allow links to fail arbitrarily via mobility
or changing channel conditions.
While relying on a credit-based approach to stimulate cooperation in MANETs, the
authors in [65] use the notion of core in coalitional game theory to find a stable payment
scheme for nodes that contribute to relaying packets on a cost efficient route. They define
a stable time slot as the duration of time in which the network topology does not change.
They assume that the duration of a stable time slot is enough for a node to discover the
complete graph of the network. Although this assumption could be valid in small networks
with short routes, the rate at which network topology changes due to the above mentioned
factors in actual MANETs can make this assumption infeasible in many real cases [14].
In this dissertation, we introduce a cooperation model in MANETs based on coalition
game theory that has a main goal of stimulating cooperation in the network while surviving
the observed high level of topology dynamism. We use the concept of reachability to measure
how cooperative the network is, and track the changes in the coalition structure with respect
to the topology change.
Chapter 3
MANET Characterization 1
Several environmental and behavioral factors influence the characteristics observed in real
MANETs. Since deploying and running a real MANET is logistically difficult, researchers
often resort to simulations. However, simulating complex environments accurately is an-
other challenge. Therefore, simplified assumptions and models are usually applied to make
simulation easier.
In this chapter, we present a study of MANET characteristics that sheds light on the
qualitative differences between real-life and simulated MANETs. We use metrics including
node degree, link stability, and link symmetry to compare real and simulated MANETs.
We use data from two sources: The MANIAC Challenge [11] and an experiment run at
Dartmouth University to compare four mobile ad hoc routing protocols [12]. We use our
study results to suggest the use of a two-state Markov model of link status to better model
MANET characteristics.
1The work in this chapter is based on a manuscript in preparation for publication. The manuscript isco-authored by Amr E. Hilal, Michael S. Thompson, Allen B. MacKenzie, and Luiz A. DaSilva.
19
20
3.1 Introduction
While there has been an interest in the research community in studying MANET charac-
teristics for many years, many of these studies have relied on using simulations to model
MANETs. Actual MANET experimental results are more complex than assumed in sim-
ulations because of factors including mobility, obstructions, and cooperation policies. For
instance, the impact of selfishness on MANET performance has been studied in [1], [2], [3].
Simulations usually rely on simplifying assumptions because realistic simulations are inher-
ently complex [9]. These assumptions include symmetric communication links, free space
propagation, and random mobility models [10]. Simulation-based MANET studies do not
closely resemble actual MANET environments, making them unreliable predictors of the
performance of real MANETs.
Motivated by the need for studying MANETs in more realistic scenarios, we created
a real MANET to compare the observed characteristics and behaviors to those predicted
by simulation. The Mobile Ad-hoc Network Interoperability and Cooperation (MANIAC)
Challenge [11] was an NSF-funded competition, in which we relinquished control of the
network, allowing participating teams from multiple academic institutions to employ custom
strategies to cooperate with others in the network. The first MANIAC Challenge was held in
conjunction with the IEEE Global Communication Conference (Globecom) in Washington,
DC in November 2007; the second was held in Galveston, Texas in March 2009 at the IEEE
International Conference on Pervasive Computing and Communications (Percom).
Unlike traditional simulations and testbeds where node position and direction as well as
forwarding and routing decisions are under the control of a common operator, participants in
the MANIAC experiments were free to move about and to choose their level of cooperation.
This gave us the opportunity to collect network traces that, we believe, can provide deeper
insights about the characteristics of real MANETs. Besides the MANIAC Challenge, we
sought to collect more data from actual MANET deployments to support our study. We
used data from an outdoor experiment run at Dartmouth University, in 2004, to compare
21
four mobile ad hoc routing protocols [12]. We searched online data repositories, such as
CRAWDAD (http://crawdad.cs.dartmouth.edu/), for additional data on experimental
MANET topology data, but were able to find very little, reinforcing our claim that there is
insufficient experimental work in this area.
The main goal of the work presented in this chapter is to compare the characteristics of
a simulated MANET built with common simplifying assumptions with the characteristics of
a real MANET, and to suggest realistic alternative simulation assumptions. To do this, we
used data collected from the MANIAC competitions to characterize the underlying network
topology. We studied reachability, node degree, link stability, link symmetry, and clustering
coefficient. The network topology was not affected by the cooperation strategies employed
by the participating teams, as these teams were permitted to manipulate (i.e., drop or
redirect) data packets but not control packets. Then, we built a MANET simulation using
common simplifying assumptions and models, and compared the characteristics of the real
and simulated MANETs.
Our results show that node degree in the simulated MANET was close to what we
observed in the real network. However, significant differences were observed in link stability
and link symmetry statistics, which are directly influenced by the choice of connectivity and
mobility models used in the simulation. We suggest the use of a two-state Markov chain to
model link dynamics in MANETs in a modified way. Our simulations showed an improved
matching in the resulting histograms of node degree and link stability. However, due to the
lack of available experimental data, more work needs to be done to statistically confirm our
results.
3.2 Simulation vs. Experimentation
Although most of the published research on MANETs relies on simulation, there is also a
considerable body of work that describes experimental results on MANET performance. A
22
survey of MANET implementations is provided in [66] and more recently reported results
include [11],[67]. Of particular relevance to this chapter are investigations that focus on
the realism of simulated MANET environments as compared to observations of MANET
testbeds.
The MANET research community is, arguably, overly reliant on simulation, and the
credibility of these simulations has been questioned in [9] and [68]. Particular points that
are criticized include: insufficiently rigorous experimental design, the lack of statistical sig-
nificance analysis, and poor repeatability. There can also be significant disagreement among
results obtained under equivalent system parameters on different network simulators, as
shown in [69].
Ultimately, however, the determination of the degree of realism of simulated MANET
environments should rely on a comparison of the results obtained from simulation to those
observed in real networks. This is the approach followed in [11],[67],[70],[71], and in this
work. The authors of [70] and [71] report a number of discrepancies between simulated and
observed MANET performance. For example, the simulation of MANET routing protocols
tends to overestimate the robustness of links, which are often asymmetric and less stable
than expected. This is also one of the conclusions from our data set, as reported in [11].
Unrealistic traffic patterns, mobility models, and propagation models (for instance, due to
the use of the simplified disk connectivity model) are other common causes of discrepancies
between simulation and reality.
Our contribution in this chapter is to quantify the differences in network characteris-
tics, primarily focusing on topology-related metrics, between a deployed MANET and one
simulated under assumptions typically made in the literature. Much prior published work
on experimental MANETs relies on tightly controlled testbeds. In contrast, our experimen-
tal data is collected from a network of heterogeneous nodes (different manufacturers and
models of laptops, different network interface cards and variations of IEEE 802.11) whose
operation, including mobility and packet forwarding policies, was not controlled by us. We
23
believe this network to more closely reflect the performance that would be seen in a commer-
cially deployed MANET. From our results, we suggest guidelines to achieve more realistic
simulations.
3.3 The MANIAC Challenge - A Real MANET
The main goal of the MANIAC Challenge was to study the effect of the tension that may arise
in a real MANET between nodes that strive to maximize their own benefit. In particular,
a node may act selfishly by trying to limit the number of packets it forwards for others
to preserve its battery life; at the same time, nodes are expected to cooperate to keep the
network operational and get their packets delivered. Hence, nodes must apply strategies
that compromise between their own benefit and the benefit of other nodes in the network.
To enable this level of realism, an Application Programming Interface (API) was pro-
vided to the participants in the MANIAC Challenge to give them the ability to manipulate
traffic by accepting, dropping, or redirecting packets that they were requested to forward.
However, nodes were not permitted to manipulate control packets generated by the under-
lying routing protocol and therefore topology information was not affected by participants’
strategies. The MANIAC Challege was set up as a competition, and nodes were incentivized
to cooperate and form a connected network, while also seeking to minimize the amount
of traffic they forwarded for others. In the competition, teams accrued points whenever a
packet destined to one of their nodes was correctly received and lost points whenever they
forwarded a data packet that did not belong to them.
Several teams from academic institutions from Europe, Africa, and the United States
participated in the competitions. Each team included two laptop nodes, each carried by a
participant who could move freely around the competition area. Nodes used network inter-
face cards (NICs) from different vendors and using different, but compatible, technologies
(IEEE 802.11b and 802.11g) operating in ad-hoc mode. Six teams participated in the first
24
competition, while eight teams participated in the second. Each competition included three
runs of 20 minutes each and took place in an indoor environment.
Some nodes in the network were controlled by the MANIAC Challenge organizers and
served primarily as sources. The task of the source nodes was to generate realtime and non-
realtime traffic randomly and equally to all the participant nodes (destination nodes). The
realtime traffic had associated playback deadlines that packets must meet to be considered
on “time”. Source nodes were placed at the edges of the experiment area to ensure the exis-
tence of multi-hop routes to destinations. The Naval Research Laboratory implementation
of OLSR (NRL-OLSR) [72] was used as the underlying routing protocol in the MANIAC
network.
Three types of log files were created during each competition run in each node: routing
logs, API logs, and traffic logs. The routing logs contained snapshots of the node’s routing
table, collected once per second. These routing logs were later used to generate topology files
that provide snapshots of the whole network topology at each time instant. The API logs
stored every packet that passed through the API and what decision the user made regarding
this packet (accept, drop or redirect). The traffic logs stored information regarding the
realtime and non-realtime streams that were sent to every node. This information included
stream id, packet reception time at the destination node, and which stream packets were
received on time or late. The traffic logs were used in the MANIAC Challenge to decide the
winners of the competition based on the number of packets a team received of its own and
the number of packets a team forwarded to others.
More information about the MANIAC Challenge can be obtained from the official web
site at www.maniacchallenge.org.
25
3.4 Topology Characterization of an Experimental
MANET
In this section, we characterize the network topology of the experimental MANET in terms
of reachability, average node degree, link symmetry, and clustering coefficient. In the MA-
NIAC Challenge, the source nodes served exclusively as traffic sources. As these nodes were
statically deployed at the edges of the network and did not have a role in forwarding packets,
we have excluded them from the topology metrics reported below.
3.4.1 Reachability
We define the reachability of a single node at a particular time instant as the percentage
of nodes in the network, with which that node has a two-way communication route. A
two-way communication route between two nodes means that both nodes can reach each
other, possibly through multiple hops. We use the percentage of nodes in the network that
belong to the largest connected component (i.e., the largest connected sub-graph) as the
network-wide metric of reachability. Figure 3.1 shows histograms of this percentage for the
two MANIAC competitions. The network was nearly completely connected most of the time
with more than 95% of the nodes were connected 60% to 80% of the time. We use notion of
reachability later in chapter 4 as a metric to measure cooperation in MANETs.
3.4.2 Node Degree
Node degree can be defined as the number of one-hop neighbors that a node can discover.
In proactive routing protocols, one-hop neighbors are usually discovered using exchanged
periodic messages (OLSR Hello messages in our case). Figure 3.2 shows histograms of node
degree for the MANIAC’07 and MANIAC’09 competitions. The figures show similar shapes,
but the MANIAC’09 histogram is slightly shifted to the right because of the existence of
26
(a) MANIAC’07.
(b) MANIAC’09.
Figure 3.1: Largest connected component in MANIAC competitions.
27
Figure 3.2: Node Degree in MANIAC competitions.
more teams in the second competition, which led to a larger average node degree. The
average node degree in MANIAC’07 was 8.7, and 12.3 in MANIAC’09.
3.4.3 Link Symmetry
A link is said to be symmetric if both nodes on the link can detect each other. Figure 3.3
shows a comparison of the percentage of symmetric links between the three runs on every
MANIAC competition. While over 60% of the links were symmetric, the figures show a
considerable presence of asymmetric links in the network.
3.4.4 Clustering Coefficient
We refer to clustering coefficient, as defined in [73], as the degree with which neighbors of
a node are connected to one another. Formally, if any node, k, has n neighbors then the
maximum number of edges among those n nodes is n(n− 1)/2. The clustering coefficient is
28
(a) MANIAC’07.
(b) MANIAC’09.
Figure 3.3: Symmetric Links in MANIAC Competitions.
29
Table 3.1: Clustering Coefficient in MANIAC competitions - asymmetric links not included.
Run 1 Run 2 Run 3
MANIAC’07 0.8677 0.8645 0.8141
MANIAC’09 0.8541 0.8836 0.8765
the fraction of the possible number of edges that actually exist. If there are l such actual
edges, the clustering coefficient for node k is expressed as Ck = ln(n−1)/2 . A clustering
coefficient near 1 indicates that the node has a densely connected neighborhood.
Table 3.1 shows the average clustering coefficient in each competition in MANIAC’07
and MANIAC’09. The clustering coefficients are averaged over all nodes and all time instants
of every competition run after excluding the source nodes. The table shows a high degree
and close level of clustering in both competitions.
The definition of clustering coefficient in [73] treats the network as an undirected graph.
Therefore, we had to disregard the links that were asymmetric to satisfy the definition.
However, since a non-negligible percentage of the links were asymmetric, as pointed out
before, we wanted to see the impact of including them in our calculation of the clustering
coefficient. To do that, we differentiated between the two directions of a link, and doubled
the number of possible edges. That is, if there are l′ such directional edges in an n-node
cluster, the clustering coefficient for node k will be expressed as Ck = l′
n(n−1) . If the clustering
coefficient remains the same, this means that all the links were originally symmetric. While
if the clustering coefficient increased, this means that some asymmetric links were newly
included. Table 3.2 shows the clustering coefficient after including the asymmetric links.
The table shows an increase in the clustering coefficient of nearly 3.5%. We believe that
link asymmetry has a considerable impact on characterizing MANETs that it should be
considered in future studies.
30
Table 3.2: Clustering Coefficient in MANIAC competitions - asymmetric links included.
Run 1 Run 2 Run 3
MANIAC’07 0.8934 0.8940 0.8424
MANIAC’09 0.9004 0.9183 0.9153
3.5 Simulation Environment
In order to evaluate the differences between real world and simulated MANETs, we con-
ducted a MANET simulation to mimic the environment of the MANIAC Challenge. We
used the OMNET++ open source simulator. We selected simulation models representing
the most common simplifying assumptions adopted in the literature, and simulation param-
eters resembling the conditions in the MANIAC Challenge.
Among the most common MANET simulation models adopted in the literature are the
random waypoint mobility model (RWP) [10] [74] [75] [76], and the unit-disk connectivity
model [10] [77] [78]. We used the RWP model with zero pause time, and with speeds starting
from 2 m/s (an average pedestrian speed). We used the conventional unit-disk connectivity
model to determine the neighbors of each node with communication range values falling
between 37 and 54 meters. This means that a node will be able to communicate with only
neighbors that are located, at the time of communication, within a distance not greater than
that particular node’s communication range.
Other simulation parameters include the deployment area and number of nodes. En-
closed areas such as the interior of hotels, office buildings, and shopping centers are usually
irregularly shaped and contain obstructions. Simulations, on the other hand, typically as-
sume rectangular areas free of obstacles. Although the two MANIAC competitions were
held in different places (the first competition was run in the concourse level at the Hilton
Washington Hotel, and the second was run in the Galveston Island Convention Center), they
both had approximate dimensions of 61 m × 122 m which we used in our simulation setup.
31
Table 3.3: MANET Simulation parameters
Simulation Parameter Value
Area 61 m × 122 m
Number of Nodes 16 and 20
Mobility Model Random Waypoint
Pause Time 0 seconds
Mobility Speed 2 – 14 m/s
Connectivity model unit-disk
Communication Range 37 – 54 m
Since the number of participating teams increased in the second competition, we ran
two sets of simulations; one using 16 nodes, and the other using 20 nodes. In both cases,
four of the nodes served as source nodes. Table 3.3 summarizes the simulation parameters.
During the MANIAC competitions, the source nodes were mostly stationary at positions
located on the boundaries of the deployment area, but some of them were moved from time
to time to obtain better reception from other nodes or to gain more hops inside the network.
Therefore, we made the source nodes move slowly in the simulation (0.1 - 0.2 m/s) as
compared to the other nodes.
3.6 Experimental vs. Simulation Results
In this section, we highlight major differences between the MANET characteristics observed
in the MANIAC experiment and our simulated MANETs.
32
3.6.1 Node Degree
With the unit-disk propagation model, we tried different values for nodes communication
range falling between 37 to 54 meters. We compared the node degree distribution observed
in simulation and in the experimental MANIAC network qualitatively for the different com-
munication range values. We obtained close, yet not identical, histogram shapes for both
MANIAC competitions as shown in Figure 3.4. In MANIAC’07, we found the closest his-
togram shape at communication range of 46 m, while 48 m communication range provided
the best match for MANIAC’09. We tried different node speeds in the above simulations,
but there was no significant impact observed on the node degree plots, which is expected.
The communication ranges, at which simulation distributions of node degree matched
that of the MANIAC competitions, fall within the effective ranges that can be observed in
indoor wireless applications using 802.11b and 802.11g technologies, whereas the maximum
theoretical range is 92m (300 ft) [79].
3.6.2 Link Stability
As nodes move faster, we can expect less stable links. In other words, as nodes increase their
speed, links are susceptible more frequently to be torn down and brought up, hence higher
frequencies of link up/down time can be found. We define link up/down-time as a time
period during which a direct link between two nodes exists (up) or does not exist (down).
We studied link stability in terms of the distribution of link up-time for different simulation
runs with different speeds starting from the normal pedestrian speed (2 m/s), which best
approximates actual node speed in the MANIAC competitions, up to 14 m/s (an impossible
speed for pedestrians). We used the communication ranges that lead to the best matching
of node degree distribution (46m for MANIAC’07 and 48m for MANIAC’09) to compare the
simulation link up-time histograms to those of the MANIAC experiment.
At the normal pedestrian speed (2 m/s), a significant difference in the histogram shapes
33
(a) 46 m communication range.
(b) 48 m communication range.
Figure 3.4: Node Degree - MANIAC vs. Simulation.
34
(a) MANIAC’07 vs. Simulation.
(b) MANIAC’09 vs. Simulation.
Figure 3.5: Link Stability - MANIAC vs. Simulation.
35
was observed between the real experiment and simulations as shown in Figure 3.5. To
investigate how inaccurate is the mobility model in comparison to the real experiment, we
looked at higher speeds. At speed 9 m/s, the histograms became closer, yet can not be
matched. Since a typical pedestrian speed ranges from 1 to 2 m/s [80], having link stability
only matched at simulation speeds greater than 8 m/s is a major discrepancy.
Looking at all the histograms for speeds ranging from 2 to 10 m/s together, we can
observe that as the speed increases, the mean link up-time decreases as shown in Figure
3.6. This coincides with the intuition that the network tends to have more unstable links as
nodes move faster. However, comparing simulation to the MANIAC experiment, for every
particular speed, links in the real experiment tend to be more unstable than in simulations.
Therefore, we believe that attributing link dynamics in real MANETs to mobility alone is a
mistake.
Figure 3.6: Histogram shapes for link up-time for increasing mobility speeds.
36
3.6.3 Link Symmetry
We have seen that links in the MANIAC experiment exhibited a high degree of asymmetry.
Comparing this to simulations, a significant difference can be observed. As the simulation
follows the unit-disk propagation model to discover neighboring nodes, it follows — by defi-
nition — that 100% of the links will be symmetric, which is not the actual case. Asymmetric
links can impact not only the link status but also the behavior of the whole route [11].
3.7 Markov Model for Link Status
In Section 3.6 we showed that mobility alone should not be used to characterize link behavior
in MANETs. However, the question remains as to how to incorporate other factors into a
simple simulation link model. The two-state Markov model has been used in the literature
to model different aspects of MANETs. In [81], a two-state discrete-time Markov model
has been used to model packet drop rate of the communication channels in MANETs. A
two-state Markov model has been used in [82] to model the link life time in an effort to study
the effect of node mobility on the wireless links and protocol performance in MANETs. In
[83], a two-state Markov model is used to propose a mobility model that better models the
nature of human movements in MANETs. In [14], we showed that modeling link status as a
two-state Markov chain can provide a reasonable, though imperfect, model for wireless link
status in MANETs. The novelty of this work is that we verify the realism of the proposed
model through comparison to a real experiment, the MANIAC Challenge.
In our two-state Markov model of link status, the period of time a link stays up or down
should follows a geometric distribution with parameters p and q, respectively, as shown in
Figure 3.7. In this section, we improve our work in [11].
37
Figure 3.7: Two-state Markov model.
3.7.1 Improved Link Model
Two main factors can cause link transitions in a mobile wireless network: mobility that re-
sults in a node moving outside radio communication range, and environmental obstructions
that cause effects like fading and shadowing that attenuate the radio signals. The effect of
mobility is limited only to the transition time at which a node crosses the communication
range of another (in or out). Likewise, the impact of the signal distortion caused by envi-
ronmental factors is only significant during the time period in which two nodes lie physically
in the communication range of each other. In other words, a link from node A to node B
should suffer from frequent intermission only if B is in the communication range of A. If
B is out of A’s range, then we consider the link down until they get back in range. We
differentiate between a link from A to B and a link from B to A because the communication
range of A is not necessarily the same as that of B.
To improve the model of link status described in Section 2.5, which was based only
on the disk connectivity model and the random waypoint mobility model, we modified our
MANET simulation to include the two-state Markov model to model the link dynamics
between pairs of nodes when they are are within range of one another. That is, we modify
the disk connectivity model such that once node B is in the range of node A, the link from
A to B will evolve according to the two-state Markov model in Figure 3.7. We chose to use
38
a single Markov model for both directions because this is closer to reality (in which the two
link directions are highly correlated) than using separate models.
Table 3.4: Estimated transition probabilities for the Markov model in the MANIAC simula-tions.
p q
MANIAC’07 Simulation 0.0440 0.0180
MANIAC’09 Simulation 0.0510 0.0210
We tried different values for the transition probabilities p and q to achieve best results.
We used node degree and link stability to judge the matching level we reach. Table 3.4 shows
the values finally selected to give the closest match in both node degree and link stability
results.
Beside the Markov model transition probabilities, Table 3.5 shows the simulation pa-
rameters we used in the MANIAC simulation. During the MANIAC competitions, we gave
some guidelines for movement, however there was no hard rules imposed. In fact, one goal
was to let the participants move with minimal restrictions, as in reality. Therefore, we are
uncertain about some simulation parameters that best mimic the MANIAC experiments.
For instance, we do not have exact value of the speed at which the participants were moving,
so we tried a range of speeds that fit pedestrians (0.5 m/s to 2 m/s) and picked the one
that gave us the best results. Also, with the use of NICs from different vendors, we applied
communication ranges that fall within the accepted indoor ranges.
Figures 3.8 and 3.9 show the distribution of node degree and link up-time (the down-
time histograms exhibited similar results) obtained from the modified simulations compared
to MANIAC07 and MANIAC09. The figures show a better matching between the real and
simulated MANET under the Markov model. To provide a quantitative assessment, we
used the Mean Square Error metric (MSE) to assess the similarity between the simulated
MANET, with and without the Markov chain model, and MANIAC data as shown in Table
3.6. Using the Markov model transition probabilities shown in Table 3.4, the MSE values
39
Table 3.5: Parameters of the MANIAC simulations.
Simulation Parameter MANIAC’07 Simulation MANIAC’09 Simulation
Area 61 m × 122 m 45 m × 90 m
Communication range 58 m 67 m
Speed 2 m/s 2 m/s
Number of runs 10 10
Duration of a run 20 minutes 20 minutes
for the link stability histograms were significantly reduced. The improvement in node degree
was not as significant as in link stability because they were already close as shown in Section
3.6.
Table 3.6: Mean Square Error values for simulated MANETs with and without Markovmodel.
MANIAC’07 MANIAC’09
Without With Without With
Markov Markov Markov Markov
Node Degree 8.01 2.12 3.11 3.25
Link Stability 59.05 3.31 63.81 2.20
It is worth pointing out here that the communication ranges used in these simulations
are larger than what were used in the simulations in Section 3.6 because of the addition of
the two-state Markov model. The goal of the results shown in Section 3.6 is to show the
differences between the characteristics of real MANETs and simulations when run using com-
mon models and simplifying assumptions. However, in both cases, the used communication
ranges are within typical indoor ranges.
40
(a) Node degree.
(b) Link stability.
Figure 3.8: Modified Markov model - MANIAC07 vs. Simulations.
41
(a) Node degree.
(b) Link stability.
Figure 3.9: Modified Markov model - MANIAC09 vs. Simulations.
42
3.7.2 More Real MANET Experiments
To support our results, we looked in the literature for more real MANET experiments to
include in our study. Although we could find several publications that discuss real MANET
experiments, most of them did not provide access to their collected traces [84] [85] [86]. Even
for the few available experiments, it is difficult or impossible to extract topology information
because few experiments were run to study topology-related metrics.
Despite the lack of available data, we found one experiment that, although studying
routing behavior, provided sufficient log data to allow us to extract topology information.
The experiment was run by a team at Dartmouth University to compare the performance
of four different MANET routing algorithms. The results of this experiment is published in
[12], and the collected traces are available online [87]. Since the MANIAC Challenge was
held indoors, this experiment is of interest because it was held outdoors.
The experiment, conducted in 2004, compared four different MANET routing algo-
rithms: two reactive protocols, APRL [88] and AODV [89], and two proactive protocols,
ODMRP [90] and STARA [91]. The outdoor routing experiment took place on a rectangu-
lar athletic field measuring approximately 225 by 365 meters. The experiment was run on
top of thirty-three 802.11-enabled laptops moving randomly through an athletic field. The
comparison was intended to provide insight into the behavior of ad hoc routing algorithms
at larger real-world scales than had been considered previously. More information about the
experiment description and content of the collected logs can be found in [12] and [87].
We chose a proactive algorithm from the compared algorithms because proactive routing
algorithms usually exchange periodical messages to update their routing information. These
messages can provide regular topology updates by looking into their header data, if available.
From the two examined proactive protocols, we could extract topology data from only the
STARA routing logs. We used the extracted data to build topology files similar to those
created for the MANIAC experiment, and used these files to obtain node degree and link
stability plots corresponding to the STARA outdoor experiment.
43
Table 3.7: Summary of STARA simulation parameters.
Simulation Paramter Value
Markov Model Parameters p = 0.09, q = 0.035
Area 225 m × 365 m
Communication range 105 m
Speed 2 m/s
Number of runs 10
Duration of a run 20 minutes
Using the experiment log description available online at [87], we did our best to ex-
tract topology information that we can use to generate similar statistics to those generated
for MANIAC. We ran MANET simulations to mimic the STARA experiment applying the
modified two-state Markov model. Table 3.7 summarizes the simulation parameters used in
the STARA simulation. Figure 3.10 shows the distribution of node degree and link up-time
obtained from the simulations compared to STARA experiment. The figures show similar
trends, though not perfect, between the simulation and STARA experiment.
3.8 Summary
In this chapter, we have examined major qualitative differences between topology character-
istics of real world and simulated MANET. Our study focused on the influence of the simpli-
fying assumptions and models that are widely adopted in MANET simulations like unit-disk
propagation and random mobility on the observed characteristics of simulated networks. We
compared topology characteristics of MANETs observed in the MANIAC Challenge to those
of a simulated MANET designed to mimic the environment in which the MANIAC Chal-
lenge was run. These characteristics include node degree, link stability and link symmetry.
Our preliminary results showed similarity between the node degree distribution of the real
and simulated MANET. However, a significant difference was observed in comparing link
44
(a) Node degree.
(b) Link stability.
Figure 3.10: Modified Markov model - STARA vs. Simulations.
45
stability and link symmetry. Links looked much more unstable and asymmetric in real life
than is anticipated in simulated MANETs.
To provide a better model of topology changes in MANETs, we suggested an improved
model for link status that uses a two-state Markov chain to model link transitions while
communicating nodes are in-range. We showed a better match to reality in terms of node
degree and link stability. Although our qualitative results could not be statistically confirmed
due to the lack of enough data from real MANET experiments, we believe that using the two-
state Markov chain to model link dynamics in MANETs can be more realistic than commonly
applied models, yet remain simple. We encourage more work to be done to provide a clearer
mapping between reality and simulation.
We conclude that current MANET simulations rely on several unrealistic simplifying
assumptions and models that need to be modified or changed in order to have accurate tools
to study MANETs. In the next chapter, we propose a cooperation model based on coalition
game theory that can accommodate frequent topology changes as observed in real MANETs.
Chapter 4
A Coalition Game Model for
Cooperation in MANETs
In Chapter 3, we showed that actual MANET deployments may suffer from a significant rate
of topology changes due to several factors including environmental obstructions and mobility.
Coalition game theory has been used to model cooperation in ad hoc networks, yet the effect
of topology changes has not been studied thoroughly. In this chapter, we propose a coalition
game model for MANETs that uses reachability as an incentive for nodes to cooperate via
forming coalitions. We use the notion of reachability as a metric to assess the payoff a node
gains from cooperation, and show that formed coalitions sustain pairwise stability.
4.1 Effect of Mobility
When nodes in a MANET behave selfishly, routes start to become unreliable. Selfish nodes
contribute to route unreliability by introducing points of failure when they refrain from
relaying packets for other nodes. This behavior may limit reachability to the rest of the
network. (We refer to the definition of reachability provided in section 3.1.) Although much
46
47
research has proposed providing incentives for nodes to cooperate, selfishness is not the only
factor that can limit reachability in MANETs. For instance, a route can become unreliable
when one of its links fails.
Link failure can be caused by either environmental or behavioral factors or by a combina-
tion of the two. Environmental factors include fading, shadowing, or environmental obstacles
that may cause a link to fail while the communicating nodes are still within the communica-
tion range of each other. Mobility, which is an inherent characteristic in MANETs, greatly
affects link status. As nodes move around in the network area, they move in and out of the
communication range of other nodes causing links to go up and down. This, in turn, changes
the network topology and routes, and thus affects reachability. The effect of mobility on link
status increases as nodes move with higher speeds. In prior work, we studied link stability in
a real MANET experiment in terms of the period of time links remained up or down before
transitioning to the other state [14]. This study showed that links in actual MANETs suffer
from high instability.
Despite the research on alleviating the effect of selfishness in ad hoc networks, most of
this work focused on cooperation in static environments where nodes are not mobile and the
topology remains static. In this work, we show that cooperation between selfish nodes in
a MANET can be accomplished through coalitions that are robust in the face of topology
changes. We propose a cooperation model that uses reachability to incentivize cooperation
and tries to restore average node reachability after topology changes caused by mobility.
4.2 Introduction and Definitions
We model cooperation in MANETs as a coalitional game in characteristic form Γ = (N , v),
where N is the set of nodes that seek to cooperate in the network, and v is the characteristic
function that associates with every coalition C ⊆ N a real number quantifying the value of
C.
48
The model uses reachability as a measure of the benefits of cooperation and uses coalition
size as an estimate of the cooperation cost. Two nodes are defined to be reachable by one
another if a two-way communication path can be established between them. We express
both benefit and cost as a number of nodes and use the difference as the payoff a node gains
through cooperation with others.
We assume that being a member of a coalition obligates one to provide forwarding
service for any packet destined to or sourced by any other coalition member. The payoff
that a node i receives by being in coalition X is the number of nodes that i can reach through
cooperation with other coalition members minus the number of nodes it provides relaying
service for. Node j is said to be reachable by i if a two-way communication path can be
established between i and j that uses only members of coalition X as intermediate hops.
We define the border nodes of coalition X, denoted BX , as the nodes that lie outside the
coalition but can be reached in one hop by at least one coalition member. Accordingly, the
payoff node i receives by being a member in coalition X, ui(X), is the size of the coalition
plus the border nodes (Benefiti(X )) minus the size of the coalition (Costi(X )).
ui(X) = Benefiti(X)− Costi(X)
= [|X|+ |BX |]− |X|
= |BX |. (4.1)
Hence, the value of a coalition X, v(X), can be formulated as
v(X) =∑i∈X
ui(X) = |X| · |BX |. (4.2)
As discussed in the literature review, classifying a game as transferable or non-transferable
utility (TU or NTU, respectively) game depends on the way payoffs are distributed among
coalition members. In TU games, payoffs can be freely allocated and transferred between
49
players. This happens when the utility is equally valued by all game participants and there
is some mechanism of exchange, such as money, that can be used to move value between
them. However, in some games, due to game-specific rules or regulations, not all allocation
patterns are permitted or feasible. Games where the allocation of coalition payoffs between
the nodes are constrained are known as NTU games. In NTU games, distribution patterns
or sequences may also change the payoff a coalition member obtains.
In spite of its simplicity, classifying the game in the proposed model is rather tricky.
From one side, although utility, measured in reachability, is equally valued by all nodes, it
is somewhat difficult to “reallocate” reachability between nodes. On the other hand, the
structure and sequence of coalition formation do not influence the payoff a coalition member
obtains. We think that our simple model lies on the border between transferable and non-
transferable utility games, however, we have primarily analyzed it as an NTU game.
In the next sections, we provide a description of the mechanism for coalition formation
followed by a coalition stability study.
4.3 Coalition Formation
Initially we assume that each individual node forms a singleton coalition. Two coalitions
can merge if a one-to-one bargain is successfully completed between them. The one-to-one
bargain can happen between any two representative nodes, one from each coalition, provided
that these two nodes are direct neighbors. A bargain is initiated when node i from coalition
X sends a cooperation offer to node j from coalition Y . Both nodes i and j evaluate the
cooperation offer based on the prospective benefit for their whole respective coalitions. For
simplicity, we assume that the benefit obtained from the merger of coalitions is equally
distributed over the coalition members, so they all get equal payoff. However, we plan to
remove this assumption in a future extension of this work.
A node evaluates the payoff it expects upon committing to a cooperation contract using
50
Figure 4.1: Benefit coalition X gains by merging with coalition Y.
its utility function ui. This utility function is expressed in terms of expected benefit and
the incurred cost of that cooperation process. Node i cooperates with other members of
coalition X by relaying packets sourced from or destined to any member of the coalition.
We define the benefit coalition X receives by merging with coalition Y as the additional
number of nodes that will be reachable by the members of X upon fulfilling the cooperation
deal. According to our definition of reachability, nodes that are on the border of coalition
X are already reachable by any member of X. Figure 4.1 illustrates the benefit members of
coalition X gain by merging with coalition Y , denoted BenefitX (Y ).
According to our description to the coalition formation process, we can formulate the
benefit members of coalition X gain by merging with coalition Y as
BenefitX(Y ) = Benefiti(X ∪ Y )−Benefiti(X), i ∈ X
= |Y | − |Y ∩BX |+ |BY \ (X ∪BX)|.(4.3)
Similarly, we define the cost members of coalition X pay to merge with coalition Y as
the number of additional nodes for whom members of coalition X will commit to relay traffic.
This is simply the size of coalition Y .
CostX(Y ) = Costi(X ∪ Y )− Costi(X), i ∈ X
= |Y |(4.4)
51
As a representative of coalition X, node i uses the utility function UX(Y ) to evaluate
the cooperation offer with coalition Y . Since all coalition members get equal payoff, UX(Y )
can be formulated as:
UX(Y ) = ui(X ∪ Y )− ui(X)
= |BX∪Y | − |BX |
= [Benefiti(X ∪ Y )− Costi(X ∪ Y )]− [Benefiti(X)− Costi(X)]
= [Benefiti(X ∪ Y )−Benefiti(X)]− [Costi(X ∪ Y )− Costi(X)]
= BenefitX(Y )− CostX(Y )
= [|Y | − |Y ∩BX |+ |BY \ (X ∪BX)|]− |Y |
= |BY \ (X ∪BX)| − |Y ∩BX | (4.5)
Similarly, the utility function UY (X) defines how node j evaluates a cooperation offer
with coalition X:
UY (X) = |BX \ (Y ∪BY )| − |X ∩BY | (4.6)
A one-to-one bargain is successful if both bargainers find the offer beneficial to their
coalitions. In other words, if both coalition representatives expect a reachability gain for
their respected coalitions if they fulfill the deal. Therefore, both utility functions UX(Y ) and
UY (X) should be greater than or equal to zero in order for nodes i and j to agree to fulfill
the cooperation deal. Hence, the conditions for coalitions X and Y to merge is to have:
|BY \ (X ∪BX)| ≥ |Y ∩BX | (4.7)
|BX \ (Y ∪BY )| ≥ |X ∩BY | (4.8)
52
4.4 Stability of Coalitions
Two important concepts of coalition stability that have been studied in the literature are
pairwise stability and coalition stability [64], where the latter provides a stronger stability
guarantee.
• Pairwise stability : There is no node that would be better off by leaving its current
coalition.
• Coalitional stability : There is no subset of nodes belonging to a coalition that would
be better off by separating and forming a new coalition.
Although it is preferable to satisfy both stability conditions, coalitional stability is more
difficult to satisfy. Under a continuously changing topology, it may take a long time for a
subset of nodes to agree to separate from their coalition, which makes it impractical. In [14]
we showed that real mobile ad hoc networks suffer from a high level of link state change,
which reflects frequent topology changes. Therefore, we limit our discussion to the pairwise
stability concept.
Proposition 1. When two coalitions X and Y merge, the resulting coalition XY will have
a set of border nodes BXY larger than or equal in size of any of BX and BY .
Proof. The conditions for coalitions X and Y to merge is to have the utility functions UX(Y )
and UY (X) greater than or equal to zero. Since UX(Y ) represents the difference between
B(X∪Y ) and BX , and UY (X) represents the difference between B(X∪Y ) and BY , then the
coalition XY will have a set of border nodes BXY larger than or equal in size of any of BX
and BY .
Proposition 2. When the dynamics of coalition formation over a fixed topology reach a
steady state, the coalition structure will be pairwise stable.
53
Proof. According to (4.1), the payoff node i receives when it joins coalition X is |BX |. If
node i separates from coalition X, the payoff it receives will be the number of nodes it
communicates directly with, which is its direct neighbors or border nodes |B{i}|. We need
to show that the payoff node i receives by being a member of coalition X is always greater
than or equal to what it gains by separating, i.e. |BX | ≥ |B{i}|. We prove this by induction.
When node i joined coalition C1 (C1 ⊆ X) for the first time, according to Proposition
1:
|BC1| ≥ |B{i}|.
As C1 grows by merging with other coalitions/nodes, we will have:
|BCj+1| ≥ |BCj
|,
where Cj was formed before Cj+1.
Hence, as the dynamics of the coalition formation reaches a steady state, the border size
of the final coalition X that resulted from a series of merges will be greater than or equal to
that of node i, i.e. |BX | ≥ |B{i}|.
4.5 Summary
In this chapter, we have proposed a cooperation model in MANETs based on coalitional
game theory. The model aims to encourage nodes to cooperate by forming coalitions so that
cooperation survives topology changes. We used the notion of reachability to incentivize
coalition formation and showed that the formed coalitions sustain pairwise stability. In the
next chapter, we study the performance of the model under network dynamics.
Chapter 5
Model Evaluation: A Centralized
Approach
In this chapter, we evaluate the performance of the proposed coalition structure in Chapter
4 under network dynamics. We show that the model responds to topology changes by
restructuring the formed coalitions to restore stability and reachability. We study two basic
features of the model: sustained reachability improvement and coalitions stability. The
study is conducted using a centralized simulation, and run under different speeds and node
densities.
5.1 Simulation setup
We conduct our simulations in two stages:
• Stage 1: We use OMNET++ [92] to run simple MANET simulations that consist of
only moving nodes without any information exchange. The nodes move according to
the random way point mobility model with 0 pause time. We use these simulations to
generate random topology scenarios with varying parameters like node speed, commu-
54
55
nication ranges, and node densities. These topology scenarios are stored in topology
files that consist of a snapshot of the network topology at each instant of the simulation
time. We plan to test the proposed model against the MANIAC topology traces.
• Stage 2: We simulate the cooperation process in a custom C++ simulation using the
topology files obtained in stage 1.
There are two reasons we did our simulations in two stages. First, having topology files
eases the process of testing and verifying the implemented cooperation model by enabling
repeated execution of the same scenario. Second, having topology files allows us to rerun
the same simulation scenario and extract additional metrics without the need to recalculate
everything.
5.2 Simulation Scenario
Since the first simulation stage is simple, we focus here on details of the cooperation simu-
lation. The stage 2 simulation goes in cycles of topology and coalition updates. We chose
to make these cycles every 10 seconds to provide a clear view of the network’s response to
topology changes under the proposed cooperation model. We observe these measures over
a wide range of node densities. We used node densities ranging from 250 node/km2 to 2500
node/km2, with fixed network size of 100 nodes. We conduct simulations with speeds ranging
from 1 m/s to 4 m/s with 1 m/s steps, where nodes move at a fixed speed in each simulation.
We ran our simulations with different communication ranges, but results are only shown for
54 m range in this work; this represents a typical indoor environment.
The simulation starts with each individual node acting as a one-node coalition (i.e. a
singleton coalition). Every cycle in our simulation goes through four steps:
1. Update nodes’ neighbors based on the most recent topology change.
56
2. Update coalitions membership based on topology updates. When a coalition becomes
disconnected, the coalition splits. Each of the connected components of the coalition
becomes a new, independent coalition, to which the next steps apply.
3. Update coalition memberships based on benefit re-evaluation. After a coalition splits
due to mobility, individual nodes may decide to leave the new, smaller coalition. We
previously showed that a coalition formed through a process of mergers will be weakly
stable. However, stability is not guaranteed after a coalition splits because of a topology
change.
4. Coalitions reform. After the divisions and separations of steps 2 and 3, coalitions start
to merge again to obtain maximum benefit of cooperation. This process happens in
random turns between the nodes. A coalition is allowed one merger turn at every
cycle, carried out by any of its member nodes as a representative. In a turn, a node
sends a cooperation offer to the best candidate neighboring coalition according to its
utility function. A recipient coalition may accept or reject a cooperation offer according
to its evaluation. However, a coalition is not allowed to wait for better offers before
responding, because this can cause deadlocks. At a particular cycle, a node is granted
one turn whether its offer was accepted or rejected, while a coalition is granted at most
one accepted offer, if available.
5.3 Simulation Results
As mentioned in the introduction of this chapter, these simulations study two basic features of
the proposed cooperation model: sustained reachability improvement and coalitions stability,
both against any deterioration caused by topology changes. To study the first, we measure
three metrics, reachability restoration, reachability convergence, and fairness. We show the
progression of coalition features with time to verify coalition stability.
57
Figure 5.1: Average reachability deterioration and improvement.
5.3.1 Reachability Restoration
We measure the restoration of reachability following a topology change by comparing average
reachability deterioration caused by mobility after simulation step 3 to average reachability
improvement attained after coalitions reform in simulation step 4. To isolate the effect of the
proposed model, we exclude nodes that are physically disconnected from the network when
we compute reachability of each node. The reachability of node i is then defined as the ratio
between the number of nodes with which node i can establish a two-way communication path,
given the coalition to which i belongs and the forwarding rules, and the number of nodes
to which there exists a path in the communication graph. Hence, we measure reachability
deterioration after simulation step 3 as the difference between average reachability after step 3
at time t and average reachability after step 4 at time t−1. Similarly, we measure reachability
improvement after simulation step 4 as the difference between average reachability after step
4 at time t and average reachability after step 3 at time t.
58
Figure 5.2: Average reachability compared to average deterioration over all speeds.
Figure 5.1 shows this comparison over different node densities and speeds. The figure
shows that mobility causes reachability to deteriorate more as speed increases, and that the
network was able to restore average reachability, especially in more dense networks. The 95%
confidence interval of the computed average reachability improvement and deterioration has
a maximum value of ±0.45% for all points on the curve. The figure also exhibits a low tail,
which means that the effect of mobility becomes lower in highly dense MANETs. This is
because in dense networks, nodes exhibit high node degree such that lost connections caused
by mobility are small compared to the still connected ones. In the same context, Figure 5.2
shows that average reachability (averaged over all examined speeds) follows an increasing
trend as the network becomes more dense, where reachability deterioration becomes very
small.
59
5.3.2 Reachability Convergence
As described before, our simulation goes in regular cycles of topology and coalition updates
chosen to be every 10 seconds in our current setup. During the simulation cycles, coalition
membership updates, in addition to connectivity updates, result possibly in dissolving the
current coalitions into smaller ones. The resulting coalitions, then, exchange cooperation
offers to merge in bigger coalitions. Since coalitions mergers happen randomly with no
predetermined sequence, there may be a possibility for more mergers to happen, in the
same particular cycle, after all coalitions exhaust their chances for cooperation. Exploring
these extra cooperation opportunities may increase the attained reachability, but may add
computational and communication complexity to the model. In this section, we explore
this compromise by studying the possible reachability gain after reachability converges. The
state of reachability convergence is reached when there is no more chances for merge or split
to happen for any coalition in order to increase its reachability gain.
To study reachability convergence, we allow the coalition merge and split process con-
tinue until there is no change in each cycle. We consider three scenarios:
• Cumulative reachability convergence: The coalition update process starts in the
next cycle from the coalitional structure, at which convergence ends in the current
cycle.
• Non-cumulative reachability convergence: The coalition update process starts in
the next cycle from singleton coalitions as if the simulation just started.
• Cumulative reachability with no convergence: The default coalition update
model followed in the simulations. The coalitional structure accumulates from the
current cycle to the next, but with no convergence explored at any cycle.
We look at the second scenario to study reachability improvement and convergence at
each cycle independently and compare this to the cumulative approaches.
60
We study first the average number of cycles required for reachability convergence. This
is the average number of coalition update cycles (as defined above) the network takes, under
a fixed topology, to reach a state of reachability convergence. Figures 5.3(a) and 5.3(b)
show that reachability converges in fewer cycles, on the average, in the cumulative approach
than in the non-cumulative approach. However, the difference between the two approaches
decreases in more dense networks.
A comparison between the distribution of the number of cycles required for reachability
convergence with cumulative and non-cumulative convergence approaches is provided in
Figures 5.4 and 5.5 for low and high node densities, respectively. We disregard the cycle
at which convergence is confirmed, this is when no coalition structure change is detected.
The observation that, in the cumulative approach, a considerable proportion of convergence
happens in the first cycle (0 convergence cycles) emphasizes the superiority of the cumulative
approach.
Figures 5.6(a) and 5.6(b) show the average reachability level at different node densi-
ties and different speeds. The figures, from one side, show that the coalitional structure
established through the cumulative approaches helps obtain an average reachability level far
superior to that attained in the non-cumulative approach. From the other side, the figures
show that the reachability level attained through the cumulative approaches are almost iden-
tical. These observation are also emphasized in Figures 5.7 and 5.8 that show an average
progression of reachability with time in our 20-minutes simulation. The figures show that
the cumulative approaches follow the same trend of increasing reachability while the non-
cumulative approach keeps an almost steady level of reachability throughout the simulation
time.
We conclude that the approach we use in our model (cumulative reachability with no
convergence) provides the best results in terms of attained reachability and computational
complexity in comparison to the two other approaches, namely the cumulative and non-
cumulative reachability convergence.
61
(a) 1 m/s
(b) 4 m/s
Figure 5.3: Average number of reachability convergence cycles.
62
(a) 1 m/s
(b) 4 m/s
Figure 5.4: Distribution of reachability convergence cycles - low node density (500node/km2).
63
(a) 1 m/s
(b) 4 m/s
Figure 5.5: Distribution of reachability convergence cycles - high node density (2500node/km2).
64
(a) 1 m/s
(b) 4 m/s
Figure 5.6: Average reachability level at convergence.
65
(a) 1 m/s
(b) 4 m/s
Figure 5.7: Average reachability progression with time - low node density (250 node/km2).
66
(a) 1 m/s
(b) 4 m/s
Figure 5.8: Average reachability progression with time - high node density (2500 node/km2).
67
Table 5.1: Average dispersion of node’s reachability and average dispersion of every particularnode’s reachability.
Node Density 250 node/km2 1000 node/km2 2500 node/km2
Speed 1 m/s 4 m/s 1 m/s 4 m/s 1 m/s 4 m/s
Avg. dispersion ofnode reachability
8.21% 4.16% 6.48% 6.24% 5.28% 4.68%
Avg. dispersion ofevery node’s reach-ability
2.67% 2.89% 1.41% 1.57% 1.29% 1.30%
5.3.3 Fairness
Another question that logically follows in our study is how much does reachability vary
between nodes with respect to the average reachability gain over the whole network. To
answer this question, we look at two metrics: the average dispersion of node reachability
around the global average reachability, and the average dispersion of each particular node’s
reachability around its own average, as shown in Table 5.1. We use standard deviation as a
measure of dispersion.
The first metric measures how the average reachability of every node is close to the
others’. Table 5.1 shows that, on the average, nodes attained reachability levels close to each
others. For example, for 1 m/s speed, an average standard deviation of 8.21% was observed
at low node density decreasing to 5.28% at high density. The second metric examines how
a node’s reachability fluctuates throughout the experiment. The data show a slight average
change of reachability that also decreases with more dense network scenarios. These two
observations show that the participants in the network under the proposed cooperation
model acquired a close average reachability payoff throughout the experiment lifetime.
An important observation that needs more investigation pertains to the boundary nodes
problem in ad hoc networks [93]. In ad hoc networks, nodes may be tempted not to coop-
erate with those that lie on the network boundary because they are not able to offer much
68
Figure 5.9: Average reachability, maximum coalition size, and coverage - 4 m/s.
help (because they have few connections to other nodes). So, boundary nodes may starve
for cooperation because of their location. The provided results above show that nodes in
MANETs may not suffer from this problem because of mobility.
5.3.4 Coalitional Structure
In this section, we look at how coalitions evolve under the proposed cooperation model. We
studied how the average size and coverage of the largest coalition change with different node
densities. We define coalition coverage as the maximum attainable reachability of a member
of that coalition, which is the total number of nodes that belong to the coalition or its
boundary. Our study shows a natural evolution of the coalitions as node density increases.
Figure 5.9 shows the above mentioned metrics for speed 4 m/s. The figure shows that the
size of the maximum coalition initially increases as node density increases until coverage
reaches near 100%. At this point, the maximum coalition size starts to decrease while the
69
coverage saturates at the 100% level. This is due to the fact that in a more intense network,
nodes will be connected to more neighbors enabling a smaller coalition of connected nodes
to be enough to cover the whole network.
5.4 Summary
In this chapter, we evaluated two basic features of the cooperation model proposed in chap-
ter 4 using a centralized approach simulation. Our simulations show that the reachability
deterioration caused by the topology changes can be restored through a process of coalition
maintenance. We also show that the cumulative coalition formation approach we use gives
the best results in terms of attained reachability level and saved computational complexity.
Our results also show that our proposed model achieves an acceptably fair payoff distribution
among participating nodes. We also provided some insights into the evolution of the formed
coalitions in terms of size and coverage of the maximum coalition.
In the next chapter, we evaluate the applicability of the proposed cooperation model in
practice by implementing it in integration with an existing MANET routing routing protocol
OLSR.
Chapter 6
A Distributed Approach: OLSR
Integration
In Chapter 5, we provided a study of the basic features of the cooperation model proposed
in Chapter 4, sustained reachability improvement and coalition stability in the existence of
topology changes. We used a centralized simulation approach to study those basic features.
In this chapter, we study the applicability of the proposed model as a software module that
works in a distributed manner. We implement the model as an integral part of an existing
MANET routing protocol. We study two main aspects, control traffic overhead and packet
deliverability under selfish behavior.
6.1 Preliminary Choices
MANET routing protocols can generally be divided into three categories [94]: proactive
(table-driven) [95] [96] [97] [97] [98], reactive (on-demand) [99] [37] [100] [101], and hybrid
[102] [103] [104]. In proactive routing protocols, routes to all destinations (or parts of the
network) are determined upon startup, continuously updated using periodic route update
70
71
messages, and maintained in a table at each node. In reactive protocols, routes are deter-
mined when they are required by a sender using a route discovery process. Hybrid routing
protocols combine the basic properties of the first two classes of protocols into one. That is,
they switch between the proactive and reactive modes based on the network characteristics.
In this section we justify our choice of the MANET routing protocol, with which we integrate
our model.
6.1.1 Why proactive protocol?
The cooperation model proposed in Chapter 4 is based on forming coalitions. The process of
forming coalitions requires prior knowledge about the surrounding coalitions. This informa-
tion is used by bargaining coalitions to evaluate the prospective outcome of a merger process.
However, frequent topology changes (as we would expect, based on the results in Chapter 3)
makes this information change continuously causing an ongoing process of merge and split
between the coalitions. In addition, mobility makes all coalitions possible options for future
mergers. Therefore, implementing the proposed cooperation model in a distributed manner
requires exchanging periodic messages to update coalition information at each node. Hence,
we have integrated the model with a proactive MANET routing protocol.
6.1.2 Why OLSR?
Optimized link state routing protocol (OLSR) [95] is one of the most commonly used proac-
tive MANET routing protocols in the research community. Several studies have evaluated
its performance and compared it to reactive protocols (most commonly AODV) [105] [106]
[107] [108] [109]. Due its popularity among researchers, there is a wide support for OLSR
in MANET simulation tools. This includes stability, completeness, and optimization of the
available implementations, in addition to customer support. For these reasons, besides our
72
familiarity with it through the MANIAC challenge [11], we chose to integrate the proposed
cooperation model with OLSR.
6.2 Requirements Specification
6.2.1 Design Requirements
Since OLSR, being a proactive routing protocol, relies on exchanging periodical messages,
the design requirements focused on having minimal disruption to the OLSR messaging mech-
anism. These requirements are:
1. Alignment: We wanted the information exchanged by the cooperation model to be
divided into categories that align in scope and frequency with the currently exchanged
OLSR messages, namely HELLO and TC messages.
2. Overhead reduction: Along the same line, we wanted to reduce the number of new
messages and make use of information that is already included in the OLSR messages,
thus reducing overhead traffic.
6.2.2 Functional Requirements
There are three main requirements that had to be implemented to make the cooperation
model functional:
1. Coalition formation: Coalitions are formed through negotiations. These negotiations
need a messaging mechanism that supports sending and responding to cooperation
offers. Cooperation offers are created periodically based on the reachability information
available at each node about surrounding coalitions.
73
2. Coalition maintenance: To guarantee the availability of updated reachability infor-
mation at each node, a messaging mechanism is required to disseminate two types of
coalition-related information:
(a) Connectivity updates: Changes in the coalition structure caused by connectivity
changes, which in turn are caused by topology changes.
(b) Membership updates: Changes in the coalition structure that follow any merge
or split processes.
3. Stability control: A mechanism that allows coalition formation to happen at a slow
enough rate for all nodes to maintain similar coalition information, after merger pro-
cesses, through periodic update messages. The mechanism should reduce the likelihood
of having several coalition merger processes happening at the same time for the same
coalition. This should reduce control overhead as well.
6.3 Implementation
6.3.1 Message Structure
To satisfy the functional requirements 1 and 2 while the maintaining design requirements, we
modified the OLSR messaging mechanism by extending the the HELLO and TC messages
to carry necessary information to deliver coalition information updates. We also added a
new message COOP that is used to manage coalition formation bargains.
6.3.1.1 Original structure of OLSR Messages
In OLSR [95], HELLO messages perform the task of link sensing, neighbor detection, and
multipoint reply (MPR) signaling. A node, therefore, includes information about the quality
74
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Figure 6.1: OLSR HELLO message.
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Figure 6.2: OLSR TC message.
of links towards its direct neighbors in its HELLO messages. This information is used mainly
to select nodes’ MPRs.
Topology control (TC) messages perform the task of topology discovery and advertise-
ment of link states. A node’s TC message contains information about the set of neighbors
that have selected it as MPR. This set is called the MPR selector set of a node. Nodes, then,
use this information, collected from other nodes’ TCs, to build their routing table. Figures
6.1 and 6.2 show the structure of HELLO and TC messages.
There is a third type of OLSR messages called multiple interface declaration (MID)
messages. MID messages are used to declare the presence of multiple network interfaces on
a node, but this is beyond the scope of our implementation.
75
6.3.1.2 Modified OLSR Messages
From the utility function in equation (4.5), the goal of extending the OLSR messages is to
periodically harvest information about existing coalition membership and border nodes. We
structure our modifications to align with OLSR’s mechanism, where HELLO messages are
used to select MPR and TC messages are used to spread this information. In the integrated
system, a node uses HELLO messages to identify border nodes of the coalition it belongs to.
TC messages are used, then, to spread the border information so that every node can build
its own coalition information base.
We modified the HELLO message by adding only a COALITION ID field to the message
header. As described above, this field is added so that every a node can identify the coalition
affiliation of its neighbors through their HELLO messages. A node uses the collected infor-
mation from neighboring nodes to identify which of them lies on the border of the coalition
it belongs to. In fact, HELLO information identifies only a portion of the border nodes,
those that are directly connected to a node.
We modified the TC message by first adding a COALITION ID field to the message
header. This field, similar to that added to the HELLO message, reflects current node’s
coalition affiliation. Second, to track border nodes, a node includes in its TC message
border node information collected from received HELLO messages. For example, a node
that lies at the heart of a coalition with no connection to any outside-of-coalition nodes may
not include any. The way border nodes are included in a TC message is by marking the
border nodes among those that are already included in the MPR selector set using their
COALITION ID, while the rest are included separately at the end of the message.
A database is maintained at each node that contains for every node its COALITION ID
and share of border nodes. This database is updates every time a HELLO or TC messages
is received. Upon every update, coalition information is regenerated including member and
border nodes. The current implementation is not optimal in terms of space optimization,
and we propose a better design in the future extensions in Chapter 7.
76
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Figure 6.3: Modified OLSR HELLO message.
��
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Figure 6.4: Modified OLSR TC message.
Figures 6.3 and 6.4 show the modified OLSR messages.
6.3.1.3 New Message COOP
To enable the coalition formation process, we introduce a new message, COOP, to carry
merger offers and responses from one coalition to the other through their representatives as
in the model in Chapter 4. Every node periodically tries to initiate a merger process on
behalf of its coalition by sending a COOP message to one of its surrounding neighboring
coalitions that provides the best the best cooperation deal, if any. Like HELLOs, COOP
77
�����������
��� �������
����� ������������
� � � �
Figure 6.5: New introduced message COOP.
messages are sent only to direct neighbors. However since all OLSR messages are broadcast
and we want to align with the OLSR messaging mechanism, we include the destination
address in the message so that only the destination node processes it. Beside the destination
address, we include the COALITION ID and message type. There are four types of COOP
messages, one for offers and three for responses.
1. OFFER: A node sends a merger offer on behalf of its coalition.
2. ACCEPT: A node accepts a merger offer based on evaluating the offer.
3. REJECT: A nodes rejects a merger offer based on evaluating the offer.
4. IGNORE: A node is not willing to process cooperation offers because it just finished
processing a merger offer.
Figure 6.5 depicts the structure of the COOP message.
6.3.2 Stability Control
To support stability of the coalitional structure, functional requirement 3, we use following
techniques:
6.3.2.1 Choosing Best Cooperation Deal
When a nodes takes its turn to initiate a merger process, it needs to choose the best coop-
eration deal available. To guarantee a stable connection between the merging coalitions, a
78
node looks for the best deal among its current MPR selector set. By definition, an eligible
member of the MRP selector set must not be a member of the same coalition and must
provide a positive utility value as in equation (4.7). However, in case of a tie, a node chooses
the node that has the longest remaining expiration time among the MPR selector set, besides
providing the highest positive utility.
6.3.2.2 Coalition Numbering
When a merger process is completed, all nodes that belong to the new formed coalition must
have the same unique COALITION ID. However, changing the COALITION ID of all the
nodes can slow down stability of the new coalition. So, the challenge is to ensure uniqueness
of the new COALITION ID while reducing the number of updates required. To do that, we
use the following rules assuming all nodes belong to the same subnetwork:
1. Singleton coalitions are numbered using the host id part of the IP address. In our
simulations we used the IP address’s fourth octet.
2. New coalitions that are formed upon the merger of two singletons are numbered using
the following equation:
COALITIONIDnew = COALTIONID1×COALITIONID2 +MAXNUMBER
, where MAXNUMBER is the largest number a singleton coalition can take. (6.1)
3. A non-singleton coalition keeps its number when it merges with a singleton. The
singleton changes to the non-singleton coalition number.
4. In case of two non-singletons, the receiver changes its number to that of the offerer.
79
6.3.2.3 Merger Concurrency Control
As indicated in functional requirement 3, the pace of coalition expansion should be controlled
to maintain the stability of the coalition structure. Therefore, we need a mechanism to
reduce the likelihood of having multiple expansions happening concurrently or in close time
proximity. At the same time, we need to reduce the likelihood of temporary disconnections
resulting from mobility. These temporary disconnections can cause a node to separate from
its coalition. To achieve these goals, we introduce a set of timers, intervals, and flags that
are used the following way:
1. stopMsgTimer, idleInterval, and stopMsgFlag:
stopMsgTimer is set for a time period equal to idleInterval upon sending or accept-
ing a merger offer to leave enough time for new coalition information to disseminate
throughout the network. Besides expiration, the timer is released and flag is reset when
a node receives a REJECT or IGNORED message for a previously sent offer.
2. offerTimer, offerInterval, and missedOfferFlag:
At startup, offerTimer is set for time period equal to offerInterval to allow nodes
to periodically send cooperation offers to other coalitions so that coalitions contin-
uously grow. The missedOfferFlag is raised if the offerTimer expires while the
stopMsgFlag is raised, where a node cannot process any merger offers (sending or
accepting) until the stopMsgFlag is released. In this case, when the stopMsgTimer
expires or gets released, the node sends a merger offer to make up for its missed turn.
A node re-evaluate the benefit of being a member in a coalition upon the expiration
of the offerTimer according to equation (4.7).
3. separationTimer, separationInterval, and separationFlag:
separationTimer is set for time period equal to separationInterval to detect phys-
ical connectivity of a node from the coalition it belongs to. A node will be considered
disconnected from a coalition if it stays disconnected from all of its coalition members
80
for a time period equal to separationInterval. Connections with coalition mem-
bers are checked upon receiving Hello messages. A node is said to be connected to its
coalition if at least one coalition member exists in its neighbor table. This can check
individual connections to a coalition, but does not check group disconnections.
Upon the first encounter of node disconnection, the separationTimer is set for a
time period of separationInterval and the separationFlag is raised. The timer
is canceled and flag is reset if a connection is detected before the timer expires. The
timer and flag are also reset, if already activated, if a node decides to leave its current
coalition after re-evaluating the benefit of being a member of the coalition it currently
belongs to as described above.
6.4 Simulation setup
We use OMENT [92] to simulate the integrated COOP-OLSR protocol. Network nodes
were simulated with a complete network stack up to the application layer where traffic is
generated (traffic generation is discussed in the next section). As shown in Figure 6.6, each
node consists of the following modules in the network stack:
• wlan: A network interface card module (NIC) that simulates basic functionalities of
the PHY and 802.11 MAC layers, in addition to ad hoc management. We simulated a
free space propagation model at the physical layer, so the effect of interference at the
receiving nodes was simulated.
• networkLayer: A module that simulates IP protocol with related functions such as
ARP and ICMP .
• manetRouting: A module that simulates the OLSR protocol and feeds the IP module
with the resulting MANET routing table.
• udp: Simulates a UDP protocol in the transport layer..
81
Figure 6.6: Simulated node structure.
• udpApp: A module that simulates an program in the application layer that sends
random UDP traffic across the network.
In addition to the network stack, nodes were moving according to a random waypoint
mobility model (RWP module) [110] with 0 pause time. Table 6.1 shows values of the most
important setup parameters.
82
Table 6.1: Simulation setup.
Scope Simulation Parameter Value
Communication ChannelPropagation Model Free Space
Carrier Frequency 2.4 GHz
RadioCommunication Range 92 m (300 ft)
Bit Rate 54 Mbps
MAC
Standard 802.11
RTS Threshold 3000 B
Retry Limit 7
MobilityModel Random Waypoint
Pause Time 0 seconds
ARP
Retry Count 3
Retry Timeout 1 second
Cache Timeout 100 seconds
OLSRHELLO Interval 2 seconds
TC Interval 5 seconds
6.5 Simulation Scenario
Similar to the centralized simulation evaluation of the model, we simulated the integrated
system under different node densities and different mobility speeds. However, we also simu-
lated different network size scenarios to give a more complete view, especially of the overhead.
To study the reliability of the integrated system in the face of selfish behavior, we ran random
traffic across the network and studied the packet delivery ratio (PDR) under selfish behavior.
To simulate a realistic traffic generation pattern, every node generated UDP packets accord-
ing to a Poisson process (implemented in the simulation using an exponential distribution
with different packet rates). The destination of each packet was randomly selected from all
other nodes.
We simulate selfish behavior by having a node randomly drop packets requested to be
83
Table 6.2: Simulation parameters and scenarios
Scope Simulation Parameter Value
Selfishness Dropping Probability (DP) 0 and 0.5
COOP
offerInterval 7 seconds
idleInterval 2 seconds
separationInterval 1 second
Traffic Generation
Type UDP
Packet Size 1 KB (1024 B)
PatternExponential(µ),
µ = 0.2, 0.33, 1, 2, 10 pkt/second
Scenarios
Case 1 No COOP - 0 DP
Case 2 COOP - 0.5 DP
Case 3 No Coop - 0.5 DP
Run Duration 20 minutes
Node Densities 250, 500, 750, 1000, 1250, 1500, 1750 node/km2
Network Size 25, 50, 75, 100 nodes
Speed 1, 2, 3, 4 m/s
forwarded according to a predefined probability. According the model description in Chapter
4, being a member of a coalition, a node may behave selfishly only if neither the source nor
the destination of the packet belong to its coalition. Finally, for every combination of node
density, network size, and speed, we simulate three cases:
• Case 1: Ideal and original case where only OLSR is running without the cooperation
model, and there is no selfish behavior. This case is used as a benchmark for the PDR
of the second cases to compare to.
• Case 2: Cooperation model is employed with existing selfish behavior. This is the
actual case we are interested in, in which we study how the coalitional cooperation
mitigates the selfish behavior.
84
• Case 3: Bottom line scenario where only OLSR is running with existing selfish behavior.
This is situation that case 2 seeks to improve.
Table 6.2 summarizes parameters of the simulated scenarios.
6.6 Simulation Results
We evaluate the integrated system using four metrics: reachability, accuracy of coalition
information, control traffic overhead, and reliability. We simulate the three scenarios de-
scribed in the last section under different node densities, network sizes, speeds, and UDP
traffic loads. However, for some metrics, the resulting figures are very similar under one
or more of the before-mentioned aspects. In such cases, we point to this similarity, and
show only the different figures. All curves in this section are generated at a 95% confidence
interval.
6.6.1 Reachability
The first thing we look at is the main metric of the cooperation model, reachability. We study
average reachability under the integrated system, where the cooperation model is functioning
in parallel to modules of the network stack. Some of these modules in the network stack are
already exchanging messages like (e.g., MAC, ARP, ICMP) which can affect the functionality
of the cooperation model. This is as compared to the simulation study in Chapter 5, where
only the cooperation mechanism was running, all information was completely and accurately
available at a central management module, and no actual message exchange was simulated.
So, the goal of re-studying reachability is to ensure that cooperation operates well in more
realistic scenarios.
To ensure a complete view, we study reachability over different node densities from three
angles: different network size, different mobility speeds, and different levels of traffic load.
85
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Figure 6.7: Average reachability vs. node density for different network sizes, speeds, andtraffic loads.
86
Figures 6.7(a), 6.7(b), and 6.7(c) show the three angles respectively. Simulation scenario
Case 2 is used in all the plotted points. The figures exhibit the same trend regardless of
network size, speed, and network load. We can observe that node density is the major factor
in all the cases, where reachability follows node density and stabilizes at large densities.
However, we expect that changing the communication range contributes to shaping this
behavior as well.
6.6.2 Coalition Information Accuracy
Since we are simulating a distributed approach, the time needed to disseminate coalition
information can cause inaccurate information to persist at each node due to latency. In fact,
this kind of discrepancy has been spotted in OLSR [111] based on the MANIAC challenge [11]
experimental data. Therefore, we wanted to study accuracy and completeness of coalition
information at each node based on our implementation.
We measure accuracy of coalition information at a node at time t by comparing accumu-
lated information at that node to the actual coalition affiliation information collected from
each node at the same time. We calculate the accuracy at a node at time t as the percentage
of nodes with coalition correctly identified out of the total number of nodes that the node
has coalition information on. We also measure completeness of the accumulated coalition
information at a node at time t simply as the percentage of nodes in the network (network
size) on which the node has coalition information.
Accuracy(t) =Number of correctly collected nodes
Total number of collected nodes(6.2)
Completeness(t) =Total number of collected nodes
Network size(6.3)
Figures 6.8 and 6.9 show the average accuracy and completeness of the collected coalition
87
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Figure 6.9: Average completeness of collected coalition information vs. node density at 2m/s speed and 0.2 packet/second traffic load.
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Figure 6.10: Maximum coalition size vs. node density at 2 m/s speed and 0.2 packet/secondtraffic load.
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89
information across the whole network over the simulation time for different network sizes.
Different speeds and traffic loads exhibit similar behavior, so we omit them. The figures show
that the average accuracy was always above 96% for all densities, speeds, traffic loads, and
network sizes. However, we can notice that the completeness of the information deteriorates
in small size networks especially in high densities. The reason for that is because in small and
dense network, reachability is close to 100% with a node’s sole communication capabilities.
In other words, every node is reachable in only one hop. This not only translates to less and
smaller formed coalitions, but also influences the OLSR behavior.
Figures 6.10 and 6.11 show that the size of the maximum coalition shrinks significantly
as the network gets smaller and denser until it reaches 1 in network size 25 and density 1750
node/km2, while maintaining full coverage. In OLSR, TC messages are sent only if the set
of MPR selectors is not empty. As the network gets smaller and denser, a node will need
few or no MPRs to extend its reachability. This directly translates to fewer TC messages
being broadcast, which is shown in Figure 6.12 in the next subsection. This, in turn, leads
to less coalition information distribution in the network.
6.6.3 Control Traffic Overhead
Control traffic overhead has always been a concern in evaluating the performance of proactive
MANET routing protocols and their extensions [94]. In OLSR, control traffic overhead is
caused by HELLO and TC messages (MID messages are beyond the scope of this study).
Although HELLO messages are transmitted more often than TCs, it is TC messages that
is responsible for most of the incurred overhead [112]. This is because TC messages are
broadcast to the whole network, while HELLOs are broadcast only to direct neighbors.
We study the traffic overhead the cooperation model incurs by comparing it to the
overhead OLSR incurs when it is running alone. We compute a ratio between the total
overhead of the cooperation model and OLSR working together and the overhead of OLSR
alone. For TC messages, overhead is increased every time the message is re-broadcasted.
90
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OverheadRatio =COOP + TCCooperation +HELLOCooperation
TCOLSR +HELLOOLSR
(6.4)
Figure 6.12 shows the average overhead for different network sizes over a range of node
densities. Different speeds and traffic loads exhibit similar shapes, so we omit them. The
figure shows that the overhead increases dramatically when the network size increase even
with a similar node density. This may be due to the flooding nature of the TC messages,
which makes the overhead caused by re-broadcasting the TC messages increase non-linearly
as more nodes need to receive the broadcast message. However, this non-linear effect is not so
apparent here because of the flooding optimization of OLSR caused by the MPR algorithm.
91
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92
6.6.4 Reliability
Finally, we evaluate the reliability of the integrated system by looking at the packet delivery
ratio (PDR). Random UDP traffic is generated by every node to every node according to an
exponential distribution with a packet rate ranging from 0.2 to 10 packet per second. We
study the packet delivery ratio for the 3 cases described in the simulation scenario section.
Figure 6.13 shows the packet delivery ratio for the three cases. We did not include
different speeds and traffic loads because they exhibited similar shapes expect for a decreased
PDR for higher speeds, as can be expected. The figure shows that, in all simulated network
sizes, the highest improvement in PDR of the integrated system, in the existence of selfish
behavior, happens at low network densities. This is because more routes longer than 1-
hop exist in low density networks. This gives a chance for the cooperation model to show
effectiveness in multi-hop traffic flows.
6.6.5 Summary of Simulation Observations
We can summarize the observations from the simulation results as follows:
1. Performance of OLSR solely in terms of PDR, with no cooperation nor selfish behavior,
is poor in large size networks.
2. The cooperation model is not so effective in dense network scenarios because short
routes are dominating, therefore selfish behavior does not significantly influence traffic
deliverability. Hence, there is not much need for cooperation.
3. The cooperation model incurs an overhead more than double that incurred by OLSR
in big size networks (more than 50 nodes), while it goes below double in small-medium
size networks (less than 50 nodes) with similar average PDR improvement and better
OLSR performance.
93
Based on the summarized observations, the proposed coalition formation protocol would
be appropriate for use in small-medium size networks (less than 50 nodes) and for low
density networks (less than 750 node/km2). The recommendation would need to be scaled
appropriately, though, for different communication ranges.
6.7 Summary
In this chapter, we provided a more realistic evaluation of the proposed cooperation model
from chapter 4. We simulated the cooperation model as an integral module in the OLSR
routing protocol. The simulated nodes had fully functional network stack modules. We
simulated the integrated system under different node densities, network sizes, speeds, and
traffic loads. The simulations showed that the formed coalitions achieved and maintained
high reachability and coalitional stability as was concluded in the preliminary study in Chap-
ter 5. In addition, we studied the control overhead and reliability of the integrated system
measured in packet delivery ratio. Our simulations showed a considerable improvement in
the packet delivery ratio in low and medium node densities, with a rather high overhead ratio
at the same node density. We suggested that the proposed coalition cooperation protocol
would be appropriate for use in small-medium size networks, and for low density scenarios.
In the next chapter, we conclude this work and propose future extensions that include
alleviating some drawbacks of the current implementation, especially the high overhead.
Chapter 7
Conclusions and Future Work
7.1 Conclusions
In this dissertation, we have explored the problem of incentivizing cooperation in mobile ad
hoc networks. In a mobile ad hoc network, there is no infrastructure to support information
exchange like dedicated routers or access points. Rather, nodes have to play the role of relays
to help deliver traffic across the network. With this responsibility, selfish behavior may arise
to preserve nodes’ energy. Cooperation among members of such networks is essential to keep
them operational in the face of selfish behavior.
Throughout our exploration of this problem, we shed light on important topology char-
acteristics of real MANETs as compared to simulated ones. These include node degree, link
stability, and link symmetry. We used data from the MANIAC challenge as a source for real
MANET traces in our study. We showed similarity between the node degree distribution
of the real and simulated MANET, while links were much more unstable and asymmetric
in real life than was anticipated in simulated MANETs. To alleviate this discrepancy, we
suggested modeling link status in a MANET using a two-state Markov chain model. Due
to lack of available traces from real MANET experiments, we could not statistically confirm
94
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these qualitative results. However, with more analytical work, we believe that this study
can produce promising models to better simulate link status in MANETs.
Inspired by our study, we focus on one MANET characteristic that hinders cooperation
and has not been thoroughly studied in the literature: mobility. We showed the effect of
mobility on making cooperation bonds short-lasting in MANET, and proposed a coopera-
tion model based coalition game theory that aims to build a stronger cooperation structure
that withstands the topology changes caused by mobility. The model uses the notion of
reachability as a cooperation incentive. We, analytically, showed that the formed coalitional
structure maintains pairwise stability.
We evaluated the proposed cooperation model using centralized and distributed ap-
proaches. The centralized approach evaluation aimed to verify basic features of the model:
sustained reachability improvement and coalitions stability. We conducted simulations of
the cooperation model running on randomly generated MANET topologies. Our simula-
tions showed that the formed coalition structures restored reachability after different levels
of deterioration caused by topology changes at different mobility speeds. In addition, we
showed that the model achieves an acceptably fair payoff distribution among the all network
nodes.
Finally, we went a step further to evaluate the operability of the proposed model in
realistic scenarios. We integrated the model with a proactive MANET routing protocol,
OLSR. The integrated module has been inserted in the TCP/IP network stack, and full
functionality has been simulated on MANET nodes. We simulated MANET scenarios with
different node densities, network sizes, traffic loads, and mobility speeds. The simulation
confirmed our results from the centralized simulation study. In addition, we studied the
overhead caused by the model beyond that caused by OLSR alone, along with achieved
packet deliverability. We showed a considerable improvement in the packet delivery ratio in
low and medium node densities, with a rather high overhead ratio at the same node density.
96
We suggested the use of the integrated system in small-medium size networks and for low
density scenarios.
7.2 Publications
The following list summarizes the publications resulting from this dissertation:
• Journal Articles:
– Amr E. Hilal and Allen B. MacKenzie, “A Distributed Coalition Game Modelfor Cooperation in MANETs and Its Implementation,” submitted to IEEE Trans-actions on Mobile Computing.
– Amr E. Hilal, Michael S. Thompson, Allen B. MacKenzie and Luiz A. DaSaliva,“Qualitative Differences between Real World and Simulated MANET Character-istics,” in preparation.
• Conference Papers:
– Amr E. Hilal and Allen B. MacKenzie, “A Coalition Game Model of Cooperationin Ad Hoc Networks with Mobility,” The 8th IEEE International Conferenceon Wireless and Mobile Computing, Networking and Communications (WiMob2012), Barcelona, Spain, October 8, 2012. Student best paper award.
– Michael S. Thompson, Amr E. Hilal, Luiz A. DaSaliva and Allen B. MacKen-zie, “The MANIAC Challenge: Exploring MANETs Through Competition,” In-ternational Workshop on Wireless Networks: Communication, Cooperation andCompetition (WNC3 2010), Avignon, France, May 31, 2010.
– V. Srivastava, A. E. Hilal, M. S. Thompson, J. N. Chattha, A. B. MacKenzie,and L. A. DaSilva, “Characterizing Mobile Ad Hoc Networks - The MANIACChallenge Experiment,” The Third ACM International Workshop on WirelessNetwork Testbeds, Experimental Evaluation and Characterization (WiNTECH2008), San Francisco, CA, Sept. 19, 2008.
• Demonstrations:
97
– A. E. Hilal, J. N. Chattha, V. Srivastava, M. S. Thompson, A. B. MacKenzie,and L. A. DaSilva, “Interactions between Cooperation Strategies in Mobile AdHoc Networks,” The Third ACM International Workshop on Wireless NetworkTestbeds, Experimental Evaluation and Characterization (WiNTECH 2008), SanFrancisco, CA, Sept. 19, 2008.
7.3 Future Extensions
7.3.1 Weighting Parameter for Benefit and Cost of Cooperation
In the current formulation of our cooperation model, when coalition X negotiates a possible
merger with coalition Y , node i, a representative of coalition X uses the utility function
UX(Y ) (4.5) to evaluate the cooperation opportunity. This utility represents the difference
between the benefit members of coalition X gain by merging with coalition Y (4.3), which
is measured by the number of additional nodes that will be reachable in the newly merged
coalition, and the cost they pay to merge with coalition Y (4.4), which is measured by the
number of additional nodes for which packet relaying service will be provided (that is the
additional nodes in the merge coalition), with equal weights for both.
UX(Y ) = ui(X ∪ Y )− ui(X)
...
= BenefitX(Y )− CostX(Y )
Although the benefit and cost in (4.3) and (4.4) are computed in terms of number-
of-nodes that should provide or receive forwarding services for node i, respectively, some
situations may require giving a higher weight to either benefit or cost. For instance, in delay
tolerant networks (DTNs) that may lack continuous network connectivity [113], nodes may
98
value the benefit of cooperating with an additional node more than the cost of supporting
it, to mitigate the connectivity problem. On the other hand, in a dense network, the coop-
eration benefit may be overlooked to avoid the load of serving others. Therefore, we propose
adding a weighting parameter, namely α, to the utility function UX(Y ) to accommodate such
scenarios, where α > 1 means that the cost of supporting an additional coalition member
is greater than the benefit associated with reaching an additional node, and α < 1 means
that the benefit of reaching an additional node is greater than the cost of supporting an
additional coalition member. Hence, UX(Y ) will be formulated as:
UX(Y ) = ui(X ∪ Y )− ui(X)
= |BX∪Y | − |BX |
= [Benefiti(X ∪ Y )− α · Costi(X ∪ Y )]− [Benefiti(X)− α · Costi(X)]
= [Benefiti(X ∪ Y )−Benefiti(X)]− α · [Costi(X ∪ Y )− Costi(X)]
= BenefitX(Y )− α · CostX(Y )
= [|Y | − |Y ∩BX |+ |BY \ (X ∪BX)|]− α · |Y |
= ((1− α) · |Y |) + |BY \ (X ∪BX)| − |Y ∩BX |
In addition to the use of the weighting parameter α to control the relative valuation of
benefit and cost for the whole network, the concept can be further extended to achieve a
more fair payoff distribution among members of the same coalition. For example, although
all members of a coalition obtain same reachability benefit, some of them will pay a higher
cost by receiving more packet forwarding requests because of their topological position in the
network. These nodes provide a greater marginal contribution to the network than others.
This effect may be mitigated on the long run because of mobility, as we showed in Chapter
5, however it could be unfair in the short run.
In such cases, reachability gain can be proportional to the marginal contribution of a
node to the coalition it belongs to. The marginal contribution of a node can be expressed
99
in terms of the likelihood of receiving a forwarding request for the benefit of other coalition
members, which in turns depends on the node’s position within the coalition and the entire
network. With that description, a node may not be guaranteed reachability to the complete
scope of the coalition in the short term, but can restore it as it moves around within its
coalition. Although this modification can provide a more fair payoff distribution, it will
require a more sophisticated communication mechanism to compute and disseminate the
node’s marginal contribution information.
Studying stability of the cooperation model and the impact on the coalitional structure
for different values of α and for the extended payoff distribution model would be a valuable
extension of this work.
7.3.2 OLSR Route Calculation Based on Coalition Information
The way the cooperation model is currently integrated with OLSR helps fight selfish be-
havior through the cooperation agreement: Do not drop packets sent from or destined to
coalition members. However, this can be more effective if OLSR proactively uses the coali-
tion information available at each node to build its routing table. For example, OLSR could
be modified to give priority in choosing MPRs to coalition mates such that resulting routes
involve more cooperative nodes, hence achieving more reliable routes. This could greatly
enhance the packet delivery ratio of the network under the integrated system. We believe
this could be a promising extension of the current work.
7.3.3 Control Overhead Reduction
In our discussion of the simulation results in Chapter 6, we observed that the control overhead
incurred by the cooperation model can reach as high as double that incurred by OLSR alone
at a network size of 100 nodes, as shown in Figure 6.12. The overhead, however, stays as
high as in OLSR in medium-size network (50 nodes) as shown in the same figure.
100
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In reality, most of the control overhead observed in OLSR-routed networks is due to the
TC messages [112]. However, there is a fundamental difference that makes the size of the
cooperation model’s share of the modified TC message grow as node density increase, as
shown in Figure 7.1. Namely, the size of the set of border nodes scales up as the network
become denser, while the size of the MPR selector set that OLSR injects into the TC message
may not be affected as much by increasing node density. Accordingly, in OLSR, the share
of TC messages in the total overhead scales down as the network becomes denser (and the
size of HELLO messages becomes dominant in high densities), while TC messages remain
the dominant component of overhead in the cooperation model, as shown in Figure 7.2.
Therefore, optimizing the size of the TC messages for the cooperation model can significantly
reduce the total overhead incurred by the model.
A suggested optimization that can be studied in future work is to exploit the fact that all
node information exchanged by the model, namely IP address, has already been exchanged
101
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Figure 7.3: Optimized structure of the modified TC message.
in previous OLSR communications. Therefore, the model can use a reduced form of this
information that can be uniquely mapped to its original form. For example, a node can
be represented by only the host-id part of its IP address given that all nodes are on the
same subnetwork. In this case the TC message can be re-structured as shown in Figure 7.3,
leading to a reduction of 60% - 70% of the message size, and hence the total model overhead.
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