Reactive-Greedy-Reactive in Unmanned Aeronautical Ad-hoc Networks

139 Pages·2012·5.91 MB·English

Reactive-Greedy-Reactive in Unmanned
Aeronautical Ad-hoc Networks: A Combinational
Routing Mechanism
by
Rostam Shirani, M.A.Sc.
A thesis submitted to the
Faculty of Graduate and Postdoctoral Affairs
in partial fulfillment of the requirements for the degree of
Master of Applied Science in Electrical and Computer Engineering
Ottawa-Carleton Institute for Electrical and Computer Engineering
Department of Systems and Computer Engineering
Carleton University
Ottawa, Ontario
August 2011
©Copyright
Rostam Shirani, 2011
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The undersigned hereby recommends to the
Faculty of Graduate and Postdoctoral Affairs
acceptance of the thesis
Reactive-Greedy-Reactive in Unmanned Aeronautical
Ad-hoc Networks: A Combinational Routing Mechanism
submitted by Rostam Shirani, M.A.Sc.
in partial fulfillment of the requirements for the degree of
Master of Applied Science in Electrical and Computer Engineering
Professor Thomas Kunz, Thesis Co-supervisor
Professor Marc St-Hilaire, Thesis Co-supervisor
Professor Howard Schwartz, Chair,
Department of Systems and Computer Engineering
Ottawa-Carleton Institute for Electrical and Computer Engineering
Department of Systems and Computer Engineering
Carleton University
August, 2011
n
Abstract
Based on the rapid growth in the applications of Unmanned Aerial Vehicles (UAVs) in
the last decade, the idea of in-flight communication among UAVs has been proposed in
cooperative missions. Since medium and small sized UAVs are cheap and deployable,
several of them can be used to form what we call an Unmanned Aeronautical Ad-hoc
Networks (UAANETs).
Due to the availability of location information, geographic protocols can be an
option for routing in UAANETs. Although we show that greedy geographic forward
ing alone is not sufficient in UAANETs, our results illustrate that a combination
of greedy geographic forwarding with a reactive mechanism, which forms a Reactive-
Greedy-Reactive (RGR) routing, can be beneficial. Simulation results show that RGR
outperforms existing protocols such as Ad-hoc On-demand Distance Vector (AODV),
and greedy geographic forwarding in searching missions in terms of delay and packet
delivery ratio while its overhead is comparable with traditional mechanisms.
in
In memory of Hajieh Khanom Jahan Shiran
To my parents, Manijeh and Mahmoud, and my sister Roya
Acknowledgments
I would like to express my deep and sincere gratitude to my supervisors Professors
Thomas Kunz and Marc St-Hilaire for the end-less support, understanding, kindness,
and great guidance. During my studies at Carleton University, they not only helped
me in my research but also they taught me in my life.
I also would like to acknowledge researchers in Mobile Ad-hoc and Sensor Net
work Systems group in Communication Research Center (CRC) Canada, especially
Dr. Yifeng Zhou, Dr. Jun Li, and Mrs. Louise Lamont not only for funding the
project but for supporting me as a visiting researcher in CRC during the past year.
Last but not least, I would like to thank my family, my parents and my sister,
who are my reasons in life.
v
Table of Contents
Abstract iii
Acknowledgments v
Table of Contents vi
List of Tables x
List of Figures xi
List of Acronyms xv
1 Introduction 1
1.1 Research Objective 2
1 2 Thesis Contributions 2
1 3 Organization of the Thesis 3
2 Related Work on UAANET 5
2.1 In! loduct ion 5
2 2 I nmanned Aeionautical Ad-hoc Networks 6
2.2.1 { AY Operations 6
2.2 2 Applications oi UAANETs 7
2 2.3 Differences between UAAXKI s and AANhls 8
2 3 Cooperation. Contiol and Path Planning 8
2.4 Not woi king Characteristics of UAANETs 11
2 1.1 Rapid Topolog> Change 11
2.4.2 Apphc at ion-based Mobility 12
2.4 3 Medium A< cess Control Roqmioments 12
vi
2.5 Reactive Pouting 13
2 (> Geographic Routing 14
2.C 1 Location Son ice Modulo 15
2.6 2 Location Prediction in Geographic Routing 16
2 7 Towaids a Routing Protocol for UAANETs 17
2.7 1 Greedv Geographic Core in Data Forwarding 18
2.7.2 Reactive-Greedy Combination 18
2 8 Summary 19
3 Greedy Geographic Forwarding in UAANETs 20
3.1 Introduction 20
3 2 Background 21
3 3 Simulation Environment 22
3.4 Simulation Results 24
3.5 Observations From Simulation Results 27
3 6 Quadratic Estimation of Success Probability 28
3.7 Summary 32
4 Reactivc-Greedy-Reactivc Routing for UAANETs 34
1 1 Introduction 34
4.2 Motivation 36
4.2.1 Blind Broadcasting 36
4.2.2 Geographic Reverse-route Forwarding 36
4.2.3 Reactive Reverse-route Forwarding 37
4.2 4 Proactive Reverse-route Forwarding 37
'J 2.5 Discussion on the Proposals 37
1.3 Implementation of RGR in a Network SimuLitoi 38
4.3 1 0\er\ie\v of AODY in OPNET 39
4.3.2 RGR Implementation Overview 40
1.1 RGR Functionality 41
4.4.1 Elements ol RGR Mechanism 41
1 4.2 Node Tables 43
4 4.3 Reactive Route 44
4.4.4 Packet Forwarding in an Intermediate Node 45
J 1.5 Route Ertors 46
vrr
4.4.6 Switching to Greech- Geographic Forwarding 47
4.4.7 Handling a Received Packet via Cjreedv Geographic Forwarding 47
4.4 8 Dropping a Packet in iho Giooch Geographic Function . . .. 48
4.1.9 Global Repair bv the Source 49
4 4.10 Destination Operations 49
4.5 RGR versus Reactive and Geogiaphu Routing 51
1.6 Summarv 53
5 Simulation Results 55
5 1 Introduction 55
5.1 1 Packet Delivery Ratio 56
5 1.2 1 )elav 57
5.1.3 Overhead 57
5.2 Mobility Modelling 58
5.2.1 Mobility Parameters for Searching Mission 59
5 2.2 Mobility Parameter tor Tracking Mission 60
5.3 MAC7PHY Specifications 62
5.1 Network Iraffie 62
5 5 Routing Protocol Setting 64
5.6 Simulation Results for a Searching Mission 65
5.6.1 Small Network with 10 UAVs 66
5 6.2 Largo Network with 20 t'AYs 73
5.7 Simulation Results for a Tracking Mission 82
5.b Number of Switches 88
5 9 Summary 89
6 Conclusions and Future Work 91
0.1 Conclusions 91
6 2 Future Work 92
List of References 95
Appendix A OPNET Implementation 102
A.l Adding Local ion Infbrmat ion in Header Files 102
A.2 Switching to Greedy Geographic Forwarding 103
A 3 Lifetime 103
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A. 1 Create Functions and Arrival Functions in RGR 104
A.5 Statistics of the Number of Switches 105
Appendix B 10 Searching UAVs in a 25 km'2 Area 107
Appendix C Searching Mission with 30 Nodes 113
Appendix D Tracking Mission with 20 Nodes 118
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