1. Underwater Swarm Sensor Networks:
Applications, Deployment, and
Medium Access Communication Protocols
By
Gunilla Elizabeth Burrowes
BE, MPhil
Doctor of Philosophy
January 2014

2. ii
Statement of Originality
The thesis contains no material which has been accepted for the award of any other degree or
diploma in any university or other tertiary institution and, to the best of my knowledge and belief,
contains no material previously published or written by another person, except where due
reference has been made in the text. I give consent to the final version of my thesis being made
available worldwide when deposited in the University’s Digital Repository, subject to the
provisions of the Copyright Act 1968.
………………………………………
Gunilla Elizabeth Burrowes

3. iii
Dedication
This thesis is dedicated to my parents
who I am indebted to for giving me their love of learning and life
To the memory of my father,
Richard (Dick) Ranson BE Hons(Syd)
who inspired and supported me to become an engineer
and
To my mother,
Kerstin Ranson
who continues to inspire me everyday with her determination and affection.

4. iv
Acknowledgements
A piece of work such as this thesis is never done alone and I have so many people to thank and
acknowledge.
Firstly, to my supervisors who has supported me to achieve this goal. It has taken so much
longer than planned for many reason and I would like to particular thank my supervisor A/Prof
Jamil Khan for staying with me throughout this journey and always being available when
needed. Thank you also to Dr Jason Brown as my co-supervisor who has provided invaluable
support with OpNet, and was always willing to help with ideas and enthusiasm.
To my family, who if they were not by my side, I could not and maybe would not have completed
this thesis. In particular, to my husband Darren, thank you so much for all your love and support
over the years and the encouragement to keep going. To Edward and Ingrid, my beautiful
children, who are my pride and joy; thank you for your understanding during the many times that
I could not be there for you and for the inspiration that you gave me to succeed.
And also to my parents who have always believed in education. I will be eternally grateful for
their continual interest and faith in what I have done in my life that has lead me to the path of
taking on the challenge of a PhD. Thankyou to my mother, Kerstin Ranson, for her wisdom in
my life and to my brothers Eric and David and sister Caroline and their families who I am so
lucky to share my life with.
I am also indebted to my many colleagues who have had to take on extra work to allow me time
to continue to study. To my business partner, Dr Mark Toner, thank you for continuing the
business almost without me and for your words of encouragement and humor. Thank you also
to Prof David Dowling for all your advice and words of support.
And to all my wonderful friends, thank you for being there to share a coffee and a laugh.
And finally to my study buddy, my wonderful boxer dog, Ronia who passed away last Christmas
before I could finish this work.

5. v
Abstract
Our oceans are vast and remain mostly unexplored. Advances in underwater technology have
enabled exciting new applications for underwater wireless sensing and monitoring of the
environment, fauna, flora, and human activity. The 'game changer', however, for future
developments will be when swarms of mobile vehicles are able to undertake autonomous
missions as they will increase the usefulness and ability to begin extensive sampling of the
earth's oceans to gain an insight into this unknown world.
Current solutions have been built around static sensor networks and single ROV’s (remotely
operated vehicles) and single AUV’s, which have been typically sparsely deployed. The growth
of underwater operations will require data communication between various homogeneous and
heterogeneous underwater networks and surface based equipment.
This thesis has focused on the communication requirements and medium access control (MAC)
algorithms for groups of AUV’s operating in close proximity to each other in a swarm-like fashion
as an underwater swarm sensor network (USSN). An investigation into the various applications
that would benefit from using a swarm of AUV’s has lead to the classification of Non-time
Critical Missions, for mapping and surveying for example and Time Critical Missions for using
real-time payload data collection for searching for an object or target. This leads to two topology
configurations, a Bus Topology and Cluster Topology respectively that requires different Quality
of Service boundaries and MAC methods.
The requirement to operate vehicles at very close-range has meant an investigation into the
atypical short-range underwater acoustic channel and the spatial-temporal diversity that
acoustic communications between devices underwater create which is different from long-range
underwater acoustic communications and very different from RF communications in terrestrial
settings. This work has also studied the data exchange needs of swarming algorithms with a
focus on bio-inspired algorithms that can be used in a group of AUV’s to facilitate the formation
of vehicles in particular the Cluster Topology.
To maintain swarm synchronisation in both Topologies real-time communication is required in a
fully connected but distributed group of underwater vehicles (AUV) operating in an USSN. Two
MAC layer protocols were developed for the different application areas: “Adaptive Token Polling
MAC (ATP-MAC)” has used an adaption of a token polling ring to provide a decentralised
distributed MAC protocol for the Non-time Critical Missions and “Adaptive Space Time – Time
Division Multiple Access (AST-TDMA)” protocol that utilising a token to trigger time divisions
between vehicles rather than a clock used in TDMA is a fully distributed decentralised algorithm.
Both protocols are designed to effectively use a single channel broadcast acoustic environment
while incorporating a method to handle the spatial-temporal characteristics experienced
underwater. They allow operations to be independent of time synchronization between vehicles
and require no prior knowledge of propagation delays.

6. vi
Analytical results presented in this thesis show both the AST-TDMA and ATP-MAC protocols
exhibit substantial advantages over the conventional TDMA protocol for the applications that
they are designed for. It is shown that the new adaptive protocols outperform TDMA in their
ability to disseminate time-sensitive information in a timely manner and therefore allow much
higher densities of vehicles to operate in swarm-like networks in both the Bus and Cluster
Topologies studied.
The AST-TDMA protocol operations in a non-ideal underwater communication channel have
also been simulated and the results are presented and analysed. This non-ideal channel
includes the simulation of noise and reverberation models. A proposed new type of
reverberation, Swarm Reverberation, has also been introduced and incorporated in the
analysis.

7. vii
Associated Publications
The following publications are associated with work in this thesis:
[1] Burrowes, G.E., Khan, J.Y., “Adaptive Token Polling MAC Protocol for Wireless
Underwater Networks.” International Symposium on Wireless & Pervasive Computing.
Melbourne, 2009.
[2] Burrowes, G.E., Khan, J.Y., “Investigation of a Short Range Underwater Acoustic
Communication Channel for MAC Protocol design”, 4
th
International Conference on
Signal Processing and Communication Systems (ICSPS) 2010, IEEE Conference
Publications, Digital Object Identifier: 10.1109/ICSPCS.2010.5709665
[3] Burrowes, G.E., Khan, J.Y., “Short-range Underwater Acoustic Communication
Networks.” In Autonomous Underwater Vehicles, by Nuno A Cruz, 173-198. Croatia:
InTech, 2011.
[4] Burrowes, G.E., Brown J., Khan, J.Y., "Adaptive Space Time - Time Division Multiple
Access Protocol (AST - TDMA) for an Underwater Swarm of AUV's". IEEE OCEANS,
Bergen, June 2013
[5] Burrowes, G.E., Brown J., Khan, J.Y., "Impact of reverberation levels on short-range
acoustic communication in an Underwater Swarm Sensor Network (USSN) and
application to transmitter power control". IEEE OCEANS, St Johns, Sept. 2014

8. viii
Table of Contents
STATEMENT OF ORGINALITY II
DEDICATION III
ACKNOWLEDGEMENTS IV
ABSTRACT V
ASSOCIATED PUBLICATIONS VII
TABLE OF CONTENT VIII
LIST OF TABLES XIV
LIST OF FIGURES XV
ABBREVIATIONS AND SYMBOLS XIX
CHAPTER 1 INTRODUCTION
1.1 BACKGROUND 1
1.2 OBJECTIVES 1
1.3 WHY ACOUSTICS? 2
1.4 COMMUNICATION UNDERWATER 4
1.5 SPATIO-TEMPORAL OCEAN SENSING 5
1.6 RESEARCH CONTRIBUTIONS 6
1.7 ORGANISATION OF THE THESIS 7
1.8 CONCLUSION 8
CHAPTER 2 COMMUNICATION CHALLENGES IN UNDERWATER SWARM SENSOR
NETWORK (USSN)
2.1 INTRODUCTION 9
2.2 CHALLENGES IN UNDERWATER WIRELESS SENSOR NETWORKS (UWSN) 10
2.3 TAXONOMY OF MOBILE UNDERWATER WIRELESS SENSOR NETWORK 13
2.3.1 Definition Of Swarming And Robotic Swarm Networks 16
2.4 COMMUNICATION WITHIN UNDERWATER SWARM SENSOR NETWORK 17

9. ix
2.4.1 Biologically Inspired Underwater Communication 17
2.4.2 Explicit Short-Range Acoustic Communication 19
2.5 UNDERWATER SWARM SENSOR NETWORK (USSN) APPLICATIONS 20
2.5.1 Time Critical Mission Deployment 21
2.5.2 Non-Time Critical Mission Deployment 22
2.6 USSN COMMUNICATION TRAFFIC REQUIREMENTS 23
2.6.1 Payload Data 24
2.6.2 Localisation 24
2.6.2.1 Underwater Vehicle (AUV) Navigation 26
2.6.3 Mission Time Critical: Bio-Inspired Formation Control Algorithms 27
2.6.4 Mission Non-Time Critical: Patterned Formation Control Algorithms 29
2.7 USSN COMMUNICATION CHALLENGES 29
2.7.1 Swarm Network Characteristics 30
2.7.2 Summary Of USSN Data Traffic Requirements 30
2.7.3 Research Goal And Specific Research Questions 31
2.8 CONCLUSION 31
CHAPTER 3 SHORT-RANGE UNDERWATER ACOUSTIC COMMUNICATION CHANNEL
CHARACTERISTICS 33
3.1 INTRODUCTION 33
3.2 ACOUSTIC CHANNEL 33
3.3 UNDERWATER ACOUSTIC CHANNEL CHARACTERISTICS 35
3.3.1 Acoustic Signal Level 35
3.3.2 Transmitter (Projector) Signal Intensity 36
3.3.3 Signal Attenuation 36
3.3.3.1 Spreading Loss 36
3.3.3.2 Absorption Loss 37
3.3.3.3 Propagation Loss 39
3.3.3.4 Speed of Sound 40
3.3.4 Underwater Multipath Characteristics 41
3.3.4.1 Swarm Reverberation 42
3.3.4.2 Reverberation 44
3.3.4.3 Reverberation and Transmitter Power Levels 45
3.3.4.4 Delay Spread and Coherence Times 45

10. x
3.3.5 The Doppler Effect 46
3.4 NOISE 46
3.4.1 Ambient Noise 47
3.4.2 Self Noise 48
3.4.3 Intermittent Sources of Noise 49
3.5 SHORT-RANGE ACOUSTIC COMMUNICATION PHYSICAL LAYER
PARAMETERS 50
3.5.1 Signal-to-Noise Ratio 50
3.5.2 Frequency Dependent Component of SNR 50
3.5.3 Channel Bandwidth 53
3.5.4 Theoretical Channel Capacity 54
3.5.5 Receiver (Hydrophone) Signal Intensity 55
3.5.6 Signal-to-Noise+Interference- Ratio (SNIR) 56
3.5.7 Modulation and Bit Error Rate (BER) 57
3.5.7.1 Currently Available Acoustic Modem Capacities 59
3.5.8 Long-Range Vs Short Range 59
3.6 CONCLUSION 61
CHAPTER 4 MEDIUM ACCESS CHALLENGES FOR UNDERWATER SWARM SENSOR
NETWORKS 63
4.1 INTRODUCTION 63
4.2 MAC PROTOCOL OVERVIEW 64
4.2.1 Random Access 65
4.2.2 Scheduled Protocols 70
4.3 TIME SCHEDULED MEDIUM ACCESS AND TOKEN POLLING APPROACHES71
4.3.1 TDMA Based Protocols For Swarming AUVs 71
4.3.2 Token Polling Protocols For Swarming AUVs 73
4.4 CHALLENGES AND OPPORTUNITIES USING TDMA AND POLLING
ALGORITHMS 74
4.4.1 Time Synchronisation 74
4.4.2 Guard Time 75

11. xi
4.4.3 Scalability 75
4.4.4 Time-Slot Scheduling 76
4.4.5 Spatial-Temporal Diversity 77
4.4.6 Application Of Spatial-Temporal Diversity 80
4.4.7 Summary 81
4.4 CONCLUSION 81
CHAPTER 5 INTRODUCTION AND ANALYSIS OF TWO NEW MAC PROTOCOLS FOR
UNDERWATER SWARM SENSOR NETWORK (USSN) APPLICATIONS 83
5.1 INTRODUCTION 83
5.2 APPLICATION DEVELOPMENT STRATEGIES 84
5.2.1 Non-Time Critical Mission Deployment 85
5.2.1.1 Non-Time Critical Mission Data Traffic 86
5.2.2 Time Critical Mission Deployment 87
5.2.2.1 Time Critical Mission Data Traffic 87
5.3 ADAPTIVE TOKEN POLLING (ATP-MAC) PROTOCOL DESCRIPTION 87
5.3.1 ATP-MAC Packet Structures 89
5.3.2 ATP-MAC Cycle Description 90
5.3.3 Cycle Time (Tcycle) Analysis 92
5.4 ADAPTIVE SPACE TIME – TDMA (AST-TDMA) PROTOCOL DESCRIPTION 93
5.4.1 AST-TDMA Packet Structure 94
5.4.2 AST-TDMA Cycle Description 95
5.4.3 Cycle Time (Tcycle) Analysis 96
5.5 USING SPATIAL-TEMPORAL DIVERSITY 96
5.6 CONVENTIONAL TDMA PROTOCOL 99
5.7 PERFORMANCE CRITERIA 99
5.7.1 Network Delay 100
5.7.2 Channel Resource Utilisation and Throughput 100
5.7.3 Swarm Synchronisation 100
5.7.4 Performance Boundaries 102
5.7.4.1 NCCPsoft Bounds – Due To Failure 102
5.7.4.2 NCCPhard Bounds – Due To Vehicle Collision 103

12. xii
5.7.4.3 NCCPhard Bound For Bus Topology 103
5.7.4.4 NCCPhard Bound For Cluster Topology 105
5.7.4.5 NCCP Limits 105
5.8 QUEUING MODEL ANALYSIS 106
5.8.1 Model Parameters 108
5.9 IMPACT OF NETWORK DELAY ON SWARM SIZE 108
5.9.1 Expected Data Packets Per Cycle 108
5.9.2 Cycle Time And Network Saturation 112
5.9.3 Neighbourhood Communication Cycle Period (NCCP) 113
5.9.4 Minimum Packet Arrival Rates 115
5.9.5 Determination Of Maximum Swarm Size 117
5.9.5.1 Variations Due To Packet Length 120
5.9.5.2 Variations Due To Range Between Sequence Vehicles 123
5.9 CONCLUSION 126
CHAPTER 6 AST-TDMA PROTOCOL SIMULATION ANALYSIS AND EVALUATION IN
NON-IDEAL UNDERWATER ENVIRONMENTS 127
6.1 INTRODUCTION 127
6.2 SIMULATION MODEL AND METHODOLOGY 127
6.2.1 Modelling of an Acoustic Underwater Channel & Physical Layer in OpNet 128
6.2.2 OpNet Model 130
6.2.3 OpNet Parameters 130
6.3 VALIDATION OF SIMULATION MODEL 131
6.3.1 Protocol Process Evaluation 132
6.3.2 Validation of Simulation Model Results 135
6.4 PROTOCOL MODIFICATIONS 137
6.4.1 Protocol Procedures 137
6.4.2 Additional Protocol Performance Metrics 139
6.4.2.1 Throughput 139
6.4.2.2 Channel Capacity Utilisation 140
6.5 ANALYSIS OF PROTOCOL VARIATIONS 141
6.5.1 Transmission ‘WAIT’ Modification 141

13. xiii
6.5.2 Packet Size Variations 145
6.5.2.1 Option of Data Packet Train 146
6.5.2.2 Option of Piggybacking of Data Packets 148
6.6 RESULTS IN NON-IDEAL UNDERWATER ENVIRONMENTS 148
6.6.1 The Non-Ideal Channel in OpNet 148
6.6.2 Variations in Transmitter Power 150
6.6.3 Comparison with TDMA protocol and Channel Utilisation Benefits 153
6.6.4 Variations in Packet Length 154
6.6.5 Introduction of Swarm Reverberation 155
6.6.5.1 Noise and Reverberation Levels 156
6.6.5.2 Variations in Transmitter Power 157
6.7 CONCLUSION 158
CHAPTER 7 CONCLUSION 161
7.1 INTRODUCTION 161
7.2 RESEARCH CONTRIBUTIONS 162
7.2 FUTURE RESEARCH 163
REFERENCES 165
APPENDICES 175
APPENDIX A – FISHER & SIMMONS COEFFICIENTS 175
APPENDIX B – MATLAB CODE 176
APPENDIX C – BPSK MODULATION CURVE 178
APPENDIX D – ENERGY CONSUMPTION IN AN AUV 179
APPENDIX E – PROCESS MODEL OPNET CODE 180

14. xiv
List of Tables
Chapter 1
Table 1.1: Attenuation Comparison 3
Chapter 2
Table 2.1: Single Cluster Underwater Swarm Sensor Network (USSN) Applications 21
Table 2.2: Example of Payload Sensor Types For Mission Time Critical and Non-time Critical
Applications 25
Table 2.3: Single Cluster Underwater Swarm Sensor Network (USSN) Traffic Characteristics 30
Chapter 3
Table 3.1: Packet Timing Diagram with Swarm Scattering Reflections for a 4-vehicle Swarm
at 30m for different packet sizes 43
Table 3.2: Comparison of Terrestrial and Long and Short range Acoustic Bandwidths 60
Chapter 5
Table 5.1: Application Specific Deployment and Communication Requirement Overview 85
Table 5.2: ATP-MAC and AST-TDMA Packet Structures (bytes) 90
Table 5.3: NCCPhard for vehicle collisions based on Disturbances in Bus 102
Table 5.4: Summary of NCCPhard and NCCPsoft bound for Bus Topology 104
Table 5.5: Hard and Soft Time Boundaries of NCCP for Cluster Topology 106
Table 5.6: Summary of NCCPlimit (s) 106
Table 5.7: Base Parameters used in Initial Analysis 109
Table 5.8: Maximum Number of Vehicles that can be supported in Small Disturbance Model at 50m
119
Table 5.9: Packet Size Determination 121
Chapter 6
Table 6.1: Modified Pipeline Stages 128
Table 6.2: Main Parameters and Transmission Characteristics used in OpNet 132

15. xv
List of Figures
Chapter 1
Figure 1.1: AUV Swarm, Stylised SeaVision™ Vehicles 3
Chapter 2
Figure 2.1: Taxonomy for Mobile Underwater Wireless Sensor Networks 14
Figure 2.2: Various AUV’s 18
Figure 2.3: Fully Distributed Architecture for a Time Critical Mission using Underwater Swarm
Sensor Network 22
Figure 2.4: Decentralised Hierarchical Architecture for a Non-time Critical Mission using an
Underwater Swarm Sensor Network 23
Chapter 3
Figure 3.1: Underwater Acoustic Environment 34
Figure 3.2: Block Diagram of a Projector and Hydrophone 35
Figure 3.3: Absorption Coefficient vs Frequency 38
Figure 3.4: Path Loss vs Range 39
Figure 3.5: Typical Sound Speed Profile in the Ocean 41
Figure 3.6: Data Transmission and Swarm Reverberation from a 4 vehicle USSN 43
Figure 3.7: Power Spectral density of the Ambient Noise; W (wind), S (shipping) 47
Figure 3.8: Frequency Dependent Component of Narrowband SNR 51
Figure 3.9: Optimum Signal Frequency based on Optimising SNR (determined from frequency-
dependent component of narrowband SNR) 52
Figure 3.10: Range dependent 3dB Channel Bandwidth shown as dashed lines. The Y-axis is the
Optimum SNR based on the frequency dependent component of the narrowband SNR
54
Figure 3.11: Theoretical Limit of Channel Capacity (kbps) verse Range 55
Figure 3.12: Receiver Signal Intensity vs Range for Variation in Transmitter Power and Transducer
Efficiency 56
Figure 3.13: SNIR vs Range for variation in Transmitter Power, Transducer efficiency, and
Reverberation Level 57
Figure 3.14: BER vs Range for Short Range Acoustic Data Transmission Underwater 58
Chapter 4
Figure 4.1: Hidden and Expose Node Problem 66

16. xvi
Figure 4.2: Minimum CSMA cycle with handshaking 67
Figure 4.3: Spatial-Temporal Diversity 78
Figure 4.4: One Data Exchange Cycle between 2 Nodes for Different β 79
Chapter 5
Figure 5.1: Bus Topology for a Non-time Critical Mission using an Underwater Swarm Sensor
Network 85
Figure 5.2: Cluster Topology for a Time Critical Mission using Underwater Swarm Sensor Network
86
Figure 5.3: ATP-MAC and AST-TDMA Protocol Operation showing one full cycle of transmission 91
Figure 5.4: Spatial-Temporal Diversity Explained. A Simple Four Vehicle Topology 97
Figure 5.5: AST-TDMA: One cycle of slot times based on configuration of Figure 5.4 97
Figure 5.6: Determining validity of non-exclusive access 98
Figure 5.7: Potential Disturbance in Bus Topology 101
Figure 5.8: Potential Disturbance in Cluster Topology 104
Figure 5.9: Average Expected Number of Packets Serviced per Cycle for increasing Packet Arrival
Rate at 50 m. Comparison of the TDMA, ATP-MAC and AST-TDMA protocols and 5 or
15 Vehicle Swarm 110
Figure 5.10 Packets available in each vehicle per cycle at various Packet Arrival Rates in a 5-Vehicle
Swarm at 50 m 111
Figure 5.11: Comparison of Cycle Time, for the three protocols with a 5-Vehicle and 15-Vehicle
Swarm at 50m 112
Figure 5.12: AST-TDMA protocol: NCCP, Tcycle and Packet Discards for 5-Vehicle Swarm at 50 m
114
Figure 5.13: ATP-MAC protocol: NCCP, Tcycle and Packet Discards for 5-Vehicle Swarm at 50m
114
Figure 5.14: TDMA protocol: NCCP, Tcycle and Packet Discards for 5-Vehicle Swarm at 50 m 114
Figure 5.15: Comparison of Minimum Packet Arrival Rate for Increasing Swarm size at 50 m
115
Figure 5.16: AST-TDMA 5-Vehicle Swarm at 50 m showing Number of Cycles per NCCP and between
packet discarded 116
Figure 5.17: AST-TDMA 5-Vehicle Swarm at 50 m showing Number of Packets queued and
discarded 116
Figure 5.18: Determining limit to the Number of Swarm Vehicles using Bus Topology at 50m
118

17. xvii
Figure 5.19: Determining limit to the Number of Swarm Vehicles using Cluster Topology at 50m
118
Figure 5.20: AST-TDMA NCCP, NCCPlimit and various Packet Arrival Rates 120
Figure 5.21: Maximum Number of Swarm Vehicles in Bus Topology with Changes in Packet Size at
50 m 122
Figure 5.22: Maximum Number of Swarm Vehicles in Cluster Topology with Changes in Packet Size
at 50 m 122
Figure 5.23: Maximum Number of Swarm Vehicles in Bus Topology for increasing range 125
Figure 5.24: Maximum Number of Swarm Vehicles in Cluster Topology for increasing range 125
Chapter 6
Figure 6.1 Radio Transceiver Pipeline Execution for One Transmission 129
Figure 6.2a: 5-Vehicle Cluster Topology. Each vehicle represented 130
Figure 6.2b: OpNet Model 130
Figure 6.3: OpNet AST-TDMA Process Model 131
Figure 6.4: AST-TDMA Protocol, 5-Vehicle Swarm of Figure 6.1 (a), illustrating Packet Tx & Rx in
each vehicle 133
Figure 6.5: Comparison of Timing between AST-TDMA & TDMA, for V5 of 5 133
Figure 6.6: 5 and 15-Vehicle Cluster Topology Swarm, @ 50 m, initial positions 134
Figure 6.7: Comparison of Cycle Time (Tcycle) obtained in OpNet and MATLAB for both the AST-
TDMA and TDMA protocols (compare with Figure 5.11) 135
Figure 6.8: AST-TDMA protocol showing relationship between Tcycle, NCCP and Packet discard.
Comparison of OpNet and MATLAB results (compare with Figure 5.12) 136
Figure 6.9: TDMA protocol showing relationship between Tcycle, NCCP and Packet discard.
Comparison of OpNet and MATLAB results (compare with Figure 5.14) 136
Figure 6.10: AST-TDMA considerations in protocol operations 138
Figure 6.11: Comparison of Protocols and Number of Vehicles in Swarm at 50 m 140
Figure 6.12: Process Model for Wait Modification 143
Figure 6.13: Comparison of Average Number of Cycles per NCCP 144
Figure 6.14 Comparison of NCCP times between protocols and Tcycle for the AST-TDMA with
Token 144
Figure 6.15 Comparison of the true Channel Utilisation Ui
true 144
Figure 6.16 Packet Discards per cycle: Comparison between protocols for a 15 and 5 vehicle
swarm at 50 m 146
Figure 6.17 Comparison of NCCP for 15 vehicle swarm at 50 m with various Data Packet Sizes
defined in Table 5.9 147

18. xviii
Figure 6.18 Channel Utilisation at λsat: Comparing AST-TDMA with Wait and TDMA protocol 148
Figure 6.19 SINR for 5-Vehicle Swarm at 20 m 149
Figure 6.20 Average Packet loss for 5-Vehicle Swarm against SINR and Reverberation Levels at 50
m 151
Figure 6.21 Average NCCP for 5-Vehicle Swarm at 50 m showing the increasing NCCP as Packet
Arrival rate falls below cycle saturation and variations in Reverberation Levels (Sea
States) 152
Figure 6.22 Average NCCP at λsat for changes in SNIR due to Reverberation Levels: Comparison
between AST-TDMA and TDMA protocols for 15-V swarm 153
Figure 6.23 Channel Capacity Utilisation at λsat for changes in SNIR due to Reverberation Levels:
Comparison between AST-TDMA and TDMA for a 15-V swarm 154
Figure 6.24 NCCP for variations in Data Packet Size for 5-V Swarm at 50 m 155
Figure 6.25: Noise and Reverberation Levels 156
Figure 6.26: Noise and Reverberation Levels for Variation in Transmitter Power 157
Figure 6.27: Packet Loss with variations in Packet Size and Transmitter Power 158
Figure 6.28: Packet Loss with variations in Transmitter Power and Sea State 158

19. xix
Abbreviations and Symbols
Abbreviations
AUV Autonomous Underwater Vehicle
ATP-MAC Adaptive Token Polling - MAC
AST-TDMA Adaptive Space Time - TDMA
LIDCA Lowest Identifier Clustering Algorithm
NCCP Time of one completed cycle of exchange of data or Neighbourhood Communication
Cycle Period (everyone-to-everyone)
SNIR Signal to Noise & Interference Ratio
SNR Signal to Noise Ratio
TDMA Time Division Multiple Access
QoS Quality of Service
USSN Underwater Swarm Sensor Network
Commonly Used Symbols
α absorption coefficient
B Bandwidth (Hz)
Bc Channel Bandwidth
c Speed of Sound (1500 m/s)
Cc Channel Capacity
C Number of cycles per NCCP
d Depth (m)
Di Number of NCCP cycles in Vi in Tsim
Δfd Doppler shift
Dpkt
������ Expected data packet delay
d Distance of Travel of a vehicle
DItx Directivity Index
Fi Throughput of swarm Formation data in ith Vehicle
fmax Maximum frequency
fmin Minimum frequency
f Carrier frequency (fo is Optimum Signal Frequency)
FL Interference Level
Gi Throughput of i
th
Vehicle
λ Data arrival rate in a vehicle
Lpoll Length of a poll packet
Ldata Length of a data packet
Ltoken Length of a token packet
I Source intensity
Iref Reference Intensity
Iomni Intensity of spherical spreading
Idir Intensity along the axis of the beam pattern

20. xx
M Maneuverability range (m)
ηtx ηrx Projector and Hydrophone efficiency
Nq Expected number of packets in a vehicles queue
NCCPsoft Complete packet exchange limit based on maneuverability of the vehicles
NCCPhard Complete packet exchange limit based on collision avoidance of vehicles
NCCPlimit Complete packet exchange limit taking into account NCCPsoft and NCCPhard
Nd Average Number of Packets per Cycle
Nwind Noise spectrum density from wind
Nship Noise spectrum density from shipping
Nturb Noise spectrum density from turbulence
Nthermal Noise spectrum density from underwater thermal noise
Nemf Noise spectrum density from electronic thermal noise
Noise Total noise
P Pressure
Pi Number of Packets successfully received by Vehicle i
Ptx total acoustic power consumed by the Projector
Paref Reference pressure level , 1 μPa
ρ density of the medium (averages for sea water are: ρ = 1025 kg/m3)
PLspreading Propagation Loss from Spreading loss
PLabsorption Propagation Loss from absorption
PLloss Combined Propagation Loss
R Packet transmission rate (bps)
r Range between vehicles (m)
r12 Range between Vehicle with V1 and Vehicle with V2
RL Reverberation Level
s Shipping activity factor
S Speed of Vehicles
S^ Speed of Vehicle with external force added
ΔS Relative velocity between moving vehicles
SPLprojector Projector source pressure level
Sal Salinity
t Temperature
T Absolute temperature
Tcycle Time of one cycle or Vehicle Sequence Time (once through each sequenced vehicle,
whether they sent data or not)
tqueue Average queue waiting time
tLS Propagation delay between lead vehicle and swarm vehicle
tSS Propagation delay between two swarm vehicles
tij Propagation delay from Vehicle i to Vehicle j
tprop������� Average propagation delay of packets received in a vehicle in one cycle
Tslot Slot Size (s) for TDMA protocol
Tcomm Transmission Time of a poll packet
Tdata Transmission Time of a data packet
Ttoken Transmission Time of a token packet
Td-t Transmission Time of the data portion of a data packet
TX Generalised Transmission Time
Tcycle Time to complete a cycle through sequence of vehicles
tprocess Processing time required for a packet in the transceiver
tcreate Processing time required to create Command packet at start of cycle
tcoll Time to vehicle collision
θ Angle of disturbance of a vehicle from its planned trajectory

21. xxi
Tsim Time over which simulation is conducted
Titx Transmission time of a packet from i
th
Vehicle
Tirx Reception time of a packet in the i
th
Vehicle
Ui Channel Utilisation
V Number of Vehicles in a Swarm
Vi Vehicle with ID i
w Wind State m/s

22. 1
Chapter 1
Introduction
1.1 Background
Mobile swarms of autonomous underwater vehicles (AUVs) have exciting potential for extending
the current operational applications underwater and to add new opportunities to the working
environment of the oceans. Applications include areas such as mapping and surveying [122,
143], military tasks such as to replace workers for dangerous tasks in ocean war zones [31], 3D
plume identification and analysis [104] and other more general scientific and commercial studies
of dynamic oceanographic phenomena such as phytoplankton growth or fish migration [62,
65,114]. Current solutions have been built around static sensor networks and single ROVs
(remotely operated vehicles) and single AUVs. The benefits, however, of several vehicles
working together over any single vehicle include greater speed and range of operation,
increased system reliability and higher quality measurements [31, 122]. To achieve these multi-
vehicle system benefits, data communication between vehicles is essential.
A swarm of AUVs can be considered as being composed of typically many simple,
homogeneous and autonomous agents, deployed in a decentralized mobile topology with
communication on a local level for a combined purpose. Swarm behaviour infers a biologically
motivated behaviour that is exhibited by a set of similar kind of animals that are working
together as a collective, such as seen with insects, birds and fish. The communication protocol
for a swarm needs to facilitate ‘awareness’ of other vehicles in a neighbourhood and needs
each vehicle to be able to work autonomously. Swarm formation control algorithms require at a
minimum, to exchange location and trajectory information from all vehicles to all other vehicles
in a neighbourhood in a continuous fashion, so that a group of self propelled AUVs will be able
to operate in a swarm like fashion.
The growth of underwater operations has required data communication between various
heterogeneous underwater and surface based equipment, which are typically sparsely
deployed. Small Autonomous Underwater Vehicles (AUVs) are a more recent addition to the
equipment used in underwater operations. Most AUV development work, however, has
concentrated on the vehicles themselves and their operations as a single unit [41, 60] where
their communication is with other wired or wireless fixed infrastructure. There has been much
less attention given to the development of groups of autonomous vehicles being deployed in an
autonomous swarm.
1.2 Objectives
Swarm operations have many benefits: with the ability to scan or ’sense’ a wider area and to
work collaboratively provides the potential to vastly improve the efficiency and effectiveness of
mission operations. Collaboration within the swarm structure will facilitate improved operations

23. 2
by building on the ability to operate as a team that will result in emergent behaviours [17, 92]
that are not exhibited by individual vehicles. Implementation of swarms of vehicles will greatly
improve on the current ability of single vehicles to survey and explore the oceans.
Advances in the development of Autonomous Underwater Vehicles (AUV) (that include being
smaller, low cost and low power) and their potential to work in swarm like configurations,
necessitates the development of effective communication network architectures and protocols
for short-range wireless acoustic underwater communication. This communication is essential to
coordinate operation of the vehicles as well as to transmit data within the swarm to facilitate the
benefits of operating as a team.
It has been observed that the communication within a swarm network can fall into three
categories; Interaction via Environment, Interaction via Sensing and Interaction via
Communication [17]. The former two are implicit communication techniques that use an indirect
measure, from the sensors or data transmissions themselves, to gain information about what
neighbouring vehicles are doing. Interaction via Communication is an explicit communication
where data is exchanged between vehicles. The body of work in this thesis will be presenting an
explicit communication protocol for the purpose of allowing groups of AUVs to exchange each
other’s navigational data so that the group can implement swarming formations.
The main research objectives are to determine:
1. what the operating characteristics of an underwater swarm of AUVs is and how do these
characteristics impact on the design of an effective swarm communication protocol?
2. what the limits are to the number of vehicles that can operate in close proximity to each other
in an underwater swarm, given the ability to explicitly exchange inter-vehicle data through an
acoustic communication network? and
3. how can a Medium Access Control (MAC) communication protocol be designed to take into
account the constraints of a short-range underwater acoustic channel?
1.3 Why Acoustics?
Acoustics remain today the most widely used form of communication underwater due to its
ability to send messages over long distances. Sound energy travels more efficiently in water
than air but still relatively slowly at only 1500m/s ± 3% in seawater depending upon
temperature, salinity and pressure [133, 29].1
Optics work at very short range but require clear
water and electro-magnetic waves have high attenuation as shown in Table 1.1.
1
In deep sea channel, sometimes referred to as the SOFAR (Sound Fixing and Ranging)
channel, sound is trapped and travels almost horizontally with reduced path losses as has been
shown with the effective method that whales use this channel for long distance communication
[67].

24. 3
Figure 1.1: AUV Swarm, Stylised SeaVision™ Vehicles
In comparison to RF, the acoustic channel introduces a very high propagation delay, which is
0.67 ms/m (compared to RF of 3.33 ns/m in air). RF, underwater, even at low frequencies
suffers from extreme attenuation due to conductive seawater and high rates of absorption that
has predominately eliminated its use for underwater communications. The slow speed of
propagation of acoustic signals underwater however has a major effect on the performance of a
communication system. This high channel latency effectively means lower reliability due to the
quality of a single-hop link that can change significantly in the order of time required to send and
receive data and the delays in feedback to any changes in channel state information. In
addition, underwater communication channel characteristics change more dynamically than in
terrestrial channels due to its attenuation, noise and thermal profiles [27]. Thus, in terms of the
development of peer-to-peer communication underwater, the latency of acoustic signals
compared with RF in air requires essentially to redesign communication protocols [4].
The underwater environment can be a very noisy environment: including animal noises; wind,
rain and other natural phenomena such as ice cracking and earthquakes; and shipping and
other man made operations in and on the water. Each of these noise contributors operate in
different frequency bands that together build an ambient noise level that is frequency dependent
with noise levels decreasing with increasing frequencies.
Table 1.1: Attenuation Comparison [133]
Type Frequency (kHz) Attenuation dB/km
Sound 30 5
EM Wave 30 7500

25. 4
Taking advantage of the lower noise profile with increasing frequencies needs to be balanced
with the increasing path loss characteristics with increasing signal frequencies. This means
matching signal frequencies to application and environments is required to improve signal
detection. In addition, multipath can impact severely on data reception and is also affected by
application and environment in which the operations occur.
Multipath underwater can be extreme and this also differentiates wireless communication
underwater to that in air, especially in shallow water where boundary reflections on the sea floor
and surface produce a number of significant propagation paths at the receiver. These multiple
signals that have been reflected, scattered or bent will be themselves impacted by the latency of
the channel and delayed in time, more dramatically than in air. Due to the various path lengths
and timing that these additional signals can take, they may create significant Inter Symbol
Interference (ISI) and errors in symbol detection.
There has been little work done on the short-range acoustic channel model as there has not
been the operational demand for these systems. Recently the developments in underwater
acoustic sensor networking (UW-ASN) and the use of multi-hop networking architecture and
data muling operations have generated interest within the research community to develop
shorter range underwater communication systems [41]. As the knowledge of long-range
channel models are well established, the characterising of a short-range channel model will
initially extrapolate this understanding.
1.4 Communication Underwater
The underwater acoustic communication channel is recognised as one of the harshest
environments for data communication, with long-range calculations of optimal channel capacity
of less than 50kbps for SNR (Signal-to-Noise Ratio) of 20dB [124] with current modem
capacities of less than 10kbps [137]. Predictability of the channel is very difficult with the
conditions constantly changing due to seasons, weather, and the physical surroundings of sea
floor, depth, salinity and temperature.
The performance of an acoustic communication system underwater is characterised by various
losses that are both range and frequency dependent, background noise that is frequency
dependent and bandwidth and transmitter power that are both range dependent. In general, the
constraints imposed on the performance of a communication system when using an acoustic
channel are the high latency due to the slow speed of the acoustic signal propagation, and the
signal fading properties due to absorption and multipath interferences, particularly due to
reflections off the surface, sea floor and objects in the signal path. High link latency in a
communication network influences the error control techniques, protocol designs and network
throughput. A specific constraint on the performance due to the mobility of AUV swarms is the
Doppler effect resulting from any relative motion between a transmitter and a receiver, including
any natural motion present in the oceans from waves, currents and tides. Because the speed of
sound in water and the speed of AUVs is relatively similar the Doppler effect is very significant
for underwater communication compared with terrestrial systems that use RF.

26. 5
Short-range underwater communication systems have two key advantages over long-range
operations; a lower end-to-end delay and a lower signal attenuation due to range. End-to-end
propagation at 500 m for example is approximately 0.3 sec which is considerable lower than the
2 sec at 3 km but still critical as a design parameter for shorter range underwater MAC
protocols. The lower signal attenuation means that lower power transmitter are required, which
will result in reduced energy consumption, which is critical for AUVs that rely on battery power.
Battery recharge or replacement during a mission is difficult and costly. The dynamics
associated with attenuation also changes at short range where the spreading component
dominates over the absorption component, which means less dependency on temperature,
salinity and depth (pressure). This also signifies less emphasis on frequency as the frequency
dependent part of attenuation is in the absorption component and thus will allow the use of
higher signal frequencies and higher bandwidths at short ranges. This potential needs to be
exploited to significantly improve the performance of an underwater swarm network
communication system.
A significant challenge for data transmission underwater is multipath fading. The effect of
multipath fading depends on channel geometry and the presence of various objects in the
propagation channel. Multipath occur due to reflections (predominately in shallow water),
refractions and acoustic ducting (deep water channels), which create a number of additional
propagation paths, and depending on their relative strengths and delay values can impact on
the error rates at the receiver. The bit error is generated as a result of inter-symbol interference
(ISI) caused by these multipath signals. For very short-range single transmitter-receiver
systems, there could be some minimisation of multipath signals [55, 136]. For swarm
operations, however, there is potentially a different mix of multipath signals that need to be
considered, in particular, those generated due to the other vehicles in the swarm.
Careful consideration of the physical layer parameters and their appropriate design will help
maximise the advantages of a short range communications system that needs to utilise the
limited resources available in an underwater acoustic networking environment. For the medium
access layer design the unique spatial-temporal characteristics underwater due to the very slow
propagation of sound and low bandwidths available creates a very different set of constraints,
compare to RF, that also need to be incorporated in any protocol design. This is why it is not
straight forwarding in adapting RF solutions to the underwater case.
1.5 Spatio-temporal ocean sensing
The shorter ranges expected between vehicles in a swarm topology, means that propagation
delays will be smaller than for the more typical longer-range underwater applications, however,
still significant compared to RF, where propagation delay is considered negligible. In fact, the
transmission time of packets are in the same order of magnitude as the propagation delay,
which creates a unique spatial-temporal environment for underwater communication, and is far
different from what is experienced in a terrestrial RF setting. Exclusive channel access based
on transmission time of data becomes ineffective way to avoid collisions, unless large guard

27. 6
times are incorporated to take into account propagation delays between all possible vehicles in
the network. Therefore, non-exclusive access can occur due to the space diversity, which
allows more than one transmission-reception activity in the channel at the same time.
1.6 Research Contributions
The key problem being addressed in this thesis is the medium access control (MAC) protocol for
real time communication in a fully connected but distributed group of underwater autonomous
vehicles (AUV) operating as a underwater swarm sensor network (USSN). USSN will be a game
changer for underwater operations, as it will provide a low cost autonomous search and survey
method for the virtually unexplored vast oceans. The research field of USSN is still in its infancy;
in terms of the vehicles’ design and development; the classification of application areas; and
their traffic requirements as well as the communication protocols needed for swarm operations.
The key contributions of this thesis thus include;
• The development of a short-range underwater acoustic communication channel model
in which the design, development and performance analyse of underwater
communication protocols can be advanced. Specifically the development of a SNIR
(Signal to Noise + Interference Ratio) where the interference due to reverberation levels
caused by the impact of long data packets being sent via omni-directional antennas and
their reflections off the many vehicles operating at close range.
The short-range acoustic channel characteristics are compared to the more traditionally
used longer-range channels and the use of RF in terrestrial environments. This new
short-range acoustic model was implemented in OpNet Modeler®, a sophisticated
communication networking simulation environment, used for the evaluation of the
proposed new MAC layer protocols. This required the modifications to the Radio
Pipeline Stages so that the non-ideal short-range acoustic channel could be executed.
• A proposal for a new type of reverberation; Swarm Reverberation will be shown to play
an important role in the reverberation levels for an USSN. With the application of an
underwater swarming network, which has many vehicles in a dense topology, there will
be an impact on the reverberation channel geometry due to the vehicles themselves
being ‘sound reflective’ objects. This channel geometry together with packet size
creates a unique relationship between range (propagation time) and packet length
(transmission time) which will be shown to impact on the level of swarm reverberation.
• The development of a new Taxonomy for Mobile Underwater Wireless Sensor Networks
based on the network coverage area and density of vehicles required. Underwater
Swarm Sensor Networks (USSN) is thus classified based on their potential deployment
arrangements. USSN are defined as a fully connected and decentralised topology of
numerous vehicles working together collectively with continuous point-to-mulitpoint links
and can operate as a single cluster or multi-cluster. This work contributes to the

28. 7
development of a single cluster swarm either operating in a Bus, requiring a Pattern
Formation Algorithm or a Cluster using Bio-inspired swarm formation algorithms.
These deployment options lead to the development of traffic models and the Quality of
Service (QoS) requirements for a USSN.
• The design and development of two new MAC layer communication protocols that
utilise the unique spatial-temporal environment and the challenging acoustic channel
characteristics underwater for the Bus and Cluster topology deployments.
For the Bus Topology, an Adaptive Token Polling protocol, ATP-MAC, uses a polling
approach in a decentralised hierarchical topology. A revised design of the Token Polling
protocol was develop for a decentralised distributed MAC protocol, Adaptive Space
Time – Time Division Multiple Access (AST-TDMA). Both protocols are designed to
effectively use a single channel broadcast acoustic environment while incorporating a
method to handle the spatial-temporal characteristics experienced underwater. They
are both designed to work independently of time synchronization and require no prior
knowledge of propagation delays and future knowledge of the swarm network topology.
An analytical framework using a queuing model to evaluate the performance of the two
adaptive protocols was completed. This found that under ideal underwater channel
conditions and fixed data rates there is a trade-off between range, data packet size,
number of nodes in a cluster and the arrival rates of data in each vehicle queue to
maintain an average packet transfer delay.
• Two new performance metric are developed for analyses of the protocol: NCCP,
Neighbourhood Communication Cycle Period, that establishes the delay in the
distribution of one cycle of navigational data throughout the swarm; and Channel
Capacity Utilisation that investigates the extent to which the channel is utilised which
needs to be maximised for underwater use, as it cannot be used for anything else.
1.7 Organisation of the Thesis
This thesis presents the work on the background and development of underwater
communication protocols for underwater swarm sensor networks. Chapter 2 presents a new
taxonomy proposed to classify Underwater Swarm Sensor Networks and the design challenges
and objectives for an explicit communication network between the vehicles are discussed.
Chapter 3 investigates the short-range underwater channel characteristics and explores the
benefits and limitations that this environment imposes on the development of a communication
protocol. A short-range acoustic channel model is developed for the design, simulation and
analysis of the new protocols. In Chapter 4, the state of the art of medium access control (MAC)
layer protocols for Underwater Wireless Sensor Networks is presented with the arguments for
the approach taken in the new protocol designs. Chapter 5 presents the two new protocols and
the analytical analysis of them under ideal conditions while Chapter 6 presents the simulations
in a non-ideal channel.

29. 8
1.8 Conclusion
The aim of this work is to develop a communication protocol for a swarm of AUV’s. This
communication protocol requires that each vehicle’s location and navigational information is
exchanged with all other vehicles in its cluster, so that it can operate in a swarm-like behaviour.
The purpose is for future systems to be able to build on the benefits of cooperation between
vehicles and to perform collaborative missions. For this to be achieved there may be additional
information or sensor data required to be exchanged.
Creating underwater swarm sensor networks poses many new challenges for researchers, due
to the unique communication environment that exists, which has meant that many of the
techniques used in RF wireless communication do not apply. A good understanding of the
propagation channel is essential for both the design of and performance evaluation of an
underwater communication network. Due to the high propagation delay of an underwater
channel, any change of link quality such as SNIR will significantly affect the performance of the
network. MAC protocol designs require transmission channel state information in order to
optimise their performance. Hence, it is necessary to develop a new class of protocols which
can adapt themselves with the varying channel conditions and offer reasonable high throughput
in swarm networks.

30. 9
Chapter 2
Communication Challenges in
Underwater Swarm Sensor Network (USSN)
2.1 Introduction
The focus of this chapter is to introduce the research and develop associated with
communication requirements in a swarm of underwater vehicles. In particular to:
(1) Review the body of knowledge and current projects and practices associated with the
networking and communication requirements of Underwater Swarm Sensor Networks
(USSN);
(2) Explore the potential application areas to establish the architecture and traffic requirements
of a swarm network; and
(3) Define the design criteria for the explicit communication requirements of an underwater
swarm network.
Research into developing network solutions for terrestrial wireless ad-hoc and sensor networks
(WSN) has been active for many decades with more recent focus also including advancing
aerial swarming sensor networks. Only in recent years have the advances in underwater
technology enabled exciting new opportunities for UWSNs to be implemented to monitor larger
areas of the vastly unexplored oceans.
The growth of wireless systems terrestrially has been pushed by the sudden growth in wireless
communication technologies, which has enabled enormous expansion of new application areas
such as military, habitat and environment monitoring and observation as well as aerial swarm
networking. Similarly, there are significant advances in research and operational development
of underwater wireless network structures that focus on fixed infrastructure. It is envisioned
however that mobile groups of UWSNs, or USSN, will become very important because of their
relative ease of deployment, absence of cables, and their ability to adaptively sense a large
area. Despite this, autonomous swarms of mobile vehicles and even the integration of mobile
vehicles into fixed sensor networks are still a unique and growing area of research interest [13].
The field of Underwater Swarm Sensor Networking (USSN) requires the combining of the two
fields of formation (swarming) algorithms and underwater mobile WSN technology. The area of
swarming algorithms continues to advance and can be directly adapted from terrestrial to
underwater environments. It is however the developments of underwater mobile WSN
technologies that still face many challenges which are substantially different to a terrestrial
setting.
Swarming, infers a biological process, and thus the swarming algorithms are predominately
being developed from bio-inspired swarming formation control processes and systems.

31. 10
Adaptation of these algorithms to underwater applications will be considered here with
emphasis on their impact on the communication protocol requirements. An examination of the
factors that influence the design of the network topology and data communication requirements
for an underwater swarm network will thus be reviewed.
The WSN developments for underwater mobile applications, still face many challenges primarily
due to the resource constraints imposed by the underwater environment that are far more
limiting than in more traditional wired and terrestrial wireless environments. With the progress
and growth now occurring in underwater technologies, and recently with Autonomous
Underwater Vehicles (AUVs), the underwater world of mobile sensor networking is set to
expand [56]. This requires wireless communication between vehicles.
The age of USSN is thus beginning and with it the need for short-range underwater
communication and swarm communication networking protocols. Traditionally communication
underwater is by an acoustic medium, rather than electromagnetic, and this brings with it the
requirement for new approaches in networking and communication design [4, 32].
This chapter will begin with an overview of the major difficulties that underwater wireless sensor
networks face including a discussion related to the differences between terrestrial and
underwater operations. A taxonomy for Mobile UWSN was developed to put a context to the
discussion that will follow which will focus on the communication requirements of an Underwater
Swarm Sensor Networks (USSNs) that forms one part of the field of Mobile UWSN. This
taxonomy is used to discuss the communication challenges that these networks need to
overcome. A literature review of the few projects that have examined this specific area will be
discussed, however the literature related to the broader area of acoustic communication in an
underwater wireless sensor network (UWSN) will also be reviewed to present a broader
background to some of the potential interesting developments that need to be considered. A
discussion on swarm algorithms provides a view on the traffic requirements and network
structures that may results. A summary of the communication challenges that will be
investigated in this work will then be presented.
2.2 Challenges in Underwater Wireless Sensor Networks (UWSN)
Operating communication networks underwater is substantially different to terrestrial and space
operations and thus we begin this work with a list of the major challenges and principal
differences between terrestrial and UWSN. This list is divided into primary and secondary
issues. Primary issues focus specifically on the design and development needs of an acoustic
communication systems in an underwater wireless network and these will be investigated
further as we review and develop the requirements of an underwater swarm network. The
secondary issues relate to the broader network and technology matters that may indirectly
impact on the design of underwater networks and the limitations that these may impose on the
communications.

32. 11
Primary Issues
• Bandwidth: The underwater acoustic channel is considered one of the most difficult
operating mediums for data communication. Both noise and propagation losses are
frequency dependent and limit the operating frequency and bandwidths to low kHz
[134]. The severely limited bandwidths available underwater have a major impact on
network structures and protocols due to the trade-off between network node densities
and information exchange requirements. Chapter 3 will explore the channel issues in
more detail.
The lower operating frequencies create fundamental physical bandwidth limits. Not only
are the absolute bandwidths low but also they are not negligible with respect to centre
frequency, with bandwidths (B) in the low kHz and centre frequencies (fc) in the low 10’s
kHz (i.e. B is in the order of fc). Thus the generalised narrowband assumptions made in
RF communication of B << fc do not hold underwater [126, 9], and therefore the
assumption that the behaviour across the bandwidth will be the same. This is critical for
signal processing and synchronisation but also implies the need to respect the band-
limited nature of these systems at the MAC layer to develop bandwidth efficient
modulation and protocol solutions.
Underwater acoustic communication is nearly always half-duplex due to the very small
frequency bands available and also for space constrained AUVs, transmitters and
receivers cannot be spatially separated far enough to provide full-duplex connections
[101].
• Latency: Sound underwater travels at approximately 1500 m/s, which is very much
slower than the speed of light (electromagnetic radiation) at 3 x 108
m/s. This means
large propagation delays and can lead to relatively large motion-induced Doppler effects
which can mean even at very short distances high multipath spreads of 10 to 100 ms
can occur [126].
• Power: In underwater acoustic networks the transmit power is typically several
magnitudes higher than the received power [101]. For longer ranges this can be in the
order of up to 100 times while at very short ranges goes down to less than 10 times
[137]. This is very different to most terrestrial applications where the transmitting and
receiving powers are approximately the same.
• Deployment: UWSNs are generally more sparsely deployed and employ considerably
fewer nodes compared to terrestrial WSNs due to the cost of underwater hardware (see
Component Costs below) as well as operational deployment costs [56, 101].
• Duration: Underwater wireless sensor networks are generally deployed over shorter
periods - from several hours to weeks. This is considerably different to terrestrial sensor

33. 12
networks where, depending on the application, it is more common that their deployment
is for several months to years.
• Communication Addressing: Trends towards data centric communication networking is
occurring in terrestrial sensor networks due to the large-scale and dense deployment of
larger systems. As underwater sensor networks use much smaller numbers of units and
are more sparsely deployed, address-centric methods are more practical.
Secondary Issues
• Energy Consumption: the energy consumption requirements for underwater acoustic
sensors are much higher than those required in RF sensors due to the more complex
signal processing capabilities required in the receiver to compensate for the harsh
underwater channel conditions and the higher transmitter power requirements for the
acoustic rather than RF physical layer discussed above [4].
As battery capacity can be limited due to size restrictions on small AUVs and recharging
is virtually impossible underwater, battery power is a limited resource.
• Component Costs: While the cost of terrestrial sensors continues to decrease at a rapid
rate, underwater sensor costs remain high. This is because of the lack of economies of
scale as well as the significantly higher manufacturing costs due to materials and
techniques required to combat the harsh operating conditions experienced in water.
This is expected to change, albeit slowly, as underwater operational work becomes
more commonplace. Both terrestrial and underwater devices have benefited from the
miniaturisation of sensor technologies with smaller chip sizes driving down power
consumption and therefore improving their energy efficiency.
• Economics: There are several significant economic differences between terrestrial and
underwater networks, particularly those that relate to operational aspects of deployment
and recovery as well as to component costs, as discussed above. Launch and recovery
costs for underwater sensor networks are typically much higher due to the need to use
either oceanographic research vessels or commercially operated ships that are often
needed for several days at a time. Much of the time and therefore cost is due to the
difficulty of recovery, which is still considered essential because the devices are too
expensive to be considered disposable. Concerns are being raised with abandoning
items at the end of projects as rubbish, as this means a build-up of litter that is already a
problem in our oceans.
In addition, because the bandwidths are poor, full data recovery is often planned on
retrieval. GPS technology is being considered more for both node recovery and data
downloads but it has its limitations. As radio waves suffer from high attenuation
underwater, GPS cannot function underwater and is only advantageous when vehicles
surface. This functionality would make recovery quicker and easier but it adds costs to
incorporate it on vehicles when it may not play any other part.

34. 13
The infrastructure costs of underwater sensor networks and their deployment
underwater are significantly higher than for terrestrial systems which also has an
economic impact on these systems.
• Environment: Seawater is a particularly harsh environment for most materials which
means underwater sensors are much more prone to failure because of fouling and
corrosion than terrestrial sensors, which do not generally require maintenance.
• Data Storage: The data storage capacity requirements in underwater sensors will
generally be higher than those of terrestrial sensors. This is because of the greater
potential for connectivity losses due to poor channel conditions and the lack of spatial
correlation underwater as GPS or other forms of positioning techniques are not
available.
2.3 Taxonomy of Mobile Underwater Wireless Sensor Network
To begin this work and to provide a context in which to place this work, a new taxonomy for
mobile UWSN has been developed, see Figure 2.1, adjusted from the taxonomy for underwater
acoustic networks [101, 74]. This taxonomy for mobile UWSN is build from the possible network
operating environments and is used to discuss the implication of these classifications on the
communication protocol structures. These operating environments are based on the
deployment configurations expressed by the density of vehicles and sensor network coverage
area, which is defined as a volume due to the 3D nature of operations underwater [105]. These
two dimensions impact on the design of the MAC and network-layers [101] and will provide a
framework in which a broader discussion on the communication requirements of an Underwater
Swarm Sensor Network can be defined. In the following section, this will be expanded upon
through an evaluation of the literature available.
Underwater mobile networks that cover large geographical areas, identified by the top two ‘blue’
quadrants in Figure 2.1, have been recognised to have similar network characteristics to those
encountered in Delay / Disruptive Tolerant Networking (DTN) [10, 53, 54, 74, 101, 146]. While
originally developed for deep space networking and interplanetary communication, DTN is a
field that is seeing significant research and development due to its applicability to satellite and
sensor-based networks as well as acoustic and underwater applications [48, 70, 139].
Characteristics of a DTN are that may lack continuous network connectivity, have long and
variable delays, have limitations due to the wireless range, high error rates, asymmetric data
rates and have demanding energy and noise issues [85,139]. A DTN can be a network of
smaller networks or subnets, and generally have to deal with disruptions due to link outages
that are likely to occur due to large distances between mobile nodes as well as orbital
mechanics issues in space or topographical disorientation due to mobility of receivers [138].

35. 14
Decreasing Latency
Increasing Connectivity
Increasing Throughput
Increasing Vehicle Collision Potential
Ra
ng
e
det
er
mi
ne
d
Ac
ou
stic
Ra
ng
e
S
m
ll
La
rg
Networ
k
Covera
ge
Volume
Link
Laye
r
beco
mes
partit
ione
d.
Hidd
en/e
xpos
ed
termi
nalsLink
Laye
r
unpa
rtitio
ned.
Hidd
en/e
xpos
ed
termi
nals
is
com
mon.
Small Numbers Large Numbers
Density of Vehicles
Figure 2.1: Taxonomy for Mobile Underwater Wireless Sensor Networks
These characteristics and features are analogous to underwater long-range networks, although
the range magnitudes are different (kilometres (underwater) rather than tens of km (space)),
and thus there are several important similarities in terms of communication structures that can
be considered when designing underwater networks. In both environments intermittent
connectivity leads to the absence of an end-to-end path between a source and destination, that
is called network partitioning, and requires specific communication techniques to allow a
network to continue to function. In addition, even if it is not absent, the end-to-end path can
experience significant delays due to the long and variable propagation delays between nodes
and the variable queuing delays that can be expected at a node. In this case, ACK or
retransmission strategies need to be carefully implemented to minimise further delays. With
higher error rates expected there is a need to consider either bit error correction or
retransmission of whole packets which results in more processing and network traffic which will
reduce bandwidth efficiency of a network that is of particular concern in underwater acoustic
communication. To overcome these problems in the terrestrial/space environments DTNs have
successfully adapted a multi-hop ‘store and forward’ message switching approach. This
provides a mechanism where data transmissions are held when a link is unavailable and then
Underwater Mobile
DTN
Extremely Sparse
Underwater Mobile
DTN
Multi-subnets
Underwater Swarms
USSN
Single Cluster
Underwater Swarms
USSN
Multi-Cluster
AUVs, Gliders& Drifters
Multi-Hop
Point-to-point
Point-to-multipoint
Single-Hop
AUVs

36. 15
allows it to continue when a path to its destination becomes available, and therefore a delay will
only occur on one of the links between source and destination.
Multi-hop ‘store and forward’ and other approaches are also being investigated for long range
underwater mobile DTNs. Thus, irrespective of density, communication in these networks are
defined as generally multi-hop and point-to-point. This has been shown to be beneficial in long-
range underwater networks, as it can improve energy efficiency due to the reduction in power
requirements to send data along shorter distances which is a major challenge that these
networks face [2, 19, 148].
In these networks, irrespective of environment, the predominate requirement of the Link Layer is
to maintain fairness among nodes and effective path determination through the network as well
as careful bit error correction and retransmission strategies while the routing layer has to deal
with the extremely long propagation delays. When the number of vehicles is extremely small
and coverage area is very large, the network will reach a limit of overlapping mobile coverage in
which case the network may not be able to form without other infrastructure in place. For the
other extreme, with networks growing large with increasing number of vehicles the current
limiting factor underwater will be the prohibitive costs associated with the devices themselves.
As the network coverage volume decreases and therefore the range between vehicles
decreases, as defined in the bottom two ‘green’ quadrants of Figure 2.1, all vehicles are defined
to be within direct contact of each other, and as such single-hop acoustic communication
networks are possible. Depending on the size of the network, they can operate as a single-
cluster or multi-cluster network depending on the application and traffic requirements. In either
case, these mobile networks have a high vehicle density compared to the DTNs and will be
referred to here as Swarm networks.
Communication between vehicles in these swarm networks can be point-to-point when
networks may only have a few vehicles however in general these networks are more likely to be
point-to-multi-point to increase distribution of information throughout the network. As the range
between vehicles becomes smaller and vehicles are operating within 10s of metres of each
other, vehicle collisions are a serious consideration and navigational information becomes
critical. As the numbers of vehicles in the network increases, so will the data throughput of the
network which the communication protocols and particularly the MAC layer will need to handle.
Maintaining collision-free messaging will support this increase as retransmission and additional
traffic can be avoided.
There has been significantly less research and development work done in the USSN field due to
the focus on applications around long-distant operations and the cost of technology
development and deployment underwater as discussed above. Within the research community,
however, there is a growing interest in underwater swarm sensor networks and it is in this area
that this work will concentrate. More specifically, the focus will be on the single cluster USSN,
represented in the bottom left quadrant of the Taxonomy, which will be more realistically the first

37. 16
development systems primarily due to the cost of AUVs and the fact that they will be less
complex in nature to deploy and test.
Thus a definition and brief description of a robotic swarm will be presented.
2.3.1Definition of Swarming and Robotic Swarm Networks
Swarms are systems where many individuals are organised and coordinated by principles of
decentralized control, self-organization and at least some form of local communication within
the swarm. There may also be remote communication to a supervisory or control node. A well
known and used definition of swarm (robotics) networks is taken from Sahin [112]: "Swarm
robotics is the study of how large numbers of relatively simple physical embodied agents can be
designed such that a desired collective behaviour emerges from the local interactions among
agents and between the agents and the environment". Sahin [112] also sets out four criteria that
apply when determining the degree to which the term "swarm robotics" should apply in a
specific case:
1. Large numbers of robots: The number of agents must be large or at least the control rules
allow it to be scalable
2. Homogeneous groups of robots: Swarm are often made up of a homogenous group of
agents or if heterogeneous then only with a small number of different types of agents
3. Relatively incapable or inefficient robots: A group of collaborating agents is required
because an individual agent is 'incapable' of completing a task
4. Robots with local sensing and communication capabilities: This ensures that
coordination is distributed [112].
The advantages of swarm networks are that they can cover a large area in detail both in terms
of the static coverage area based on the number of agents and over time with the mobility of the
swarm. Robotic swarms, which will be referred to as swarms, can perform monitoring and
search tasks as well as ‘real-time’ problem solving where they can act to prevent the
consequences of that problem [93]. The autonomy also means that they are very suited to
dangerous tasks, such as searching in mine fields and in dealing with hazardous events like
chemical leaks.
In essence, the swarm network provides the infrastructure that facilitates the collaborative
behaviours being implemented. The first and most fundamental design decision required is
whether the architecture of the network is to be centralised or decentralised, and if
decentralised whether it is hierarchical or distributed. Decentralised networks are claimed to
have several advantages over centralised networks, such as, reliability and scalability, and are
the predominant paradigm discussion and used today [93] as they reflect the 'biologically
inspired' notion of swarming in terms of 'emergent properties' and 'self-organisation'. However,
one of the open research questions is: does the scaling advantage of decentralised networks
offset the coordination advantage of centralised networks?

38. 17
The swarm collaborative behaviour infers a biologically motivated behaviour that is exhibited by
a set of similar kind or size of animals that are working together as a collective, such as swarms
of insects, flocks of birds and schools of fish. As described in Criteria 3 above, at an individual
level each agent can be modelled by a simple set of rules (such as Boids Algorithm described
below) that may not be complex yet the emergent behaviour of the swarm can be quite complex
and harder to model. These rules will need some information from the other agents and thus as
Criteria 4, sets out, some form of communication between agents is essential.
Bio-inspired networking techniques have begun to emerge over the last decades to take a new
approach in some of the most challenging areas of network developments such as large-scale
systems, heterogeneity and unattended operations. These bio-inspired solutions are being
sought from the bio-inspired computing and system application domains and look first at the
identification of analogies with well-researched biological systems. The biological principles of
swarm intelligence and social insects have found equivalencies in several network areas
including distributed search and optimisation, task and resource allocation, and WSNs (Wireless
Sensor Networks) [40].
2.4 Communication within Underwater Swarm Sensor Network
The current research and development in underwater swarm sensor networks has focus on
three major aspects:
• AUV design [28, 96, 121, 149];
• Development of the 'bio-inspired' swarm algorithms associated with localisation, formation
and cognition to build the collaborative behaviours [97, 115, 116, 140, 145]; and
• Communication requirements that are recognised as essential for implementing swarm
behaviours [22, 95, 115, 146].
The focus of this work is in the development of communication algorithms and thus the
remainder of this section will investigate the current developments in this area firstly focusing on
research around bio-inspired underwater communication and then more broadly on other short-
range approaches. A review of some of the localisation and formation algorithms for swarming
AUVs will be presented in Section 2.7 as the data requirements will impact on the network
traffic.
2.4.1Biologically Inspired Underwater Communication
For a group of underwater AUV’s to operate in a swarm like fashion they will need to have both
a swarm formation control algorithm and a communication system which is essential for
neighbouring vehicles to inform each other of formation actions and their location [82]. The
swarm control algorithms discussed in Section 2.7, require knowledge of the location, trajectory
and/or the ‘presence’ of at least one of the closest neighbour vehicles. It has been observed
that the types of communication that are available within swarms can fall into three categories;
Interaction via Environment, Interaction via Sensing and Interaction via Communication [18].

39. 18
(a)MONSUNII
UniofLubeck
(b) SHOAL BMT Group (c) CoCoRo Consortium
Figure 2.2: Various AUV’s
Interaction via Environment is an implicit communication system, which is also referred to as a
"cooperation without communication" [6] system. It is based on similar principles to the Ant
Colony Algorithm derived from the behaviour of ants finding food, where they leave 'pheromone
trails' which gain strength when they are the shortest path. The SHOAL project [121] has used
this technique in an interesting ways by leaving markers for navigation and to identify
measurement locations.
The SHOAL project uses four fixed bottom mounted sensors for localisation interacting with
mobile units so it is not a fully mobile autonomous swarm. Each SHOAL vehicle, Figure 2.2 (b)
has incorporated a set of AI (Artificial Intelligence) rules similar to the ACO approach that uses a
‘pheromone’ trail where obstacles are marked with a ‘potential’ and the target as a ‘sink’. The
vehicle uses the potential and sink as forces to navigate the vehicle away from obstacles and
towards the target.
Interaction via sensing is also an implicit communication system. It relates to techniques that
allow local interaction between vehicles using sensing systems that do not involve explicit
communication. The CoCoRo (Collective Cognitive Robots) project [27] is investigating several
types of sensor approaches to detect interactions, including active sonar, optical sensors and
blue LED light. Similarly, the MONSUN II AUV [90], Figure 2.2 (a) has integrated infrared
distance sensors into its front fins to avoid collisions with lateral obstacles, as well as a
visualisation method which uses the camera on the front of the vehicle to allow that vehicle to
follow another vehicle. They also plan to implement a frequency identification system in each
vehicle, based on the system believed to be used by dolphins to identify each other in a pod
[95]. Chen [22] uses the idea of using low-frequency short ‘whistle’-like messages to emulate
the long-haul vocalisation used by killer whales that has been important in the development of
long-distance acoustic communication. He has also incorporated an acoustic echoing system,

40. 19
similar to that used by bats, to obtain an estimate of the Doppler frequency shift of a signal,
which is then used for relative speed calculations.
Interaction via communication is an explicit communication where data is exchanged between
vehicles on a local and/or global level and can include either direct messaging from one
member specifically to another or by broadcast where the recipient maybe either known or
unknown. The focus of the work described in this thesis is on this explicit communication and
how many vehicles can access the wireless medium to exchange the required information.
2.4.2Explicit Short-Range Acoustic Communication
So far many of the projects that are developing underwater robotic swarms and using explicit
communication techniques are incorporating surfacing vehicles so that they can access GPS
signals for positioning and communication. Most commonly in homogeneous swarms is for each
vehicle, on a rotation basis, to take a turn on the surface [22, 90, 98, 103]. Alternatively, in a
heterogeneous swarm where one type of vehicle is designed as a base station and remains on
the surface throughout a mission [28] or where malfunctioning vehicles float to the surface and
active GPS for recovery [122].
The aim of the MONSUN II project [90] is to be able to deploy an operating swarm of
homogenous vehicles having a distributed hierarchical architecture on long-term operations to
monitor underwater environmental conditions. The approach taken by MONSUN II is to have at
least one of the homogenous vehicles floating on the surface to receive GPS signals that
maintains a fix on the swarms’ absolute position. Submerged AUV's will calculate their position
by the distance to the floating AUV and to their local neighbours using Received Signal Strength
Indication (RSSI) and the transmitted power levels. The vehicles rotated on a regular basis
taking on the role at the surface which includes becoming the lead vehicle until the next vehicle
surfaces. This gives the swarm the advantages of being able to access GPS signals for
absolute positioning, and to balance energy across the network through vehicle rotations.
Chen [22] similarly uses rotating vehicles on the surface for absolute positioning and to use the
last vehicle that surfaced as the lead vehicle as it has the most up-to-date position information.
Using a centralised approach, the new lead vehicle broadcasts a position report message to the
followers. Via a handshaking strategy the followers send position information in return. Once the
leader has received all position information, it can run the formation-mapping algorithm to find
the best position for all swarm vehicles. It broadcasts this back to followers who reply with an
acknowledgement. Chen also uses some implicit communications for relative positioning of
each of the vehicles in an attempt to reduce the communication overheads and this is done via
active sonar, which is a similar approach to bats echolocation mechanism. Here the Doppler
effect allows a vehicle to determine if a neighbouring vehicle is moving towards or away from it.
This worked showed the formation control coordination communication overheads were 2 times
higher than for terrestrial wireless channels due to the less reliable channel as well as the extra
retransmission required due to using contention based MAC layer.

41. 20
The communication networks designed by Petillo [103] and Paley [98] also rely on vehicles
surfacing periodically to access the RF network and to obtain GPS co-ordinates. Petillo
investigates the use of a swarms to monitor and follow an offshore ‘plume’, such as an algal
bloom or an oil spillage by using the RF network to obtain information that could re-direct
individual vehicles to more optimal sampling positions. Petillo believes that until better swarm
communication systems are developed, ‘Periodic Surface Communication' is necessary. The
disadvantage of the approach is that it takes away the ability to maintain a practical, real-time
operation, which limits the application and operational functionality and efficiency of the
network. This includes limiting the depths a swarm can access.
The CoCoRo (Collective Cognitive Robots) project [28] took a different approach for the network
architecture. It allows the swarm to have surface vehicles but also to access the sea-bottom.
CoCoRo incorporates two types of AUV's into a heterogeneous swarm network made up of 3
sub-swarms [116], Figure 2.2 (a). The two types of vehicles are a base station AUV that
accesses GPS and has a recharging facility and the CoCoRo design AUV that is an unusual but
simple U shape platform that can swim in 3-dimensions. As the aim is to develop sea bottom
search capability, the swarm is made up of 3 sub-swarms: the base station floating on the
surface, a group of CoCoRo AUV's used as a relay-swarm to communicate information from the
base station to the 3
rd
sub-swarm, the sea-bottom-swarm, which searches the sea-bed for its
specified target. The major focus of this work is on the nature of the cognition and 'self
awareness' required by each individual swarm vehicle and many biologically inspired solutions
are being investigated. The project is still at a very early stage of development and
communication protocols between robots are not specifically being investigated.
One of the oldest (known to the author) underwater swarm projects is Serafina [149], which has
since been discontinued, focused on the development of a vehicle that is very similar to the
MONSUN II vehicle, and uncommonly investigated low frequency RF signalling. In this project a
distributed algorithm using time based scheduling was presented. Time-based communication
algorithms for underwater swarms have also been used by several other projects [43, 87, 113,
123] and these will be examined in more detail in Chapter 4. The remainder of this chapter will
establish the application and traffic requirements, and ascertain the communication criteria and
boundaries for this work.
2.5 Underwater Swarm Sensor Network (USSN) Applications
The classification of application scenarios for UWSN has regularly been divided into variations
of delay intolerant or delay tolerant data requirements [5, 32, 105]. For USSN we propose a
modification to this classification that focuses on the network architecture and the consequential
required formation control algorithms that best suit the applications such that applications for
swarm networks can roughly be divided into Mission Time Critical or Mission Non-Time Critical
see Table 2.1.

42. 21
Table 2.1: Single Cluster Underwater Swarm Sensor Network (USSN) Applications
Application
Category
Mission time critical Mission non-time critical
Applications  Search, identification, target (objects, fish,
organisms, resources, pollutants, etc.)
 Inspections (harbours, structures, pipes) and
Military (Mine-countermeasures, hazardous
jobs)
 Scientific / Environmental sampling and
monitoring (salinity, temperature, sounds,
oxygen levels, hydrothermal helium, fish
migration, currents, pollutants, etc.)
 Oceanographic Surveying and
 Area Surveillance and Protection
Architecture Multi-vehicle coordination in a swarm like
arrangement travelling in a random ‘bio-inspired’
formation control configuration
Multi-vehicle coordination in a swarm like
arrangement traveling in a structured pattern
Operations Swarm continuously changing shape
Incorporating payload data in real time for
mission completion
Monitor for specified periods
Predominately Shallow (up to 300m)
Offshore, harbours, rivers etc
Analysis and incorporation of real-time payload
data into formation control algorithm
Endurance of several hours to days
Swarm maintains structure
 Collect and store payload data for later
downloading
Continuous monitoring
Shallow to Deep (up to 3000m)
Predominately Offshore but also harbour and
estuaries
 Endurance of day(s) / month(s)
Data exchange Continuous real-time localisation and payload
data exchange for speedy mission completion
Regular localisation data exchange to
maintenance formation structure
Energy
Requirements
Hours of Operation Day(s) to month(s) of Operation
2.5.1Time Critical Mission Deployment
Mission Time Critical applications are the more traditionally expected swarming style network
where vehicles operate in a ‘bio-inspired’ formation control pattern and endeavour to gain from
the power of a swarm’s intelligence. Examples of these applications include searching and
finding a target or object such as a black box, a geological vent or a pollutant source such as
finding a chemical leak. Inspection of harbours or underwater structures and military
surveillance applications including mine countermeasures and gathering information in the
battlefield are also generally time critical missions. These missions require speedy discovery
and often-urgent responses and are operating for short durations, lasting for hours rather than
days until targets are found.
The concept of swarm intelligence is important to the network structure and mission approach.
Swarm intelligence describes the behaviours that result from the local interactions of the
individual vehicles with each other and with their environment [58]. There are interesting
emergent properties that occur on the global scale in large swarms even when

43. 22
Figure 2.3: Fully Distributed Architecture for a Time Critical Mission using Underwater
Swarm Sensor Network
individuals have a restricted view of the system and only have interactions between neighbours
on a local scale, while operating in a coordinated way without a coordinator or external
controller. Most of the solutions, if appropriately modelled, are built on simple concepts and
rules, which are described in the Section 2.6.
These rules or formation control algorithm together with the payload sensor data that supports
the finding of the target in Time Critical Mission applications defines the data exchange
requirements, which is therefore strongly influenced by the real-time localisation and payload
data collected. This creates a random pattern of movement as vehicles manoeuvre in a swarm
like fashion and is referred to in this work as a Cluster Topology. The Cluster Topology,
illustrated in Figure 2.3, reflects the standard definition of a swarm.
This type of applications demands continuous exchange of real-time localisation and payload
data for speedy mission completion.
2.5.2Non-Time Critical Mission Deployment
Applications considered as non-time critical missions include environmental and scientific
sampling or surveying for mapping or bathymetry. These applications require that the payload
sensor data is collected along side the location where it was collected and does not need the
same level of real-time interaction or ‘swarm intelligence’ for enabling a speedy mission
completion. The focus in these applications is on the regularity of payload data collection with
the importance on the accurate location where the payload data was collected. This regularity
Surface
Sea Floor
Swarm
Vehicles

44. 23
Figure 2.4: Decentralised Hierarchical Architecture for a Non-time Critical Mission using
an Underwater Swarm Sensor Network
suggests a need for a structured pattern of formation where the exchange of data is focused on
maintenance of the structure. As GPS is unavailable underwater there are benefits that multi-
vehicle collaboration can have on better determination of their position [98, 122] especially over
longer missions.
Thus for these applications, vehicle deployment necessitates a structured and stable pattern of
motion that offers a consistent and steady sweep of an area using an arrangement of vehicles
in a line or V pattern that will be referred to in this work as a String Topology. The V pattern
seen in Figure 2.4, which can extend out to a line of vehicles, is used to sweep the widest area
while keeping communication ranges between vehicles as small as possible.
These missions may require days or months of operation and require a regular exchange of
localisation data to maintain accurate location and formation structure. The payload data needs
to be collected in association with the vehicles position and can be stored and retrieved at a
later time when vehicles are recovered.
2.6 USSN Communication Traffic Requirements
Sensor data is collected by each AUV for both navigational and mission purposes. Mission data
is the sensor data collected for the application, which for Mission Non-time Critical is generally
stored or for Mission Time Critical applications it is used as an input into the ‘bio-inspired’
formation control algorithm. Navigational data is determined from the collected localisation
data, which is required in both of the formation control algorithms that provide the trajectory
information for each vehicle. In addition, for USSN applications, irrespective of mission type,
vehicles will be operating in close range to each other and thus avoidance of vehicle collisions
is an important consideration.
Surface
Sea Floor
Swarm
Vehicles
Leader

45. 24
Therefore, an understanding of the localisation techniques and the formation control algorithms
is required to determine the kind and quantity of data traffic expected in each of the application
classifications. Examples of payload sensor types and their mechanisms are also included.
2.6.1Payload Data
There are a variety of sensors and mechanisms available to collect data for the different
applications and each of these have different data requirements. Table 2.2 provides a small
selection of underwater sensors that might be used for different applications. The amount of
payload data that is generated from these sensors can vary substantially.
For the purposes of this work, an initial assumption will be that the data for the Mission Non-
time Critical applications will be collected and stored and therefore will not impact on the
network traffic. For the Mission Time Critical applications the information gained from these
sensors will be included in the formation control algorithm, Rule 2, and can also be available to
be sent as lower priority data. In this case it will be aggregated to a manageable packet size not
more than 40 bytes plus overheads which will be further discussed in Chapter 5.
2.6.2Localisation
Localisation data is required to determine position of a vehicle. Localisation can be classified
into two different approaches: absolute or relative positioning. Absolute positioning is where
vehicles need to know their actual geographical position, and this information is normally
acquired using GPS or similar positioning technologies. GPS can be problematic even in some
terrestrial settings, such as in forests or indoors, and also in large-scale deployments because
of power consumption and costs [12]. GPS positioning is practically impossible underwater, due
to the attenuation of radio waves.
Where absolute positioning is necessary during an underwater swarm operation, this
information can be obtained by:
• using one or more anchored nodes of known position. These anchor nodes often use a
broadcast approach, where the anchor nodes are located at certain intervals over the target
area and are used to inform other vehicles in the network of their known position; or
• For non real-time requirements, where vehicles are able surface and use GPS to capture
the vehicles absolute position. Generally, surfacing occurs at the beginning and end of a
mission but can also occur following a specific trigger. Various examples of using GPS for
absolute positioning has been implemented in underwater settings [22, 31, 98, 103, 146].
When vehicles only need to know the relative position of their neighbours, this can be gained
explicitly by having each vehicle send location details with their messages or implicitly by using
properties of the message itself. Implicit approaches include:
• Time of Arrival (TOA) technique which is based on measures of the travel time of a
message;

46. 25
Table 2.2: Examples of Payload Sensor Types For Mission Time Critical and Non-time
Critical Applications
Mission Time Critical
Application Area Sensor Type Mechanism
Pollution detection (e.g.
detection of crude oil leaks or
ballast discharge)
Active Acoustic Sensor
Hydrocarbon & Methane
Sensors
Fluorometer
• Active transmissions are reflected by boundaries
between different media. Larger droplets or plumes of a
leaking medium will give a stronger backscattered
acoustic signal.
• Dissolved CH4 molecules diffuse through a thin-film
composite membrane into the detector chamber, where
their volume is determined by means of IR absorption
spectrometry
• Detection and measurement of fluorescent compounds
such as Chloropyhll, CDOM, Crude Oil, and Fluorescein,
Rhodamine, and UV Tracer Dyes
Shipwreck or Black Box or
archaeological locator
Magnetometer • Measures disturbance in earth’s magnetic field
Mission Non-time Critical
Application Area Sensor Type Mechanism
Oil and Gas exploration – (e.g.
detection of hydrothermal
vents)
Methane sniffers • Two measurement principles exist for measuring
methane dissolved in water that are based on: dissolved
methane diffusing over a composite membrane into an
internal gas circuit where the CH 4 concentration is
measured with infrared spectrometry and directly into a
sensor chamber
Scientific/Environmental
sampling
Salinity, temperature,
depth or oxygen sensors,
phytoplankton
• Numerous instruments are used for measuring physical
aspects of the ocean for post mission analysis. E.g.
conductivity (salinity), temperature, pressure (depth),
dissolved oxygen, Chlorophyll A fluorometry
(phytoplankton) etc.
Surveying ocean bottom,
Bathemetry
Single beam echo
sounders
Multi beam echo
sounders,
Side scan sonars
Sub-bottom profilers
• The instruments currently used for this work require
large energy levels. This drives battery size and hull size
so that they cannot be currently integrated into small
swarming AUVs.
• New concepts are emerging where transmission of the
active sonar signal might occur from a “mother ship” and
the AUV swarm will be used to detect the return signal
from the seafloor.
• It can be anticipated that data will be required to be
distributed in the swarm (e.g. time and location) to
support post mission analysis.

47. 26
• Time Difference of Arrival (TDOA) technique which is based on measures on the difference
of arrival time at different antennas;
• Angle of Arrival (AOA) technique that uses measurements of the relative angle between
nodes;
• Receiver Signal Strength Indicator (RSSI) technique that determines range from the power
in the received signal and the Doppler shift measurement of the relative inter-vehicle
velocity.
There are also some interesting biologically inspired alternative approaches suggested for
providing relative positioning underwater and some of these were discussed in Section 2.4.1.
2.6.2.1 Underwater Vehicle (AUV) Navigation
The most commonly used technique for navigation within an AUV underwater is a traditional
method of nautical navigation known as Dead-Reckoning [92]. To determine the position,
orientation and velocity of a vehicle, AUVs are generally equipped with an Inertial Navigation
System, which includes accelerometers, gyroscopes, doppler velocity technology (DVL) and a
magnetic compass. At the beginning of an operation the starting depth, latitude and longitude
are entered into the Inertial Navigational System that will perform the Dead Reckoning
calculations. The system then receives information from the on board navigational sensors that
measure motion along three or more axes enabling continual and accurate calculations of the
vehicle’s current depth, latitude and longitude. The advantages of this approach is that once the
starting position is set, the device does not need external information, which can be hampered
by poor weather conditions and, means for military operations, the vehicle cannot be detected
or jammed. The disadvantage of the dead reckoning approach is that errors in position will
accumulate because the current position is calculated solely from the previous position and that
vehicle drift, due to the impact of water currents or collisions for example are more difficult to
take into account. It can be assumed, however, that in most situations vehicles working in close
proximity will be subjected to similar motions and therefore their relative position will be
maintained. Generally this means that the geographical position of vehicles with inertial
navigation systems should be corrected from time to time with a local ‘fix' from other types of
navigation systems such as magnetic compass (for heading) or a GPS (latitude and longitude)
on the surface, particularly for long operations and non-time critical applications. Pressure
sensors are also included on-board vehicles depth measurements.
An example of a simpler and less computationally intensive approach, is the SeaVision
™
prototype vehicle that uses only a LinkQuest NavQuest DVL and Compass [84, 86]. These
provide the localisation data that is feed into the main on-board computer in an 80 ASCII
character format and included: Pitch; Roll; Heading; Temperature; Velocity relative to current;
and velocity relative to bottom. The relative position, direction and velocity of the vehicle could
then be calculated. Aggregating the data is both practical and valuable to reduce the amount of
information sent around the swarm due to the high latency and low bandwidths of an