Design and Use of Anatomical Atlases for Radiotherapy

207 Pages·2015·5.75 MB·English

UNIVERSITY PARIS 6 PIERRE ET MARIE CURIE
P H D T H E S I S
To obtain the title of
DOCTEUR DE L’UNIVERSITE PIERRE ET
MARIE CURIE
Speciality: Computer Science
Autonomous or Assisted Deployment
by Mobile Robots of Wireless Sensor Networks:
Coverage and Connectivity Issues
Defended by
Khoufi
Ines
On September 30, 2015
Thesis Advisor: Mrs. Pascale Minet
Thesis Co-Advisor: Mr. Anis Laouiti
Prepared at Inria Paris-Rocquencourt Research center
Eva Team
Jury:
Mr. Nadjib Achir Reviewer Associate Professor HDR, University of Paris 13
Mr. AndrØ-Luc Beylot Reviewer Professor, ENSEEIHT, France
Mrs. Pascale Minet Advisor Research Scientist HDR, Inria Rocquencourt, France
Mr. Anis Laouiti Co-Advisor Associate Professor HDR, Telecom SudParis, France
Mr. Marcelo Dias de Amorim Examiner Research Director , Pierre & Marie Curie University
Mr. Azzedine Boukerche Examiner Professor, University of Ottawa, Canada
Mrs. Leila Saidane Examiner Professor, ENSI, Manouba University, Tunisia
Mr. Tuan Dang Examiner Doctor, Engineer Research Expert, EDF, France

Acknowledgements
I would like to begin by expressing my gratitude to Mr Nadjib Achir and Mr AndrØ-Luc
Beylot for kindly accepting to review my thesis. Also, my thanks go to the members of
the jury for the interest they showed in my research: Mr Marcelo Dias de Amorim, Mr
Azzedine Boukerche, Mrs Leila Saidane and Mr Tuan Dang.
I would never have been able to (cid:28)nish my dissertation without the guidance, help and
support of colleagues, friends and family. I thank all those people who made this thesis
possible and an unforgettable experience for me.
Firstly,IwouldliketoexpressmydeepestsenseofgratitudetomyadvisorMrsPascale
Minet for her continuous support of my PhD study, for her knowledge and immense
encouragement. Her guidance helped me throughout the research and writing of this
thesis. I cannot have imagine having a better advisor and mentor for my PhD study.
I would like to express my special appreciation to my co-advisor Mr Anis Laouiti, for
encouraging my research and for helping me to further my career as a research scientist.
I have been honored to work with the members of Eva Team, formerly ’HIPERCOM’.
Thank you: Erwan Livolant, Selma Boumerdassi, Dana Marinca, Cedric Adjih, Paul
Muhlethaler and Thomas Watteyne for the pleasant working atmosphere you created. I
also thank Christine Anocq for her valuable help, support and friendly company. She
always made my administrative tasks so much easier.
I also am happy to have collaborated with Saoucene Mahfoudh, Erwan Livolant, Mo-
hamed Haddad, Mohamed Amine Koulali and Nadia Boufares.
I want to address my kind regards to the many friends that I have made at Inria, for
their wonderful company and sincere friendship: thank you Nesrine, Hana, Ichrak, Ines,
Saoucene, Ridha, Younes, Alaa and Haykel. I am grateful to all of you, for your support
as well as the fun times I have shared with you.
I am indebted to Richard James, our English teacher at Inria. His interesting lessons
improved my English ’beyond measure’ (in his opinion!). I would also thank him, for the
considerable time and e(cid:27)ort he spent correcting my papers and this dissertation.
I must take this opportunity to express my heartfelt gratitude to my family. Words
cannot express how grateful I am for your support and kind thoughts. Their prayers for
me have sustained me over the years.
SpecialthanksmustgotomyhusbandandbestfriendBilel. Hewasalwaysmysupport
in those moments when there was no one to answer my queries, and there to cheer me up
and stand by me through both the good times and the bad.
Finally, this research would not have been possible without the (cid:28)nancial assistance
of the Cluster CONNEXION (digital command COntrol for Nuclear EXport and renova-
tION) project.
ii
To my Family
iii
Abstract
Wireless sensor networks are deployed to monitor physical phenomena. The accuracy
oftheinformationcollecteddependsonthepositionofsensornodes. Thesepositionsmust
meet the application requirements in terms of coverage and connectivity, which therefore
requires the use of deployment algorithms.
This thesis focuses on the deployment of wireless sensor nodes: (cid:28)rstly when the nodes
are autonomous, and secondly when they are static and the deployment is assisted by
mobile robots. In both cases, this deployment must not only meet the application’s cover-
age and connectivity requirements, but must also minimize the number of sensors needed
while satisfying various constraints (e.g. obstacles, energy, fault-tolerant connectivity).
We propose several autonomous deployment algorithms, based on the virtual forces
strategytomonitor2Dand3Dareas. Sincethevirtualforcesstrategysu(cid:27)ersfromthenode
oscillations problem, we have designed the ADVFA algorithm that adapts the distance
between neighboring sensor nodes to the number of connected nodes. ADVFA avoids
useless moves in order to reduce node oscillations. We also propose the GDVFA algorithm
to cope with the node oscillations problem. GDVFA is a hybrid algorithm that combines
the virtual forces strategy with the grid strategy to stop node oscillations. In addition,
since the monitoring area may be unknown and contain obstacles, we propose the OA-
DVFA algorithm. For a 3D area, we have designed the 3D-DVFA algorithm, based on a
3D version of the virtual forces algorithm.
Autonomous deployment may be expensive when the number of mobile sensor nodes
is very high. In this case, an assisted deployment may be necessary: the nodes’ positions
being pre-computed and given to mobile robots that place a static sensor at each position.
To compute the optimized number of nodes needed to fully cover a 2D area containing
obstacles, we propose the OAD-Area algorithm. We also propose OAD-PoI, to optimize
the relay node positions and ensure a fault-tolerant connectivity between each Point of
Interest (PoI) and the sink. Once the sensor node positions have been computed, they can
be given to mobile robots to carry out the actual deployment. We adopt two approaches
to optimize the deployment duration. The (cid:28)rst one is based on game theory to optimize
thelengthofthepathsoftworobots(TRDS),andthesecondisbasedonamulti-objective
optimization, for multiple robots (MRDS). The objectives to be met are: optimizing the
duration of the longest tour, balancing the durations of the robot tours and minimizing
the number of robots used, while bypassing obstacles.

Contents
Acknowledgement i
Abstract iii
1 General Introduction 3
1.1 Context and motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Main contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Manuscript organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Part I : State of the art 9
2 Coverage and connectivity issues in WSNs 11
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 De(cid:28)nition of coverage and connectivity problems in WSNs . . . . . . . . . 11
2.2.1 Coverage problems . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.2 Connectivity problems . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3 Representative use cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4 Coverage and connectivity problems with regard to R and r . . . . . . . . 19
2.5 Coverage and connectivity with regard to regular optimal deployment . . 21
2.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3 Deployment algorithms in WSNs 25
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2 Analysis of the criteria of deployment algorithms . . . . . . . . . . . . . . 25
3.2.1 Factors impacting the deployment . . . . . . . . . . . . . . . . . . 25
3.2.2 Common assumptions and models . . . . . . . . . . . . . . . . . . 27
3.2.3 Criteria for performance evaluation . . . . . . . . . . . . . . . . . . 28
3.3 Area coverage and connectivity algorithms . . . . . . . . . . . . . . . . . . 29
3.3.1 Full coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3.2 Partial coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3.3 Intermittent connectivity . . . . . . . . . . . . . . . . . . . . . . . 37
3.3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.4 Barrier coverage and connectivity algorithms . . . . . . . . . . . . . . . . 40
3.4.1 Full barrier coverage . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.4.2 Partial barrier coverage . . . . . . . . . . . . . . . . . . . . . . . . 44
3.4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.5 Point coverage and connectivity algorithms . . . . . . . . . . . . . . . . . 45
3.5.1 Static PoI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.5.2 Mobile PoIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
vi Contents
3.5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.6 Node activity scheduling with regard to coverage . . . . . . . . . . . . . . 48
3.6.1 Nodeactivityschedulingbasedonmessageexchangesbetweenneigh-
bors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.6.2 Node activity scheduling based on implicit coordination . . . . . . 50
3.7 Guidelines for selecting a deployment algorithm . . . . . . . . . . . . . . . 50
3.8 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Part II : Models and theoretical computation for an optimized de-
ployment in 2D and 3D 55
4 Models for an optimized deployment 57
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2 Models for 2D deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2.1 Sensing range and communication range in 2D . . . . . . . . . . . 57
4.2.2 The area considered and obstacles in 2D . . . . . . . . . . . . . . . 57
4.3 Models for 3D deployment . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.3.1 Sensing range and communication range in 3D . . . . . . . . . . . 59
4.3.2 The area considered and obstacles in 3D . . . . . . . . . . . . . . . 60
4.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5 Theoretical computation of an optimized deployment in 2D and 3D 61
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.2 Theoretical computation of an optimal 2D deployment . . . . . . . . . . . 62
5.2.1 Target distance in the optimal deployment . . . . . . . . . . . . . . 62
5.2.2 Optimal number of sensors to cover a given area . . . . . . . . . . 63
5.2.3 Computation of the e(cid:27)ective distance . . . . . . . . . . . . . . . . . 65
5.3 Theoretical computation of an optimized 3D deployment . . . . . . . . . . 66
5.3.1 Best polyhedron tessellation for 3D space . . . . . . . . . . . . . . 67
5.3.2 Optimized number of nodes to cover 3D space . . . . . . . . . . . . 68
5.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Part III : Autonomous deployment 73
6 Virtual forces based algorithms 75
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.2 DVFA: Distributed Virtual Forces Algorithm . . . . . . . . . . . . . . . . 76
6.2.1 DVFA principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.2.2 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . 78
6.2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.3 How to cope with node oscillations . . . . . . . . . . . . . . . . . . . . . . 82
6.3.1 ADVFA: Adaptive Distributed Virtual Forces Algorithm . . . . . . 82
6.3.2 GDVFA: Grid Distributed Virtual Forces Algorithm . . . . . . . . 86
Contents vii
6.3.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.4 How to cope with the presence of known or unknown obstacles . . . . . . 94
6.4.1 Obstacles and deployment algorithms . . . . . . . . . . . . . . . . 94
6.4.2 OA-DVFA:ObstaclesAvoidanceDistributedVirtualForcesAlgorithm 95
6.4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.5 How to use virtual forces in 3D . . . . . . . . . . . . . . . . . . . . . . . . 102
6.5.1 3D-DVFA: 3D Distributed Virtual Forces Algorithm . . . . . . . . 102
6.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
Part IV : Assisted deployment 109
7 Optimized deployment in the presence of obstacles 111
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
7.2 First problem: full area coverage and connectivity . . . . . . . . . . . . . . 112
7.2.1 Optimized deployment in an irregular area . . . . . . . . . . . . . . 113
7.2.2 Optimized deployment in an irregular area with opaque obstacles . 116
7.2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
7.3 Second problem: PoI coverage and connectivity . . . . . . . . . . . . . . . 120
7.3.1 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
7.3.2 De(cid:28)nition of relay node placement problems . . . . . . . . . . . . . 121
7.3.3 Solution for Relay Node Placement: RNP . . . . . . . . . . . . . . 123
7.3.4 Solution for Fault-tolerant RNP: FT-RNP . . . . . . . . . . . . . 128
7.3.5 Solution for Constrained fault-tolerant RNP: CFT-RNP . . . . . . 132
7.3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
7.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
8 Optimization of Robot Trajectories 139
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
8.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
8.3 Two-robot assisted deployment: based on a game theory approach . . . . 141
8.3.1 Assumptions and de(cid:28)nitions . . . . . . . . . . . . . . . . . . . . . . 142
8.3.2 Deployment duration and obstacles . . . . . . . . . . . . . . . . . . 142
8.3.3 Problem formalization . . . . . . . . . . . . . . . . . . . . . . . . . 143
8.3.4 Problem resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
8.4 Multi-robot assisted deployment: based on a multi-objective optimization
approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
8.4.1 Problem formalization . . . . . . . . . . . . . . . . . . . . . . . . . 149
8.4.2 NSGA-II based approach for MRDS optimization . . . . . . . . . . 152
8.4.3 Hybrid algorithm for the MRDS problem . . . . . . . . . . . . . . 155
8.4.4 Problem resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
8.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
Contents 1
Part V : Discussion and Conclusion 161
9 Conclusion and perspectives 163
9.1 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
9.1.1 Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
9.1.2 Application to the Cluster Connexion project . . . . . . . . . . . . 165
9.2 Perspectives and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 165
9.2.1 More realistic models . . . . . . . . . . . . . . . . . . . . . . . . . . 166
9.2.2 From 2D toward 3D . . . . . . . . . . . . . . . . . . . . . . . . . . 167
9.2.3 Implementation on real robots . . . . . . . . . . . . . . . . . . . . 167
9.2.4 Use of our algorithms to collect data . . . . . . . . . . . . . . . . . 167
List of Publications 169
A A Robot Trajectory Optimization 171
A.1 Introduction and motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 171
A.2 Formalization of the RDS problem . . . . . . . . . . . . . . . . . . . . . . 171
A.3 Proposed algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
A.3.1 The exact solution . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
A.3.2 2-Opt algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
A.3.3 Genetic algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
A.3.4 Hybrid algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
A.4 Comparative evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
A.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
A.5.1 Obstacles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
A.5.2 Capacity constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
A.5.3 Energy constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
A.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
B RØsumØ 181
B.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
B.1.1 Les problŁmes de couverture et de connectivitØ . . . . . . . . . . . 181
B.1.2 Les problŁmes de connectivitØ . . . . . . . . . . . . . . . . . . . . . 182
B.2 Contraintes, hypothŁses et modŁles thØoriques pour le dØploiement en 2D
et 3D. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
B.3 DØploiement autonome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
B.4 DØploiement assistØ par robots mobiles . . . . . . . . . . . . . . . . . . . . 188
B.4.1 Calcul du dØploiement optimisØ . . . . . . . . . . . . . . . . . . . . 189
B.4.2 Optimisation des trajectoires des robots . . . . . . . . . . . . . . . 192
B.5 Conclusion et perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
B.5.1 SynthŁse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
B.5.2 Perpectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
Bibliography 195