impacts of self-organized mechanisms in wireless sensor...
TRANSCRIPT
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No: 2008-ISAL-0026 Year 2008
Thesis
Impacts of Self-organized Mechanisms in
Wireless Sensor Networks
defend at
l’Institut National des Sciences Appliquées de Lyon
for the degree of
Doctor of Philosophy
Ecole Doctorale Informatique et Information pour la Societe
by
JiaLiang LU
dissertation May 6
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Committee in charge: AL AGHA Khaldoun (Reviewer)
Professor, University Paris Sud
BARTHEL Dominique (Examiner)
Senior researcher, France Telecom R&D, Grenoble
FLEURY Eric (Co-supervisor)
Professor, ENS Lyon
Noel Thomas (Examiner)
Professor, Louis Pasteur University Strasbourg
Simplot-Ryl David (Reviewer)
Professor, University Lille 1
Ubeda Stephane (Examiner)
Professor, INSA Lyon
VALOIS Fabrice (Co-supervisor)
Associated professor, INSA Lyon
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“To observe without the observer”
Jiddu Krishnamurti
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Remerciements v
Remerciements
Cette thèse a été effetuée au sein du Centre d’Innovation en Télécommunications et Inté-
gration de Services (CITI), à l’INSA de Lyon, et dans le projet ARES de l’INRIA Rhône-
Alpes, en collaboration avec le Centre de Recherche et Développement de FranceTélé-
com Grenoble et Beijing.
Je tiens tout d’abort à remercier Fabrice Valois sans, qui cette thèse n’aurait, je
pense, jamais eu lieu. Je le remercie de m’avoir encadré non seulement durant la thèse
mais également pendant mon mastère de recherche, en me donnant les outils et surtout
l’esprit de recherche pour réussir dans ce domaine. Je le remercie de m’avoir me toujour
soutenu mais aussi crétiqué pour réussir cette thèse. Je remercie également Eric Fleury
qui m’a co-encadré durant cette thèse et a su partager son expertise scientifique.
Je remercie tout particuliérement Monsieur Khaldoun Al Agha, Professeur à l’Université
de Paris-Sud, et Monsieur David Simplot-Ryl, Professeur à l’Université de Lille qui ont
accepté de juger mes traveaux de thèse et d’en être les rapporteurs.
Je remercie également Monsieur Dominique Barthel, Chercheur Sénior à FranceTélé-
com R&D de Grenoble, Monsieur Thomas Noel, Professeur à l’Université de Strasbourg
et Monsieur Stéphane Ubeda, Professur à l’INSA de Lyon d’avoir accepté de faire partie
de ce jury de thèse et de s’être intéressés à mon travail.
Je remercie également Monsieur Mischa Dohler avec qui j’ai passé ces moments de
concentration riche, tant pour son apport scientifique que personnel.
Je tiens à remercier Yvan Royon et Noha Ibrahim pour les discussions scientifiques,
sociales ou personnelles que nous avons eu. Je remercie également Fabrice Theo-
leyre, Nathalie Mitton, Tahiry Razafindralambo, Karel Heurtefeux, Elyès Ben Hamida,
Thomas Watteyne, Katia Jaffrès-Runser avec qui les nombreuse exchanges scientifiques
ont eu lieu. Je remercie Haila Wang, Song Wang et Yu Zhang qui m’ont aidé à dévelop-
per les activité de recherche à FranceTélécom Beijing. Je remercie également tous les
chercheurs du laboratoire CITI (pêle-mêle Jean-Marie Gorce, Guillaume Chélius, Is-
abelle Augé-Blum, et tant d’autres que je ne nommerai pas par manque de place).
Enfin, je tiens à remercier Yi, mon epouse, qui a su m’accompagner (physiquement et
moralement) dans cette grande expérience scientifique mais surtout personnelle qu’est
une thèse.
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Résumé
Un réseau de capteurs est un réseau radio multi-sauts formé par une quantité impor-
tante de capteurs identiques. Contrairement aux noeuds d’un réseau ad hoc, les cap-
teurs ont des nombreuses contraintes, notamment en termes de consommation d’énergie,
de capacité de calcul et de stockage. L’absence d’infrastructure et de contrôle centralisé
implique une collaboration efficace et pertinente des noeuds du réseau de capteurs.
Ces travaux de thèse s’inscrivent dans la problématique d’auto-organisation des
réseaux de capteurs. De notre point de vue, l’auto-organisation est un problème fonda-
mental des réseaux de capteurs conduisant à construire une vue logique de la topolo-
gie du réseau physique et de fournir des protocoles de communication basés sur cette
vue logique. Nous avons donc proposé une architecture d’auto-organisation (nommée
FISCO) permettant d’organiser le réseau - via des interactions locales uniquement-
sous le forme soit d’un arbre soit d’un treillis. Les propriétés structurelles et les perfor-
mances de FISCO ont été évaluées puis comparées aux principaux mécanismes d’auto-
organisation de la littérature. Cette architecture exhibe de très bonnes performances
en termes de stabilité, persistance, robustesse et surtout gestion de l’énergie.
Ensuite, nous avons revisité les principaux défis de la communication des réseaux
de capteurs en se basant sur l’auto-organisation préalablement introduite. Nous nous
sommes donc intéressé aux problèmes clefs suivant : allocation dynamique d’adresses,
protocole d’inondation, dissémination et agrégation de données dans les réseaux de
capteurs. L’impact de l’auto-organisation est clairement constaté en comparant avec
les approches classiques. Notons que la structure d’auto-organisation offre également
une solution très efficace et très flexible pour la gestion de puits mobiles et multiples
dans un réseau de capteurs. Le travail réalisé dans cette partie repose sur l’utilisation
d’outils issus de la théorie des graphes, de l’algorithmique distribuée mais aussi de
l’évaluation de performances.
En parallèle, nous avons cherché à justifier l’utilisation d’approche auto-organisée
(i.e. structurée) par rapport aux approches à plat. Pour ce faire, nous avons introduit
une nouvelle métrique d’évaluation hérité de la notion d’entropie statistique. Cette
métrique est la première métrique quantitative qui mesure l’ordre de l’organisation
dans un réseau sans fil multi-sauts. Cette métrique ouvre de nombreuses pistes de
recherches dans le domaine de l’auto-organisation des réseaux.
Nous avons aussi proposé une plate-forme d’expérimentation composant des 30 cap-
teurs. Cette plate-forme expérimentale est utilisée pour valider les solutions proposées.
Ainsi, les contributions des travaux de la thèse couvrent à la fois le domaine théorique,
mais aussi la simulation et l’expérimentation.
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Contents vii
Contents
1 Introduction 1
1.1 Popularity of Wireless Sensor Networks . . . . . . . . . . . . . . . . . . 2
1.1.1 Advanced sensor nodes . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 Ease of deployment . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1.3 Versatile applications . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Description of WSN in Our Consideration . . . . . . . . . . . . . . . . . 6
1.3 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.1 Setting up and organizing . . . . . . . . . . . . . . . . . . . . . . 7
1.3.2 Managing and maintaining . . . . . . . . . . . . . . . . . . . . . 8
1.3.3 Support of applications and services . . . . . . . . . . . . . . . . 9
1.3.4 Energy efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 State of the Art 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Self-configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.1 Autoconfiguration in IP networks . . . . . . . . . . . . . . . . . . 15
2.2.2 Address conflict detection based solutions . . . . . . . . . . . . . 17
2.2.3 Distributed DHCP solutions . . . . . . . . . . . . . . . . . . . . . 18
2.2.4 Synthesis on self-configuration . . . . . . . . . . . . . . . . . . . 20
2.3 Self-organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.1 Network model formalism . . . . . . . . . . . . . . . . . . . . . . 22
2.3.2 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3.3 Virtual backbone . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3.3.1 Connected dominating set . . . . . . . . . . . . . . . . 25
2.3.3.2 Maximal independent set . . . . . . . . . . . . . . . . . 26
2.3.3.3 Relative neighborhood graph . . . . . . . . . . . . . . . 27
2.3.3.4 Local minimum spanning tree . . . . . . . . . . . . . . 28
2.3.4 Source dependent . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.3.5 Synthesis on self-organization . . . . . . . . . . . . . . . . . . . . 29
2.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3 FISCO: An Autonomous Architecture for WSN 33
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2 FISCO Highlights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . 34
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3.2.2 Hierarchical structure . . . . . . . . . . . . . . . . . . . . . . . . 353.2.3 Two-level address allocation . . . . . . . . . . . . . . . . . . . . . 36
3.2.4 FISCO messages . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.3 Join of Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383.3.1 One-hop address allocation . . . . . . . . . . . . . . . . . . . . . 39
3.3.2 Two-hop address allocation . . . . . . . . . . . . . . . . . . . . . 40
3.3.3 Creation of a new partition . . . . . . . . . . . . . . . . . . . . . 403.4 Departure of Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.4.1 Departure of a member node . . . . . . . . . . . . . . . . . . . . 41
3.4.2 Departure of a gateway node . . . . . . . . . . . . . . . . . . . . 42
3.4.3 Departure of a leader node . . . . . . . . . . . . . . . . . . . . . 433.5 Partition Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.5.1 Partition splitting . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.5.2 Partition detection . . . . . . . . . . . . . . . . . . . . . . . . . . 453.5.3 Partition merge . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.6 Local Re-organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.7 Mesh Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483.8 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.8.1 Analysis on FISCO backbone . . . . . . . . . . . . . . . . . . . . 51
3.8.2 Message complexity analysis . . . . . . . . . . . . . . . . . . . . 55
3.9 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 553.9.1 Self-configuration related properties . . . . . . . . . . . . . . . . 56
3.9.1.1 Configuration message overhead . . . . . . . . . . . . . 57
3.9.1.2 Configuration latency . . . . . . . . . . . . . . . . . . . 583.9.1.3 Evolution of partitions during configuration . . . . . . . 59
3.9.1.4 Energy consumption for configuration . . . . . . . . . . 60
3.9.2 Self-organization related properties . . . . . . . . . . . . . . . . . 603.9.2.1 FISCO backbone . . . . . . . . . . . . . . . . . . . . . . 61
3.9.2.2 Local structure characteristics . . . . . . . . . . . . . . 62
3.9.2.3 Long term message overhead . . . . . . . . . . . . . . . 633.9.2.4 Energy consumption for organization . . . . . . . . . . 63
3.9.3 Impact of re-organization on lifetime . . . . . . . . . . . . . . . . 65
3.9.4 Energy consumption of FISCO mesh organization . . . . . . . . 65
3.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4 Data Dissemination and Data Aggregation 69
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.2 Data Dissemination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 724.2.1 Overview of data dissemination schemes . . . . . . . . . . . . . . 72
4.2.2 Backbone based data dissemination . . . . . . . . . . . . . . . . 74
4.2.2.1 Directed query forwarding . . . . . . . . . . . . . . . . 754.2.2.2 Data notification and data forwarding . . . . . . . . . . 76
4.2.3 Management of multiple mobile sinks over BBDD . . . . . . . . 77
4.2.4 Analysis on data dissemination . . . . . . . . . . . . . . . . . . . 78
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4.3 Data Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.3.1 Where to aggregate . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.3.2 When to aggregate . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.3.3 Adaptive-ARMA model for data aggregation . . . . . . . . . . . 85
4.3.3.1 ARMA model . . . . . . . . . . . . . . . . . . . . . . . 85
4.3.3.2 Local A-ARMA computation . . . . . . . . . . . . . . . 86
4.3.4 Analysis on A-ARMA . . . . . . . . . . . . . . . . . . . . . . . . 88
4.3.4.1 Accuracy and efficiency . . . . . . . . . . . . . . . . . . 88
4.3.4.2 Under erroneous measurements . . . . . . . . . . . . . . 90
4.3.5 Highlights of A-ARMA technique . . . . . . . . . . . . . . . . . . 93
4.4 SODA Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.4.1 Spatial packet merge on leader nodes . . . . . . . . . . . . . . . . 95
4.4.2 Performance evaluation on data collection . . . . . . . . . . . . . 95
4.4.2.1 Message cost during data collection . . . . . . . . . . . 97
4.4.2.2 Active time during data collection . . . . . . . . . . . . 97
4.4.2.3 Energy consumption during data collection . . . . . . . 100
4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5 Entropy of Organization 105
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.2 Original Definitions of Entropy . . . . . . . . . . . . . . . . . . . . . . . 106
5.3 Extended Definition of Entropy . . . . . . . . . . . . . . . . . . . . . . . 107
5.3.1 Formulation of entropy . . . . . . . . . . . . . . . . . . . . . . . 107
5.3.2 Interpretations of this formulation . . . . . . . . . . . . . . . . . 109
5.3.3 Application of entropy on a simple network . . . . . . . . . . . . 109
5.3.3.1 Flat organization scheme . . . . . . . . . . . . . . . . . 110
5.3.3.2 LMST self-organization scheme . . . . . . . . . . . . . . 110
5.4 Entropy Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
5.5 Entropy Variation Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 115
5.5.1 When single node disappears . . . . . . . . . . . . . . . . . . . . 115
5.5.2 When several nodes disappear . . . . . . . . . . . . . . . . . . . 117
5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
6 Test-bed 123
6.1 Description of Test-bed . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
6.1.1 Imote2 hardware platform . . . . . . . . . . . . . . . . . . . . . . 124
6.1.2 Basic communication architecture . . . . . . . . . . . . . . . . . 125
6.1.3 MAC layer based on CC2420 driver . . . . . . . . . . . . . . . . 126
6.1.4 Detailed architecture of network layer . . . . . . . . . . . . . . . 126
6.2 Implementation of FISCO . . . . . . . . . . . . . . . . . . . . . . . . . . 128
6.2.1 FISCO FSM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
6.2.2 FISCO message format . . . . . . . . . . . . . . . . . . . . . . . 129
6.3 Methodology of Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
6.3.1 Funtional test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
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6.3.2 Performance test . . . . . . . . . . . . . . . . . . . . . . . . . . . 1316.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
6.4.1 First node configuration . . . . . . . . . . . . . . . . . . . . . . . 1326.4.2 Configure to member . . . . . . . . . . . . . . . . . . . . . . . . . 1336.4.3 Configure to leader . . . . . . . . . . . . . . . . . . . . . . . . . . 133
6.5 Conclusions and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . 134
7 Conclusions and Persperctives 137
7.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1377.1.1 Autonomous architecture . . . . . . . . . . . . . . . . . . . . . . 1377.1.2 Self-organized mechanisms . . . . . . . . . . . . . . . . . . . . . . 1387.1.3 Framework solution . . . . . . . . . . . . . . . . . . . . . . . . . 1397.1.4 Entropy for quantifying organization . . . . . . . . . . . . . . . . 1397.1.5 Experimental Imote2 test-bed . . . . . . . . . . . . . . . . . . . . 139
7.2 Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1407.2.1 Enrich the functionalities of the architecture . . . . . . . . . . . 1407.2.2 Security aspects of the solutions . . . . . . . . . . . . . . . . . . 1417.2.3 Scaling laws . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1417.2.4 Application tunable parameters . . . . . . . . . . . . . . . . . . . 142
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List of Figures xi
List of Figures
1.1 General architecture of a sensor node . . . . . . . . . . . . . . . . . . . . . 21.2 Imote2 node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 DHCP server-client configuration . . . . . . . . . . . . . . . . . . . . . . . . 152.2 IPv6 stateless address autoconfiguration . . . . . . . . . . . . . . . . . . . . 162.3 Segmented address pools in Buddy [1] . . . . . . . . . . . . . . . . . . . . . 182.4 MANETConf [2] configuration process . . . . . . . . . . . . . . . . . . . . . 192.5 Prophet [3] address allocation . . . . . . . . . . . . . . . . . . . . . . . . . 202.6 A Unit Disk Graph of 120 nodes and radius=0.16 . . . . . . . . . . . . . . . 222.7 2.5-hop and 3-hop coverage set . . . . . . . . . . . . . . . . . . . . . . . . . 242.8 Example of CDS construction in [4] and [5] . . . . . . . . . . . . . . . . . . 262.9 RNG: pruning the longest edge . . . . . . . . . . . . . . . . . . . . . . . . . 282.10 Snapshots of self-organization structures: a network with 120 nodes, radius=0.16 30
3.1 FISCO structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.2 LDBR message format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.3 Address space management in FISCO . . . . . . . . . . . . . . . . . . . . . 373.4 New node configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.5 One-hop address allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . 403.6 Two-hop address allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . 413.7 The departure of a gateway . . . . . . . . . . . . . . . . . . . . . . . . . . 423.8 The departure of a leader . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.9 Local re-organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483.10 Mesh backbone with different values of p . . . . . . . . . . . . . . . . . . . . 493.11 A topology with multiple gateways when p = 1 . . . . . . . . . . . . . . . . 503.12 FISCO mesh analysis with radius=0.18 . . . . . . . . . . . . . . . . . . . . 513.13 The number of nodes in S within node u’s neighborhood . . . . . . . . . . . 523.14 Motif: hexagon, octagon, dodecagon . . . . . . . . . . . . . . . . . . . . . . 533.15 Paving the space with hexagons . . . . . . . . . . . . . . . . . . . . . . . . 543.16 Configuration message overhead, radius=0.20 . . . . . . . . . . . . . . . . . 583.17 Configuration latency, radius=0.20 . . . . . . . . . . . . . . . . . . . . . . . 593.18 Evolution of the number of partitions from 1 to 400 nodes . . . . . . . . . . . 603.19 Energy saving in configuration, radius=0.20 . . . . . . . . . . . . . . . . . . 613.20 Cardinality of dominating set, radius=0.20 . . . . . . . . . . . . . . . . . . . 613.21 Properties of FISCO local structures, radius=0.14 . . . . . . . . . . . . . . . 623.22 FISCO sent and received control message cost, radius=0.14 . . . . . . . . . . 633.23 CDS and FISCO control message cost, radius=0.20 . . . . . . . . . . . . . . 64
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List of Figures
3.24 Energy saving in organization, radius=0.20 . . . . . . . . . . . . . . . . . . 643.25 Impact of re-organization in FISCO on network lifetime, radius=0.20 . . . . . 653.26 Impact of mesh organization on energy saving, radius=0.20 . . . . . . . . . . 66
4.1 Communication in WSN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704.2 Data dissemination: data centric routing and rendezvous systems . . . . . . . 744.3 Directed query forwarding structure on FISCO mesh backbone . . . . . . . . 754.4 Data forwarding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764.5 Support for sink mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784.6 Analysis of communication overhead (average number of queries per sink q=50,
average number of events per source node e=500, N=10000, r=0.1, α=0.3 for
TTDD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 804.7 Analysis of communication overhead (m=5 mobile sinks, n=10 source nodes,
N=10000, r=0.1, α=0.3 for TTDD) . . . . . . . . . . . . . . . . . . . . . . 814.8 Block diagram of A-ARMA. . . . . . . . . . . . . . . . . . . . . . . . . . . 874.9 Applying ARMA and A-ARMA models on indoor temperatures. . . . . . . . 894.10 Accuracy and efficiency of the A-ARMA(2,2) on 720 samples. . . . . . . . . . 904.11 Accuracy and efficiency under independent errors . . . . . . . . . . . . . . . 924.12 Accuracy and efficiency under consecutive errors . . . . . . . . . . . . . . . . 934.13 Number of messages in data collection . . . . . . . . . . . . . . . . . . . . . 974.14 Average active time of sensor nodes in data collection (ideal MAC scheduling) 984.15 Average active time of sensor nodes in data collection (BMAC) . . . . . . . . 994.16 Factor of active time between BMAC and ideal MAC scheduling . . . . . . . 1004.17 Distribution of active modes among total active time . . . . . . . . . . . . . 1014.18 Average energy consumption on nodes . . . . . . . . . . . . . . . . . . . . . 102
5.1 Flat organization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1105.2 LMST organization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1115.3 Entropy value of a topology of 4 nodes . . . . . . . . . . . . . . . . . . . . . 1115.4 Average entropy value on networks of 200 nodes, radius=0.16 . . . . . . . . . 1135.5 Average broadcast cost on networks of 200 nodes, radius=0.16 . . . . . . . . 1145.6 Probability Density Function of entropy variation for simple node disappearance 1165.7 Entropy variation of self-organization schemes for random geometric network,
radius=0.16 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1175.8 Entropy variation of self-organization schemes under different densities . . . . 119
6.1 Basic communication architecture . . . . . . . . . . . . . . . . . . . . . . . 1256.2 Modules description in network layer . . . . . . . . . . . . . . . . . . . . . . 1276.3 Finite state machine of FISCO configuration . . . . . . . . . . . . . . . . . . 1286.4 Message header of FISCO . . . . . . . . . . . . . . . . . . . . . . . . . . . 1296.5 Setup for 30 Imotes test-bed platform . . . . . . . . . . . . . . . . . . . . . 131
xii
-
Introduction 1
Introduction 1In the past decade, wireless technologies have become key technologies, offering mo-
bile and flexible communications for industries, enterprises and individuals. The GSM
and CDMA wireless mobile networks are reaching the leadership as daily voice com-
munications media, surpassing the wired telephone network. They are evolving from
2nd generation to 3rd generation, with larger bandwidth, higher data transmission rate
and better support of data and video transmissions. On the other side, the widely
deployed wireless LANs with WiFi technology have also been great successes. The
standard IEEE 802.11a/b/g [6] provides a bandwidth up to 54Mbps. This technology
enables Internet connections everywhere as long as one has a laptop or a PDA. Around
each person, Wireless Personal Area Networks (Wireless PANs) based on Bluetooth
[7] technology, begin to appear which offer the possibility to connect different devices
such as wireless handsets, PDAs and game controllers together to enrich the interac-
tions between humans and environment. Another wireless technology Radio Frequency
Identification (RFID) [8], standing for clear and contact-less identification of objects,
enables rapid and automatic data acquisition via radio waves. It is changing the way
of organization in retail, transportation and logistic.
Beside all these wireless technologies, the Wireless Sensor Networks (WSNs) [9, 10]
became popular in the past five years. More and more sensing applications take advan-
tages of WSNs based on the collaborative efforts of a large number of sensor nodes. In
this work, we place our focus on this new type of wireless networks. Particularly, we
try to find autonomous solutions for networking and data communication in WSNs.
In this chapter, we first discuss the popularity of wireless sensor networks from three
principal angles: the designs of advanced sensor nodes, the ease of deployment and
the versatile WSN applications. Secondly, the motivations of this work are stated as
providing an autonomous architecture for WSN. This architecture’s objectives are to set
up, organize, operate and manage the WSN as well as to support various applications
and services.
-
Introduction
Sensor Broad
RAM Flash
Infrared sensor
Humidity sensor
Temperature sensor
Radio
Micro−processor
Power
Supply
Con−
nector
A/D Converter
Sensor
Con−nector
Antenna
Communication Broad
Power Supply Broad
Figure 1.1: General architecture of a sensor node
1.1 Popularity of Wireless Sensor Networks
Wireless Sensor Network becomes one of the hottest topics in both research and com-
mercial fields. How can we explain its popularity?
1.1.1 Advanced sensor nodes
The individual processing, storage and communication capabilities of sensor nodes are
relatively limited when comparing to personal computers or devices. However each of
them uses low cost components and targets for low power operation. The size of a
sensor node, as an individual communication unit, has also been dramatically reduced
even comparing to the most up-to-date personal devices. Small size, low cost and low
power operation make wireless sensor nodes significantly different from other wireless
devices.
We distinguish a sensor node from a sensor. A sensor node has capability to sense
(taking measure from physical environment), to communicate (to other sensor nodes)
and it has a power supply module (see Fig. 1.1). The sensing part is generally called
a sensor (or a sensor board) which is a transduction device that measures some phys-
ical quantities and converts them to electrical quantities. The sensor is connected to
a communication board, so called the main board, via analog or digital Iuput/Output
(I/O) interfaces. With the increase of versatile wireless sensor network applications,
2
-
Popularity of Wireless Sensor Networks
many advanced small powerful main boards such as Mica2/MicaZ [11], µAMPS [12]
and Imote2 (Fig. 1.2(a) and 1.2(b)), have been developed to support in-network data
processing and reporting of measurements. Table 1.1 gives the detailed hardware in-
formation of these advanced sensor nodes. Besides, various modules have also been
designed to supply energy to sensor nodes’ communication boards and sensor boards.
Traditional batteries, solar chargers [13] and energy extracted from vibration [14] have
been used as the possible ways of power supply.
Table 1.1: Detailed specifications of MICA2/MicaZ, µAMPS and Imote2
Components Mica2/MicaZ µAMPS Imote2
Processor ATmega128L SA-1110 Intel PXA271Processor speed 7.3728 MHz 206 MHz 13-416 MHzSRAM 4 KBytes 16 KBytes 256 KBytesProgram Memory 128 KBytes 128 KBytes 32 MBytesData storage 512 KBytes 512 KBytes 32 MBytesSerial Interface UART GPIO UART&GPIO&mini-UOther Interfaces DIO, I2C, SPI No SPI I2C, I2SBattery 2x AA 4x AAA 3x AAAExternal Power 2.7 - 3.3 V 3.6 V 3.2 - 4.5 VExpansion Connector 51 pin 256 pin JTAGRadio CC1000[15]/CC2420 Bluetooth[7] CC2420 [16]Software TinyOs µOS TinyOS, Linux[17]
It is worth noting that the research interests are also multiplied with the develop-
ment of event-driven embedded operating system such as TinyOS [18]. TinyOS is an
event-based operating environment/framework designed for using with embedded net-
worked sensors, to support intensive concurrent operations needed by sensor network
applications. It features a component-based architecture which enables rapid innova-
tion and implementation while minimizing code size as required by the severe memory
constraints inherent in sensor networks.
The design of advanced sensor nodes can be summarized as to target the following
three purposes:
1. Low-energy operations. In the most cases, sensor nodes are battery supplied.
Regarding to the variety of deployment terrains, it may be unfeasible to renew
their energy resources. Therefore low power processing and communication are
required to prolong the lifetime of WSNs. In the earlier design of sensor nodes,
low-frequency micro-controller is used (in Mica2/MicaZ series) to ensure low con-
sumption during the processing. In order to increase the processing capability,
3
-
Introduction
(a) Imote2 hardware components (b) Imote2 architecture
Figure 1.2: Imote2 node
more powerful CPU have been used lately in Imote2. Thanks to the low-frequency
mode of these CPUs, moderate power operation is also possible. However there
is always a trade-off between the processing capability and energy saving. Radio
communication is another source of energy dissipation, even much more costly
than processing operations. A comparison between computation and communi-
cation cost [19] reveals that 3000 instructions can be executed for the same cost
as the transmission of one bit over 100m. Using low power radio transceivers such
as IEEE 802.15.4 [20] compatible radio module becomes the trend.
2. Size and cost constraints. In order to benefit from collaborative operations of
sensor nodes, a wireless sensor network is formed by hundreds of sensor nodes.
Therefore sensor nodes should have small size, light weight and low cost to support
large scale deployments. Mica2/MicaZ, µAMPS and Imote2 have shown the pos-
sibility of embedding processing, communication and sensing on small platforms.
However the cost of sensor nodes is still too high nowadays.
3. Support of operating system. By running an operating system on small sensor
nodes, both the communication protocols and applications can be developed with
more flexibilities.
1.1.2 Ease of deployment
The needs for large scale sensor network and for intensive collaborative operations
and computations change the way of deploying sensors. Sensor nodes are required
to form multi-hop networks to communicate among themselves. It is a significant
4
-
Popularity of Wireless Sensor Networks
improvement over traditional sensors in the way that nodes can be randomly deployed
in inaccessible terrains in case of disaster relief operation. In addition to the advances
in sensor nodes hardware architectures, the power of wireless sensor networks also lies
on the ability to deploy large number of nodes which configure and organize themselves.
Through advanced distributed networking protocols, the sensor nodes form a virtual
environment that extends the reach of cyberspace out into the physical world. Although
the capability of any single sensor node is minimal, the composition of hundreds and
thousands of nodes offers radical new technological possibilities.
Unlike traditional wireless devices such as cell phones and PDAs, wireless sensor
nodes do not need to communicate directly with the nearest base station, but only
with nodes within its local area. Instead of relying on a pre-deployed infrastructure,
each individual sensor node becomes a part of the overall infrastructure. Therefore
a wireless sensor network should be able to configure sensor nodes under any possible
node placement upon the deployment. Nevertheless real systems must place constraints
on actual node placement; for example the physical topology should be connected to
ensure the connectivity on network level. The wireless sensor network must be capable
of providing feedback when these constraints are violated.
In addition to an initial configuration phase, a WSN also has the capability to adapt
to changing environmental conditions. The envisioned flexible self-organizing network
architectures dynamically adapt themselves to support arrival of new nodes or to ex-
pand to cover a larger geographic region. Furthermore, the network can automatically
adapt to compensate for node failures. Throughout the lifetime of a deployment, nodes
may also be relocated or other objects may appear in the radio environment which
might interfere with the communication in the WSN. The network should be able to
automatically reconfigure in order to tolerate these occurrences.
1.1.3 Versatile applications
The raise of its versatile applications is one of the propelling forces for researches in
WSNs. Wireless sensor network applications cover a large range of applications from
our daily life acme military usage. Agriculture, environment monitoring, health care,
structure monitoring, intrusion detection, traffic monitoring and industries have made
WSNs increasingly popular [21, 22, 23]. We envision that, in the near future, wireless
sensor networks will be an integral part of our lives, more than the present-day personal
computers.
The common characteristics of wireless sensor applications are: large number of
nodes, long lifetime and fault tolerance.
5
-
Introduction
1. The use of large number of sensor nodes increases the accuracy of the application
and reduce the cost of prior study of the node placements. It also requires that
networking protocols efficiently reduce the communication overhead and provide
a virtual coordination for data report.
2. Running for a long lifetime is required to minimize the human interventions from
replacing sensor nodes, while ensuring the profitability of such deployed sensor
network services and applications. Long lifetime obviously implies a low energy
consumption of sensor nodes during the operation of the network.
3. As the network ages, it is expected that nodes will fail over time. A large number
of sensor nodes are deployed with redundancy, hence the failure of a fraction of the
sensor nodes should not Hamper the operation of the application. Nevertheless
the network should be capable of reconfiguring its nodes to handle node/link
failure or to redistribute network traffic load.
1.2 Description of WSN in Our Consideration
Now that we have established the sensor nodes’ capabilities, constraints and the set of
applications, we eventually consider a WSN as a network having following characteris-
tics:
1. A WSN is formed by small identical sensor nodes. Each sensor node has the
same limited processing, storage and communication capabilities. A sensor node
is supplied by limited energy resource on board.
2. A WSN requires an easy deployment of sensor nodes in the field. Sensor nodes
must configure themselves. An untrained person should be able to place sensor
nodes throughout the environment and have the system simply work.
3. No predefined communication infrastructure should be given when the network
is set up. After the deployment, the sensor nodes should keep organizing among
themselves to deal with the changes, so as to provide a flexible communication
structure.
4. Long lifetime is critical to many sensor network applications. In some applica-
tions (such as environment monitoring, military and tracking applications), the
goal is to have nodes placed out in the field, unattended, for months or years.
Long lifetime also leads to much more convenient usage of WSNs in home and
6
-
Motivations
health application. The use of low-power electronic components is one way to
provide energy saving. Moreover, the networking protocol used in WSNs should
be energy efficient by keeping the nodes’ activity to a minimum (communication
and computation).
1.3 Motivations
The popularity of wireless sensor networks also promotes a range of research topics,
particularly around providing energy-optimized communication. A wireless sensor net-
work is deployed to collect and report data while reducing human intervention and
labor. The following scenario is an example of desired implementation and operation
of a WSN.
One may buy a box of sensor nodes from a store. These sensor nodes have been
mass-produced and sold at low price. When the user gets them, they are identical
but may be connected to various sensor boards or controllers. Once at home, the user
places nodes around his house: on the ceiling for monitoring the lights, at every corner
to monitoring the temperature or connected to air-conditioning and light switches for
controlling. Once the nodes are deployed, he simply wants the network work! He
wants to get a comfortable living space without configuring nodes one by one to set up
addresses, data paths and communication structures!
This work aims at providing an autonomous network architecture for WSNs to
minimize the user’s tasks. The architecture makes the WSN run as an autonomous
system where sensor nodes organize themselves without external configurations and
interventions.
This architecture is built and maintained by a set of mechanisms executed by all
sensor nodes. It is the unique architecture upon the deployment of a wireless sensor
network. It takes into account the constraints of sensor nodes as well as the requirement
of sensor applications to set up, organize, manage and maintain the autonomous archi-
tecture in WSNs. This work also addresses how to simplify the multiple development
of networking protocols within the architecture.
The motivations for such architecture are stated as follows along with the operation
of WSNs.
1.3.1 Setting up and organizing
Each sensor node should be configured to set up a network, instead of working indepen-
dently. Such configuration should be achieved by sensor nodes themselves, in order to
7
-
Introduction
save on human interventions. The corresponding mechanism is called self-configuration.
Self-configuration allows sensor nodes to set up their addresses and parameters for data
communications in WSNs. Although it has been addressed as Autoconfiguration prob-
lem [1, 3, 24] in the field of Mobile Ad hoc Network (MANET) [25], the context of
WSNs is different. Unlike a wireless LAN card containing a unique MAC address, sen-
sor nodes are not necessarily manufactured with unique identifiers, because they are
produced in mass quantities at low cost. Self-configuration scheme is the only way
that a sensor node may get a unique address without any pre-defined identifier, man-
ually configuration or centralized servers. A sensor node can communicate with other
nodes only if it can be identified. Therefore self-configuration is one of the preliminary
schemes for setting up communications in a wireless sensor network.
After the self-configuration, the communication between two nodes which share a
wireless channel can be established. However, in our point of view, multi-hop commu-
nication is still impossible, unless a communication structure is built. Self-organization
is the mechanism which aims at providing such a structure, more precisely a logic struc-
ture on the top of physical network topology. All sensor nodes naturally participate in
the overall structure. They collect information via the structure and they make local
decisions to change it at the same time. Self-organization is an autonomous process, in
which decisions of organization is not guided or managed by centralized elements. The
structure of the self-organization is formed by all local structures around each node. It
is a result of information exchanges and local decisions taken by sensor nodes.
1.3.2 Managing and maintaining
Throughout the life of a wireless sensor network, sensor nodes may fail or be relocated.
It is also possible that new sensor nodes are introduced to cover a larger service re-
gion. The autonomous architecture also has the role of compensating the influences
of these events in the network. A new node should still be configured through the
self-configuration mechanism and be integrated to the communication structure by
self-organization. Besides, the failures of sensor nodes also bring about changes in
the communication structure. Hence, the initial deployment and configuration is only
the first step in the network lifetime. In the long term, the total cost (in terms of
message, computation and energy) may have more to do with the maintenance cost.
Self-configuration and self-organization that are used in the network set up, are con-
tinuously running as self-maintenance mechanisms in order to deal with spontaneous
changes in the network.
As the network ages, the residual energy on board the sensor nodes decrease as well.
8
-
Motivations
Optimizing the energy consumption during the WSN’s life is another goal of managing
and maintaining the network. In practice, some nodes may spend more energy than
others, because of their particular roles in the network. The network management
should also spread the energy cost over all sensor nodes.
1.3.3 Support of applications and services
One purpose of this autonomous architecture is to provide a general platform for easy
implementation of applications and services. The autonomous architecture makes the
physical changes transparent to the upper applications and services. Several proper-
ties are conserved along with the network operation. One may take advantages of
these properties to improve the effectiveness and efficiency of running applications and
services.
Data dissemination and data aggregation are considered in this work as two examples
of basic services in wireless sensor networks. How to provide efficient data dissemination
and data aggregation are the core problems of many applications such as environment
monitoring and tracking applications. Both of them are supported by the autonomous
architecture that we proposed for WSNs. The additional cost of running data dissemi-
nation and data aggregation is dramatically reduced, and the overall message cost and
energy consumption are lower as well. The results (see in chapter 4) confirm the positive
impacts on the applications and services of using an autonomous network architecture.
1.3.4 Energy efficiency
It is worth noting that such an autonomous architecture should achieve energy efficiency
during its generation, maintenance as well as lifelong operation. As explained in section
1.1.1, the wireless sensor nodes are extremely energy constrained due to their small
size and low cost. Whilst the majority of WSNs applications targets long lifetime, the
utilization of the autonomous architecture should optimize the communications to meet
high energy efficiency. Furthermore, the additional energy consumption generated from
the executions of the autonomous architecture should also be minimized.
Unfortunately, a number of networking mechanisms proposed for WSNs still adopt
energy-hungry techniques. The utilization of HELLO message is the most popular tech-
nique for topology control and organization in WSNs, while it generates a significant
control overhead and is very energy consuming.
The autonomous architecture that we propose in this work as well as all of the asso-
ciated mechanisms aim at providing energy savings during the entire life of WSNs. The
9
-
Introduction
energy efficiency are particularly considered and analyzed as one of the most important
performance metrics.
To summarize, the motivations of this work is to provide efficient and low-cost mech-
anisms from the deployment to the effective execution of services and application in
WSNs.
1.4 Organization of the Thesis
This thesis is organized in 7 chapters. Chapter 2 presents some prior contributions to
the networking of wireless sensor network. We point out that self-configuration and
self-organization are two key mechanisms for WSNs. The aim of self-configuration is to
let nodes set up addresses and parameters without any manual intervention (minimize
user’s tasks), so that nodes may identify each other in a communication. There exist
two class of self-configuration schemes: Duplicated Address Detection (DAD) based
and Distributed Dynamic Host Configuration Protocol (DDHCP) based. Several works
are reviewed. It is shown through discussions that few of them conform with the energy
constraints of WSN. Self-organization aims at structuring the network (i.e. provide a
communication structure) through merely local information and decisions. Clustering,
virtual backbone based and source dependent self-organization schemes are discussed.
Once again the energy efficiency is the most critical problem to these schemes. By
analyzing and identifying the shortcomings in the existing self-configuration and self-
organization solutions, we point out the necessity of our autonomous architecture.
We propose, in chapter 3, an autonomous architecture which combines self-configuration
and self-organization, named FISCO (Fully Integrated Scheme of self-Configuration and
self-Organization). This autonomous architecture is motivated by improving efficiency
of the network formation and organization. Our point of view is that self-configuration
and self-organization are two relevant schemes for wireless sensor networks. A unique
structure is generated and maintained with the autonomous architecture for both
conflict-free address allocation and data communication. The autonomous architecture
is built with event-driven procedures, while the use of periodical actions are optimized
in order to avoid control overhead and provide energy saving during its operation. Our
architecture integrating two mechanisms exhibits significant improvements comparing
to existing solutions, not only in the energy efficiency but also in other properties such
as the cardinality of backbone, etc. The performance results also indicate the reduction
on total energy consumption and the extension on network lifespan.
In chapter 4, we discuss the usage of FISCO architecture, by exploring data dis-
10
-
Organization of the Thesis
semination and data aggregation. One of the goals of running FISCO structure is to
provide further improvement in intensive data communication, such as easing path set
up between source nodes and sink nodes or providing energy saving. We show in this
chapter that the use of FISCO architecture may provide an flexible and low-cost data
dissemination structure. It also supports data aggregation techniques to provide sig-
nificant energy saving during data reporting. Through a set of simulations, the impact
of FISCO are clearly highlighted, by comparing the cost and the result of running
data dissemination and data aggregation schemes with and without the autonomous
architecture. We also propose a localized temporal data aggregation technique based
on Adaptive AutoRegression Moving Average (A-ARMA) technique. It is shown that
using an adaptive technique such as A-ARMA is better than non-adaptive methods,
because it achieves low complexity, high accuracy and good efficiency at the same time.
Chapter 5 addresses a fundamental question in wireless sensor networks, more gener-
ally wireless multi-hop network: how to define a good organization? Although metrics
such as complexity or self-stability are commonly used for evaluation, to the best of
our knowledge, none of them quantifies the efficiency of building and maintaining an
organization (order) under connection changes. In this chapter, the notion of entropy
is adopted as a metric for evaluating the organization of a network where different
self-organization schemes are used. This approach provides several quantitative and
qualitative insights into the behavior and design of self-organization protocols. For
example, any of the chosen protocols yields a higher organizational state than a flat
topology.
Chapter 6 presents our work on building a test-bed based on 30 Imote2 sensor nodes in
FranceTelecom R&D center in Beijing. We designed and implemented a protocol stack
based on an embedded Linux OS for wireless sensor nodes. A partial implementation
of FISCO scheme is available on this test-bed. Through a set of functional tests, the
correctness of the FISCO implementation is validated. The Imote2 test-bed shows that
it is possible, with commercial sensor nodes, to validate the autonomous architecture
proposed in this thesis. It is also a step from the design to the practice. This test-bed
will be used in FranceTelecom R&D center as an extensible hardware platform with
stable development environment for further demonstrations of WSN.
Chapter 7 summarizes the thesis and gives a prediction of future technological trends.
The major contribution of this thesis is that we designed a complete solution for net-
working in WSNs which covers deployment, configuration, organization and data com-
munications. This work also leads to several perspectives which are not limited to the
functionalities of the architecture, but also extended to security and applications. This
11
-
Introduction
thesis was financed and supported by France Telecom R&D under CRE No 46130157.
It is realized at CITI Laboratory of INSA Lyon, in the ARES Team of INRIA Rhône-
Alpes, under the direction of Doctor Fabrice Valois and Professor Eric Fleury.
12
-
State of the Art 13
State of the Art 22.1 Introduction
As addressed in the introduction (section 1.3), the quality of service provided by a
wireless sensor network relies on an autonomous network architecture. It is not a single
scheme, but the combination of mechanisms which support the operation of a WSN
from its deployment onward, including self-configuration, self-organization, self-healing
and self-management.
Self-configuration [26, 27] is the first action to take when sensor nodes are deployed.
The aim of self-configuration is to let nodes set up addresses and parameters without
any manual intervention. Address allocation is known as the core problem of self-
configuration. The main issue of address allocation in multi-hop wireless network with-
out a pre-defined infrastructure (including both ad hoc networks and wireless sensor
networks) is how to ensure the uniqueness of allocated addresses in the entire network.
Solutions using centralized address servers are not optimized to work for large scale
sensor networks. The use of server-client approach even becomes a bottle-neck of con-
figuration performance. Indeed, the self-configuration should achieve a distributed and
dynamic address allocation in WSNs. It should deal with random deployment of spo-
radic node arrival by an immediate join procedure. Furthermore, it should handle the
problem related to address conflicts during partition splitting and partition merging in
the network.
Self-organization [28, 29] in our point of view is defined as a process aiming at struc-
turing the network through purely local information and decisions. The logic structure
of the network is represented by relations between neighboring nodes (as logic links)
and node roles in the network. Using self-organization instead of a pre-defined structure
makes the communication structure scalable, flexible and adaptive. According to local
information and localized algorithm, nodes form local structures. The self-organization
structure is built based on these local structures. It reflects the emerging behavior in
-
State of the Art
the network from pure local decisions.
Self-healing [30, 31] aims at adapting the network to keep certain properties that
have been established. It is highly related to the notion of stability, because it runs for
maintaining the structure built by self-organization facing spontaneous changes such
as node failures, relocations, etc. Self-healing also adjusts the structure of organization
partially or entirely on-the-fly to optimize the real time performance such as energy
consumption. Hence, we consider self-healing as a part of self-organization.
Self-management [32, 33] deals with the control of the services based on the au-
tonomous architecture. It benefits from the autonomous architecture to improve the
quality of service in the network. Different from other mechanisms, it also involves
application configurations and parameters. As we target an autonomous network ar-
chitecture, the self-management is beyond the scope of this work. Nevertheless it is
discussed as one of our perspectives in section 7.2.
The objective of this chapter is to review the existing works before proposing an
autonomous network architecture, while self-configuration and self-organization are two
indispensable mechanisms for this end. Therefore this chapter is organized as follows:
Section 2.2 reviews prior contributions to self-configuration. The majority of these
works is in the context of ad hoc networks, where self-configuration aims at providing
dynamic address allocation solutions in a network. Section 2.3 reviews the major works
in self-organization in ad hoc and wireless sensor networks. Clustering based, virtual
backbone based and source dependent self-organizations are considered. Based on the
resulting structure, virtual backbone based self-organizations are further classified into
Maximal Independent Set (MIS), Connected Dominating Set (CDS), Local Minimum
Spanning Tree (LMST), Relative Neighboring Graph (RNG). It is after identifying the
concepts and weaknesses in these solutions that we propose an autonomous architecture
in the next chapter.
2.2 Self-configuration
It is worth noting that some WSN applications do not require nodes to be identified by
logic addresses such as IP addresses. In such applications, coordinates [34] or contents
[35] are used for data reporting. However the routes can not be recorded or reused if
nodes can not be identified. Therefore assigning addresses to sensor nodes facilitates the
data communication in WSN. Moreover, for most of WSN applications, the addressing
is indispensable.
Although the majority of self-configuration schemes reviewed in this section were
14
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Self-configuration
Client
DHCP_ACK
DHCP_REQUEST
DHCP_OFFER
DHCP_DISCOVER
Server
Figure 2.1: DHCP server-client configuration
proposed for ad hoc networks, it is worth noting that self-configuration is not a new
notion for IP networks, especially known as autoconfiguration in IPv6 networks. Before
embarking on to the self-configuration schemes in ad hoc network, let us first take a
glance at the mechanisms used in IP networks in order to demonstrate that they can
not be directly applied in our context.
2.2.1 Autoconfiguration in IP networks
Already available for IPv4, Dynamic Host Configuration Protocol (DHCP) [36] is the
most used autoconfiguration protocol for address allocation in wired networks and AP-
based wireless networks. It is built on a server-client model, where designated DHCP
servers allocate IP addresses and deliver configuration parameters to hosts. Client-
server exchanges in DHCP involve four messages (as shown in Fig. 2.1). DHCP is also
known as a stateful autoconfiguration protocol because all the address allocation states
are stored (in DHCP servers) during the configuration.
IPv6 [37], Internet Protocol version 6, is designed as an evolutionary step from IPv4
with some main changes such as: expanded addressing capability, improved support
for extensions and options, extensions for authentication and privacy, etc. As one
of the improvements, IPv6 integrates a stateless autoconfiguration [38] for traditional
hierarchical networks to deal with those networks without DHCP servers.
In a IPv6 stateless autoconfiguration, the following steps are performed by a host
once it joins the network on a physical link (see Fig. 2.2):
1. The host generates locally a link-local address that is based on the interface
identifier (IEEE 64-bits Extended Universal Identifier) and a pre-defined link-
local prefix (FF02::1/64). The state of this address is set to Provisioning.
2. A Duplicated Address Detection (DAD) procedure is launched by the host to
have the uniqueness of its provisioning address verified. A Neighbor Solicitation
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State of the Art
Provising link−local @
Yes RA
No RARA
RS
No NA
NA Yes NA
NS
Working with link−local @Obtain prefix
Link−local @
Figure 2.2: IPv6 stateless address autoconfiguration
(NS) message is sent out with the provisioning address as the target address. The
source address is the all-zero address; the destination IP address is the solicited-
node multicast address. If the address is already in use by another host in the
network, then it replies with a Neighbor Advertisement (NA) message. An address
conflict is henceforth recognized. Either a manual configuration or a regeneration
of link-local address is required. If there is no answer to the NS within the timeout,
the address is assigned to the interface and the state of the address changes to
Preferred.
3. The host sends a Router Solicitation (RS) in order to determine the global prefix.
The RS message is sent to the all-routers multicast group of FF02::2.
4. If there is a router on the link, then it replies with a Router Advertisement (RA).
Because the uniqueness of EUI-64 has already been verified in DAD with link-
local interface, there is no need to repeat it for the global address. The host
simply combines the prefix with the interface identifier. If no RA is received in
timeout, the host keeps communicating with its link-local address.
Although DHCP and IPv6 stateless autoconfiguration are widely used as the standard
address configuration techniques in the networks which have pre-defined infrastructures,
neither of them is applicable in ad hoc and wireless sensor networks. The major problem
comes from the scalability. In ad hoc or wireless sensor networks, nodes do not reside
on the same physical links. The discoveries of DHCP servers as well as duplicated
addresses are not time bounded. They are achieved through a flooding because there
is no pre-defined structure in the network. It is not only message costly but also time
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Self-configuration
consuming. To reduce the configuration cost and duration, self-configuration has been
investigated as a particularly important problem in ad hoc and wireless sensor networks
where two approaches have been proposed.
2.2.2 Address conflict detection based solutions
The first approach relies on DAD [24] mechanism. If the network diameter is known in
advance, then the duplicate address can be detected within a timeout. This is known
as Strong DAD. However when the network diameter is unknown, which is actually the
case in ad hoc and wireless sensor networks, the DAD can not be achieved in a given
timeout. Hence Weak [39] and Passive DAD [40] mechanisms are proposed to resolve
this issue of Strong DAD. Contrary to Strong DAD, they only prevent a packet from
being routed to a wrong destination.
Weak DAD [39] tolerates duplicate addresses in the network after the local generation
of addresses as long as all packets are delivered correctly. A unique per-node key is
assumed to be included in the routing control packets and the routing table entries,
but is not embedded in IP address. If two nodes happen to have chosen the same
IP address, then they can still be identified by their keys. It is worth noting that a
new implementation of Weak DAD is required every time a different MANET routing
protocol is used (eg. its implementation over link state routing is different from that
over dynamic source routing). This increases the development cost of Weak DAD.
Passive DAD [40] is similar to Weak DAD. Address conflicts are detected passively
by nodes using continuous monitoring on routing control traffic. It was initially built
for link state routing protocols, and then extended to work with reactive routing pro-
tocols as well. A node analyzes incoming protocol packets, including date packets and
control packets, to derive hints about address conflicts. The basic idea is to apply
various Passive DAD algorithms to exploit protocol events that 1) never occur in case
of a unique address, but always in case of duplicate addresses or; 2) rarely occur in
case of a unique address, but often in case of duplicate addresses. In the first case, de-
tection is certain while in the second case a conflict is probably occurring. In order to
have a certain confidence on the conflict detection, long-term monitoring is necessary.
Although this approach does not incur extra overhead in the network, it heavily relies
on the underlying routing protocol. Furthermore, the correctness and effectiveness are
strongly affected by the particular parameter settings of the routing protocol.
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A
DB
B
80 − 12764 − 79
0 − 127
64 − 1270 − 63A
Address
Figure 2.3: Segmented address pools in Buddy [1]
2.2.3 Distributed DHCP solutions
The second approach of self-configuration is to build a distributed version of DHCP
in ad hoc and wireless sensor networks. The idea is to assign the DHCP functionality
to every node. Each node has capability to allocate addresses to other nodes on the
same wireless link. The allocation is hence limited to one-hop. In order to avoid address
duplication in the entire network, nodes should exchange address allocation information
and maintain this knowledge synchronized among them.
[1] proposes to use Buddy system (a well known method for memory management) in
address allocation. When a node join the network, it asks one of its one-hop neighbor
nodes for an address allocation after a discovery procedure. The latter node acting as
a DHCP server, named Buddy node in the proposal, responses back by giving the half
of its current address pool. The new node now gets its own DHCP address pool and
assigns itself the first address of its address pool. The address space is hence divided
into a binary tree with the arrival of nodes in the network (see Fig. 2.3). Nodes should
synchronize from time to time to keep the records of IP address assignment in the entire
network and detect any IP address leak for recover.
However the binary division of address space has several inconveniences. First, it
causes a non-uniform distribution of addresses. A massive arrival of nodes may cause
some region of the network to run out of addresses, while many addresses are not used
in another region. Secondly, the binary address tree (Fig. 2.3) reaches very quickly
its leaf level (no address is available on leaf nodes) within an exponential speed 2n.
When a node goes out of addresses, it has to go through its buddy nodes (parent nodes
in the binary address tree) until one buddy node replies to the request with available
addresses. An unpredicted amount of message overhead is generated in this address
pool searching. Furthermore, if several partitions are generated during the network
deployment, then each partition has its own binary address tree. It is difficult to merge
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Self-configuration
nor Allocate Pending tables
Neighbor discovery
Informing all MANET
Selecting an @ which
Select node A as Initiator
A B
MANET node New arrival node
Neighbor_Reply
Neighbor_Query
Requester_Request
@ + Allocated Table +Allocate Pending Table
nodes of this configuration
is neither in Allocated
Figure 2.4: MANETConf [2] configuration process
binary address trees when these partitions meet, because the address pools used in one
partition should be completely transformed to a branch of the other. It increases not
only the message overhead but also the complexity of computation. Hence it does not
meet the requirements of wireless sensor networks.
MANETconf [2] is another distributed DHCP solution for ad hoc networks. In order
to maintain the address information, every node holds an Allocated and an Allocate
Pending tables. The first one contains the set of all IP addresses in use and the second
one notes all addresses used in the procedure of allocation. When a node joins the
network, it sends a one-hop Neighbor Query message to find a configured node (see
Fig. 2.4). The first replying neighbor is considered the DHCP server for the new node.
It allocates an address which is neither in Allocated or Allocate Pending tables. This
node also informs all other nodes of this allocation via flooding. At the same time, the
Allocated and Allocate Pending tables are duplicated in the new node.
In order to deal with partition splitting, merge, initiator crash and concurrent alloca-
tion, additional mechanisms are proposed. However the flooding used for each address
allocation limits the scalability of the solution and generates a significant message over-
head.
Prophet Addressing [3] uses a function f(n) to assign no-conflict addresses to nodes.
The scheme is simple and entails low overhead as long as f(n) has good properties.
Address allocation is considered as an assignment of different numbers from an integer
range to different nodes. The first node A in the network chooses a random number
as its address and uses a random state value as the seed for its f(n) (Fig. 2.5). The
second node, say node B, joins the network as a neighbor of node A and asks node
A for an address allocation. Node A uses f(n) to compute a new address from its
address and its state value. A new state value is also generated for the next round of
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State of the Art
@_1, state_1
A
A
A
D
B
BC
f(@_2, state_2) = (@4, state_4)f(@_1, state_2) = (@3, state_3)
f(@_1, state_1) = (@2, state_2)
@_4, state_4@_2, state_4@_3, state_3@_1, state_3
@_2, state_2@_1, state_2
Figure 2.5: Prophet [3] address allocation
address generation. The new address and the new state value are sent back to B, from
which the node is able to generate addresses without conflict. The f(n) also creates an
tree-like address hierarchy in the network (Fig. 2.5).
However, the tree-like address hierarchy causes problems in partition merge. First,
nodes need send periodical HELLO message to detect address conflicts in two partitions.
Upon the address conflict detection, all nodes in one partition have to give up their
address and re-configure themselves using the state value of the other partition. This
mechanism generates significant message overhead and increases computation cost dur-
ing address re-configuration.
2.2.4 Synthesis on self-configuration
It is much more difficult to achieve self-configuration in a wireless multi-hop network,
especially in a wireless sensor network, than in traditional IP networks. Nevertheless
it is a key mechanism to WSN deployment for the following reasons:
1. Different from the production way of wired/wireless LAN card, wireless sensor
nodes are required to be manufactured in large quantities at low cost. In order to
reduce the production cost, wireless sensor nodes are note expected to have any
unique hardware addresses. Therefore it is necessary to develop address allocation
mechanisms for WSNs.
2. Because WSNs are multi-hop networks usually deployed in large scale, it is infea-
sible to implement centralized servers. Self-configuration provides dynamic and
distributed address allocations in WSNs.
3. Remember wireless sensor nodes have very limited energy resources. Hence, low
energy consumption and low message overhead are critical for self-configuration
in WSNs.
Although the existing approaches tried to adapt the techniques used in traditional
IP networks to ad hoc network and wireless sensor networks, they do not meet all the
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Self-organization
requirements, especially because of their significant control message overhead.
1. The technique of Strong DAD is applicable in an ad hoc or wireless sensor network
only if its network diameter is known in advance. Otherwise, a node is not sure
to get a duplicate detection response within a timeout. Furthermore, the DAD
messages injected during the flooding in the network is a harmful overhead for
network deployment.
2. Weak/Passive DAD is an alternative to Strong DAD, in which address conflicts are
detected by examining incoming and outgoing routing messages. This approach
does not generate additional control message only because routing messages are
used to this end. The efficiency of this technique also highly relies on the under-
lying routing protocol, and its implementations vary from one routing protocol
to another. This causes a significant development cost.
3. In distributed DHCP approach, the idea is to make every node a DHCP server in
the network. A node asks one of its neighbor nodes for an address allocation. The
core problem in this approach is how to maintain the address information updated
on all nodes in the network to keep a coherent view for next address allocation
without conflict. To this end, either periodical one-hop HELLO messages or flooding
are used to synchronize addresses in the network. However, this synchronization
on all nodes leads to significant message overhead.
2.3 Self-organization
It is worth distinguishing self-organization from the notion of self-organized. Self-
organized is a properties related to mechanisms. For instance, unicast routing in ad
hoc and wireless sensor networks should be self-organized. However, the notion of self-
organization in our consideration is a basic concept to create order and to provide a logic
structure in the network. Certainly, the mechanisms used in self-organization should
be self-organized. Nevertheless, comparing to individual self-organized protocols, self-
organization has a more general purpose: organizing and structuring the network. It is
also around this idea that we develop our autonomous network architecture. Hence, the
prior works revised in this section all aim at forming a structure in the network. They
are classified into three categories: clustering based, virtual backbone based and source
dependent structure. The virtual backbone based solutions can be further divided into
CDS, MIS, LMST and RNG according to the properties of the resulting structures.
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Figure 2.6: A Unit Disk Graph of 120 nodes and radius=0.16
The algorithms that we reviewed in this section have also been used to deal with
topology control problems in wireless ad hoc and sensor networks. Topology control
[41] aims at controlling the topology of the graph representing the communication links
between nodes, while reducing energy consumption and/or interference that are strictly
related to the nodes’ transmitting range. Both clustering and virtual backbone based
algorithm can be applied to this end. However self-organization that we focus on in
this section has a more general goal. It not only deals with individual transmission
assignment, but also takes care of an emerging behavior in the entire network.
Before embarking onto different algorithm, we shall subsequently define the formalism
of network model based on which the properties of structures are formulated.
2.3.1 Network model formalism
A wireless network is generally modeled as a graph, where nodes are represented as
vertexes and radio links as edges. If all links are symmetric, then the graph is a non-
oriented graph. If all nodes use the same radio transceiver with identical transmission
powers, then the radio vicinity of a node is a disk of ray R (normalized between 0 tp
1). In this case, the network is modeled as a Unit Disk Graph (UDG). Fig. 2.6 gives
an example of UDG with 120 nodes and radius at 0.16.
We introduce here the notations we used in this thesis:
• G = (V,E): the graph representing a wireless network, where V is the vertex setand E ⊆ V 2 the edge set.
• (u, v): the edge exists between two vertex u and v if and only if v can receive
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Self-organization
correctly the message sent by node u.
• |X|: the cardinality of a vertex set X.
• Nk(u): the neighbor set of vertex u within k-hop distance to u. By simplification,N(u) = N1(u), where N1(u) = {v|(u, v) ∈ E}).
• ∆k(u): the number of k-hop neighbors (∆k(u) = |Nk(u)|). ∆1(u) is also definedas the degree of node u.
• dist(u, v): the hop distance between u and v.
It is possible to introduce the arrival and departure of nodes as well as node mobility
in this graph model. The arrivals and departures can be modeled as random processes.
For example, we can consider the arrival as a Poisson process, while the lifetime of a
host in the network is exponentially distributed. There are also some mobility models
in the literature [42].
Although a real radio environment does not correspond exactly to the UDG model,
it shows a number of properties which simplify the analysis of geometric structure in
the network. The use of UDG model gives an idea on the bound properties of target
structures. Certainly, it is not considered as the tool which gives detailed performance
results for a solution.
2.3.2 Clustering
Cluster is a basic structure used in network organization. Each cluster is a set of nodes
which is grouped within a geographical area. A clusterhead is in charge of a set of
specific functionalities within its cluster. Organizing a wireless network into clusters
necessitates a distributed clusterhead election algorithm.
[43] is one of the fundamental works in clustering. The election of clusterhead is based
on the lowest identifier: node u is elected as a clusterhead if its identifier is the lowest
among its neighbors N(u). To this end, nodes need communicate their identifiers to
their neighbors at the beginning of the election. Non-clusterhead nodes join the cluster
whose clusterhead has the lowest identifier and become cluster members. Some of them
become gateways, if they have neighbors belonging to other clusters. The network is
partitioned into clusters where each cluster has one clusterhead and several gateways
and members. In such structure, there is no adjacent clusterheads and the clusterheads
are spaced at most by 2-hop. This solution needs synchronization for launching an
election phase. And it takes a number of iterations to finalize the election. We also
note that high message overhead is generated.
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State of the Art
3−hop coverage
w
vu Member
Gateway
Clusterhead
2.5−hop coverage
Figure 2.7: 2.5-hop and 3-hop coverage set
[44] also uses lowest identifier based clusterhead election, but extends with a localized
backbone construction to connect clusters in the network. To this end, 2.5-hop cov-
erage set of a clusterhead u is defined as a set which includes all the clusterheads in its
2-hop neighbor set N2(u) and the clusterheads having members in N2(u). It is different
from u’s 3-hop coverage set, which includes all the clusterheads in N3(u). In Fig. 2.7,
clusterhead v and w are in 3-hop coverage set of u. However, w is not in 2.5-hop cover-
age set of u, because none of w’s neighbor is in N2(u). Clusterheads collect 1-hop and
2-hop information from their neighbors to construct a 2.5-hop coverage set once they
are selected. By using a greedy algorithm, each clusterhead selects a set of gateways to
interconnect clusterheads in its 2.5-hop coverage set. A node should broadcast several
rounds of messages before it gets a stable role in the network (clusterhead, gateway or
member). This solution does not overcome the high message overhead neither.
[45] proposes to use a metric composed of residual energy and node’s degree to adjust
the backoff time before broadcasting a clusterhead decision. In this way, nodes with
more residual energy have a bigger chance to be elected as clusterheads. Low power
transmissions are used within each cluster, while clusterheads use higher transmission
power to communicate among themselves. However, the energy consumption for the
clusterhead election remains high because the maximum transmission power is used for
sending election messages. Furthermore, the election should be renewed periodically in
order to take into account the energy dissipations on nodes.
2.3.3 Virtual backbone
Here, the algorithms aim at generating a virtual backbone which covers all nodes in the
networks. Some virtual backbones represent a dominating set of the network (in case of
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Self-organization
CDS and MIS), while the others contain all nodes but with a subset of communication
links (in case of LMST and RNG). Nevertheless, the goal stays the same: simplifying
the physical structure and providing a logic structure based on localized decisions.
2.3.3.1 Connected dominating set
A Connected Dominating Set (CDS) S is defined as a subset of the vertex set V , which
satisfies two characteristics:
• Every node of V is either in S or a neighbor of a node in S.
• S is connected.
The formulation of CDS is:
∀u ∈ V, ∃v ∈ S|v ∈ N(u) (2.1)∀(u, v) ∈ S2, ∃p = Pathu→v|∀w ∈ p,w ∈ S (2.2)
By extending the formula (2.1) to k-hop neighborhood, we obtain the definition of
k-CDS:
∀u ∈ V, ∃v ∈ Sk|v ∈ Nku (2.3)
CDS based self-organizations aim at computing a CDS with small cardinality and
low computation complexity using distributed algorithms. Computation of a Mini-
mum CDS (MCDS) is known as a NP-hard problem. Hence, many distributed CDS
construction algorithms are designed to compute an approximate MCDS.
One localized algorithm is proposed in [4] by Wu and Li. They assume that each
node in the network owns a unique identifier. Nodes send periodic HELLO messages
to exchange neighbor lists with their one-hop neighbors. Each node hence has the
knowledge of 2-hop neighborhood. A node marks itself as dominating node if any pair
of its neighbors is not directly connected. After this marking procedure, all dominating
nodes form a CDS, but the cardinality of this CDS is very big. In order to reduce the
size of CDS, two self-pruning rules are adopted.
1. If node u is a dominating node in current CDS and there exists one neighbor of
u, say v, whose one-hop neighbor set N(v) includes the N(u), and the ID(u) <
ID(v), then node u becomes a dominated node.
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State of the Art
1
8
5
4
3
2
6
7
9
(a) Marking process
1
8
5
4
3
2
6
7
9
(b) Rule 2 [4]
5
Dominated node
Dominating node
6
2
1
7
9
8 4
3
(c) Rule k [5]
Figure 2.8: Example of CDS construction in [4] and [5]
2. If node u is a dominating node in current CDS and there exist two neighbors of u,
say v and w, whose one-hop neighbor set union N(v)∪N(w) includes N(u), andID(u) = min(ID(u), ID(v), ID(w), then node u becomes a dominated node.
The self-pruning rules reduce significantly the cardinality of CDS. In [5], an enhanced
rule, Rule k, is proposed. If the neighbor set of a dominating node is covered by a set
of k neighbor nodes, and it has the lowest identifier, then it becomes a dominated node.
Rule k is more efficient in reducing dominating set size than the combination of Rules 1
and 2, and has the same communication complexity and less computation complexity.
Fig. 2.8(a)-2.8(c) illustrates the CDS construction algorithm of Wu and Li. In Fig.
2.8(a), nodes {3, 4, 5, 6, 8, 9} are marked as dominating nodes because each of them hasat least a pair of non-connected neighbors. After applying Rule 2 in [4], node 3 finds
its neighbor set is completely covered by two nodes with bigger identifiers: 5 and 6.
It changes itself to a dominated node. When Rule k [5] is applied, node 4 decides to
become a dominated node because its neighbor set is fully covered by nodes {5, 6, 9}.There are other methods of CDS construction such as [46] and [47]. However, interme-
diate structures are formed (MIS and MPR respectively) during the CDS construction.
That is why these solutions are classified in section 2.3.3.2 and 2.3.4.
CDS is used as logic backbone in a network. Although Wu&Li’s CDS algorithm is
completely localized, every node still need two-hop information. To this end, the use
of HELLO message is indispensable. Hence, the control message cost rises significantly.
2.3.3.2 Maximal independent set
An Independent Set (IS) is a subset of vertexes which do not contain adjacent nodes
in a graph. A formal definition of IS is the following:
IS = {u ∈ IS|(not∃v ∈ IS|u ∈ N(v))}
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Self-organization
The Maximal Independent Set (MIS) is the Independent Set containing maximum num-
ber of vertexes. The particular interest in MIS problem stems on practical importance
of distributed computation in ad hoc and wireless sensor networks. A MIS defines a set
of nodes which can operate in scheduling or parallel processing without interference.
Furthermore, it is possible to obtain approximate Minimum CDS structure based on a
MIS structure.
[46] proposes to construct a MIS based on a rooted tree. It is a distributed algorithm
but not a localized algorithm, because one node should initiate the construction. A
spanning tree is first generated in the network by using a flooding. During the tree
construction, each node is also assigned with a rank. They move to a color-marking
phase to determinate the MIS nodes. All nodes in the network are initially marked
WHITE. The root node marks itself BLACK and broadcasts a BLACK message. A
WHITE node which receives a BLACK message becomes GRAY node and sends a
GRAY message. A WHITE node which receives GRAY messages from all its neighbor
closer to root node will marks itself BLACK and broadcasts a BLACK message. By
propagating BLACK and GRAY messages in the network, all nodes are marked either
BLACK or GRAY. The set of BLACK nodes is the MIS. The algorithm can be extended
to construct a CDS by adding a phase to connect all BLACK nodes.
Although the final CDS has a lower cardinality, the message overhead and time
complexity of the algorithm are higher than those in [4]. Hence, it does not meet the
energy constraint of WSNs. The resulting structure depends on the root node, which
does not leave the solution enough flexibility and scalability.
2.3.3.3 Relative neighborhood graph
Relative Neighborhood Graph (RNG) is a geometric concept proposed by Toussaint
[48] in 1980. The idea is to prune the longest edge in each triangle in the graph. It is
formulated as:
RNG = {(u, v) ∈ RNG|(not∃w ∈ N(u) ∩ N(v)|d(u, v) > d(u,w)d&d(u, v) > d(v,w))}
where d(u, v) is a weight assigned to edge (u, v). The length of an edge is often used as
its weight. As shown in Fig. 2.9, the edge (uv) is not in the RNG subgraph, because
it is the longest edge in triangle (u, v,w).
RNG subgraph is not a loop-free graph, although each local structure around a node
is a tree structure. It is shown that the