Radio Link Modeling

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Table of contents

1 Introduction 
1.1 Context of the Thesis
1.2 Thesis Organization
I Context 
2 Wireless Sensor Networks 
2.1 Applications
2.2 Sensor Components
2.2.1 Hardware
2.2.2 Software
2.3 Challenges
3 Model 
3.1 Physical Layer Modeling
3.1.1 Radio Range Modeling
3.1.2 Radio Link Modeling
3.1.3 Interference Modeling
3.2 WSN as a Unit-Disk Graph
3.3 Dynamic Networks
3.3.1 Time-Varying Graphs
3.3.2 Evolving Graphs
3.3.3 Definitions and Preliminaries
3.3.4 Hierarchy of Dynamic Graphs
3.4 Random Graphs
II Data Aggregation Problem 
4 Introduction of Part II 
4.1 Problem
4.1.1 Minimum Data Aggregation Time Problem in a WSN
4.1.2 Distributed Online Data Aggregation in an Arbitrary Dynamic Network
4.2 Related Work
4.2.1 Data Dissemination Problem
4.2.2 Data Aggregation Problem
4.3 Contribution of Part II
4.3.1 The Complexity of Data Aggregation in WSNs
4.3.2 Distributed Online Data Aggregation
5 The Complexity of Data Aggregation in Wireless Sensor Networks 
5.1 NP-Completeness
5.1.1 Static grid graphs of degree at most three
5.1.2 Dynamic graphs of degree at most two
5.2 Upper and Lower Bounds
5.3 Impossibility Results
5.4 Approximation Algorithm
5.5 Conclusion
6 Distributed Online Data Aggregation 
6.1 Oblivious and Online Adaptive Adversaries
6.1.1 Impossibility Results When Nodes Have no Knowledge
6.1.2 When Nodes Know The Underlying Graph
6.1.3 If Nodes Know Their Own Future
6.2 Randomized Adversary
6.2.1 Lower Bounds
6.2.2 Algorithm Performance Without Knowledge
6.2.3 Algorithm Performance With meetTime
6.3 Concluding Remarks
III Lifetime Estimation of a Wireless Sensor Network 
7 Introduction of Part III 
7.1 Problem
7.2 Related Work
7.2.1 Simulating a WSN
7.2.1.1 Generic Network Simulators
7.2.1.2 Emulators
7.2.1.3 Wireless Sensor Oriented Simulators
7.2.1.4 Simulating Energy Consumption in WSNs
7.2.2 Benchmarking Energy-Centric Broadcast Protocols
7.3 Contribution of Part III
8 WiSeBat: Accurate Energy Benchmarking of Wireless Sensor Networks 
8.1 The WiSeBat Energy Model
8.1.1 Battery Modeling
8.1.1.1 Linear Battery Modeling
8.1.1.2 The Rate Capacity Effect
8.1.1.3 Voltage Variation
8.1.1.4 WiSeBat Model
8.1.1.5 Updates Triggering
8.1.2 WiSeBat Usage
8.1.2.1 WiSeBat Options
8.1.2.2 WiSeBat Consumption and Control Functions .
8.2 Evaluation
8.2.1 Comparison with Real life measurements
8.2.2 WiSeBat simulation overhead
8.3 Simulations
8.3.1 Single Node Scenario
8.3.2 Two-Node Scenario
8.4 Conclusion
9 Energy Benchmarking of Broadcast Protocols 
9.1 Simulation Setup
9.2 Simulation Results and Discussion
9.2.1 Single Source Broadcast
9.2.2 Multiple Source Broadcast (Gossip)
9.2.3 Discussion
9.3 Conclusion
10 Conclusion 
10.1 Overview of Thesis Contributions
10.1.1 Data Aggregation
10.1.2 Lifetime Estimation of a Wireless Sensor Network
10.2 Perspectives
A Version française 
A.1 Contexte de la thèse
A.2 Modèle
A.2.1 Modélisation de la Couche Physique
A.2.2 Le Réseau Comme un Graphe de Disque-Unité
A.2.3 Modèle pour les Réseaux Dynamiques
A.3 Le Problème de l’Agrégation des Données
A.3.1 Dans les Réseaux de Capteurs Sans Fil
A.3.1.1 Complexité de l’Agrégation de Données en Temps Minimum
A.3.1.2 Borne Inférieure et Borne Supérieure
A.3.1.3 Conclusion
A.3.2 Agrégation en Ligne Dans un Réseau Dynamique Arbitraire .
A.3.2.1 Face à un Adversaire Sans Mémoire
A.3.2.2 Face à un Adversaire Probabiliste
A.3.2.3 Conclusion
A.4 L’Estimation de la Durée de Vie des Batteries
A.4.1 WiSeBat: Modèle pour une Évaluation Réaliste de la Durée de Vie des Batteries
A.4.1.1 Le Module WiSeBat
A.4.1.2 Validation Expérimentale
A.4.1.3 Conclusion
A.4.2 Analyse Comparative des Protocoles de Broadcasts Efficaces en Énergie
A.4.2.1 Configuration des Simulations
A.4.2.2 Résultats et Discussion
A.4.2.3 Conclusion
A.5 PerspectivesIndex
Acronyms and Abbreviations
List of Symbols
Bibliography