Ferrari Sensor Networks

Where Theory Meets Practice
2009
ISBN: 978-3-642-01341-6
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 394 Seiten

Reihe: Signals and Communication Technology

The idea of this book comes from the observation that sensor networks represent a topic of interest from both theoretical and practical perspectives. The title und- lines that sensor networks offer the unique opportunity of clearly linking theory with practice. In fact, owing to their typical low-cost, academic researchers have the opportunity of implementing sensor network testbeds to check the validity of their theories, algorithms, protocols, etc., in reality. Likewise, a practitioner has the opportunity of understanding what are the principles behind the sensor networks under use and, thus, how to properly tune some accessible network parameters to improve the performance. On the basis of the observations above, the book has been structured in three parts:PartIisdenotedas'Theory,'sincethetopicsofits vechaptersareapparently 'detached' from real scenarios; Part II is denoted as 'Theory and Practice,' since the topics of its three chapters, altough theoretical, have a clear connection with speci c practical scenarios; Part III is denoted as 'Practice,' since the topics of its ve chapters are clearly related to practical applications.

Autoren/Hrsg.

Ferrari, Gianluigi

Inhaltsverzeichnis

1;Preface;8
2;Contents;10
3;Contributors;12
4;Part I Theory;15
4.1;Competition and Collaboration in Wireless Sensor Networks ;16
4.1.1;H. Vincent Poor;16
4.1.1.1;1 Introduction;16
4.1.1.2;2 Energy Games in Multiple-Access Networks;17
4.1.1.3;3 Collaborative Inference;22
4.1.1.4;4 Conclusions;27
4.1.1.5;References;27
4.2;Distributed and Recursive Parameter Estimation ;29
4.2.1;Srinivasan Sundhar Ram, Venugopal V. Veeravalli, and Angelina Nedic;29
4.2.1.1;1 Introduction;29
4.2.1.2;2 Preliminaries;30
4.2.1.3;3 Simple Non-linear Regression;32
4.2.1.3.1;3.1 Algorithms;33
4.2.1.3.1.1;3.1.1 Cyclic Incremental Recursive Algorithm;33
4.2.1.3.1.2;3.1.2 Markov Incremental Recursive Algorithm;34
4.2.1.3.1.3;3.1.3 Diffusive Nonlinear Recursive Algorithm;35
4.2.1.3.2;3.2 Convergence of the Algorithms;36
4.2.1.3.3;3.3 Effect of Quantization;38
4.2.1.3.4;3.4 Special Case: Linear Regression;40
4.2.1.3.5;3.5 Special Case: Accurate Model Sets;41
4.2.1.4;4 Gaussian Linear State Space Model Sets;42
4.2.1.4.1;4.1 Convergence of the Algorithm;43
4.2.1.5;5 Application: Determining the Source of a Diffusion Field;44
4.2.1.5.1;5.1 Point Source and Constant Intensity Model Sets;44
4.2.1.5.1.1;5.1.1 Numerical Results;45
4.2.1.5.2;5.2 Point Source and Time-Varying Intensity Model Sets;46
4.2.1.5.2.1;5.2.1 Numerical Results;47
4.2.1.6;6 Discussion;48
4.2.1.7;References;49
4.3;Self-Organization of Sensor Networks with HeterogeneousConnectivity;51
4.3.1;Arun Prasath, Abhinay Venuturumilli, Aravind Ranganathan, and Ali A. Minai;51
4.3.1.1;1 Introduction;51
4.3.1.2;2 Background and Motivation;53
4.3.1.3;3 System Description;55
4.3.1.3.1;3.1 Whisperers and Shouters;56
4.3.1.4;4 Self-Organization Algorithms;57
4.3.1.4.1;4.1 Basic Self-Organization (BSO) Algorithm;57
4.3.1.4.2;4.2 Self-Organization Algorithm A;58
4.3.1.4.3;4.3 Self-Organization Algorithm B;59
4.3.1.4.4;4.4 Self-Organization Algorithm C;59
4.3.1.5;5 Simulation, Results and Discussion;60
4.3.1.5.1;5.1 Simulations;60
4.3.1.5.2;5.2 Results and Discussion;61
4.3.1.5.2.1;5.2.1 Performance Comparison Between Algorithms;61
4.3.1.5.2.2;5.2.2 Comparison with Non-optimized Networks;61
4.3.1.5.2.3;5.2.3 Robustness Evaluation;65
4.3.1.6;6 Conclusion;67
4.3.1.7;References;68
4.4;Cooperative Strategies in Dense Sensor Networks ;72
4.4.1;Anna Scaglione, Y.-W. Peter Hong, and Birsen Sirkeci Mergen;72
4.4.1.1;1 The Role of Correlated Information in Sensor Systems;73
4.4.1.1.1;1.1 Feedback and Correlation;74
4.4.1.2;2 Sensor Data Model;75
4.4.1.3;3 A Cooperative Broadcast Mechanism for Network Feedback;77
4.4.1.3.1;3.1 The OR Broadcast Channel;78
4.4.1.4;4 Channel Coding via Query-and-Response Strategies;79
4.4.1.5;5 Optimized Recursive Group Testing Algorithm;80
4.4.1.6;6 Binary Tree Splitting Algorithm;82
4.4.1.7;7 Conclusions;84
4.4.1.8;References;84
4.5;Multipath Diversity and Robustness for Sensor Networks ;86
4.5.1;Christina Fragouli, Katerina Argyraki, and Lorenzo Keller;86
4.5.1.1;1 Introduction;86
4.5.1.2;2 What is a Collection Protocol?;87
4.5.1.2.1;2.1 Path Cost and Channel Quality;88
4.5.1.3;3 Routing on a Tree;89
4.5.1.4;4 From Tree to Multipath Routing;91
4.5.1.4.1;4.1 Topology Construction;91
4.5.1.4.1.1;4.1.1 Disjoint Paths;91
4.5.1.4.1.2;4.1.2 Algorithmic Complexity of Disjoint-Path Construction;92
4.5.1.4.1.3;4.1.3 Braided Paths;94
4.5.1.4.2;4.2 Topology Usage;95
4.5.1.4.2.1;4.2.1 Replicate Transmissions;95
4.5.1.4.2.2;4.2.2 Independent Transmissions;96
4.5.1.4.2.3;4.2.3 Erasure Coding;96
4.5.1.4.2.4;4.2.4 Path-Selective Routing;96
4.5.1.4.3;4.3 Room for Improvement;97
4.5.1.5;5 What Is Network Coding;97
4.5.1.5.1;5.1 Network Coding in Practice;99
4.5.1.5.2;5.2 Randomized Network Coding;99
4.5.1.5.2.1;5.2.1 Generations and Coding Vectors;100
4.5.1.5.2.2;5.2.2 Subspace Coding;101
4.5.1.6;6 Network Coding for Sensor Networks;102
4.5.1.6.1;6.1 Code Design;104
4.5.1.6.2;6.2 Opportunistic Broadcasting with Network Coding;106
4.5.1.7;7 Conclusions;108
4.5.1.8;References;108
5;Part II Theory and Practice;111
5.1;Data Aggregation in Wireless Sensor Networks: A Multifaceted Perspective ;112
5.1.1;Sergio Palazzo, Francesca Cuomo, and Laura Galluccio;112
5.1.1.1;1 Background;112
5.1.1.1.1;1.1 Terminology;115
5.1.1.1.2;1.2 Typologies of Data Aggregation;117
5.1.1.2;2 Perspectives for a Taxonomy of Data Aggregation;119
5.1.1.3;3 Layer-Centric Taxonomy;120
5.1.1.4;4 Ingredient-Centric Taxonomy;121
5.1.1.5;5 Performance-Centric Taxonomy;122
5.1.1.6;6 Information-Centric Taxonomy;123
5.1.1.6.1;6.1 Description of Information;125
5.1.1.6.2;6.2 Information Propagation;129
5.1.1.6.2.1;6.2.1 Medium Access Management;129
5.1.1.6.2.2;6.2.2 Packet Scheduling;131
5.1.1.6.2.3;6.2.3 Propagation Path Structure;132
5.1.1.6.3;6.3 Preservation of Information;138
5.1.1.6.3.1;6.3.1 Preservation of Integrity;139
5.1.1.6.3.2;6.3.2 Preservation from Security Threats;143
5.1.1.7;7 Conclusions;148
5.1.1.8;References;149
5.2;Robust Data Dissemination for Wireless Sensor Networks in Hostile Environments ;153
5.2.1;Jun Lu, Yi Pan, Satoshi Yamamoto, and Tatsuya Suda;153
5.2.1.1;1 Introduction;153
5.2.1.2;2 Related Work;154
5.2.1.3;3 RObust dAta Dissemination (ROAD);156
5.2.1.3.1;3.1 Assumptions;156
5.2.1.3.2;3.2 Scheme Description;157
5.2.1.3.2.1;3.2.1 Data Publishing;158
5.2.1.3.2.2;3.2.2 Data Retrieval;161
5.2.1.3.2.3;3.2.3 Trajectory Maintenance;161
5.2.1.3.3;3.3 Extensions;162
5.2.1.3.3.1;3.3.1 Double-Sided Hole Circumvention;162
5.2.1.3.3.2;3.3.2 ROAD for Generic Trajectories;163
5.2.1.3.3.3;3.3.3 Time-Based Load Balancing;163
5.2.1.4;4 Simulation Evaluation;164
5.2.1.4.1;4.1 Communication Overhead;164
5.2.1.4.2;4.2 Response Time;167
5.2.1.4.3;4.3 Reliability of Data Retrieval;167
5.2.1.4.4;4.4 Robustness Against Large Scale Sensor Failures;168
5.2.1.5;5 Conclusion;172
5.2.1.6;References;173
5.3;Markov Decision Process-Based Resource and Information Management for Sensor Networks ;174
5.3.1;David Akselrod, Thomas Lang, Michael McDonald, and Thiagalingam Kirubarajan;174
5.3.1.1;1 Introduction;174
5.3.1.2;2 Decision-Based Resource and Information Management;179
5.3.1.2.1;2.1 Problem Formulation;179
5.3.1.2.2;2.2 Sensor Management as a Decision Mechanism;179
5.3.1.2.3;2.3 Policy Update and Termination Criteria;181
5.3.1.3;3 Decision-Based Multitarget Tracking;182
5.3.1.3.1;3.1 MDP-Based Structure for Multisensor Multitarget Tracking;182
5.3.1.3.2;3.2 Expected Information Gain-Based Reward Structure of MDP for Sensor Management;184
5.3.1.4;4 Multi-Level Hierarchy of MDPs for Sensor Management;187
5.3.1.5;5 Dynamic Element Matching-Based Modified ValueIteration Algorithm;189
5.3.1.5.1;5.1 Drawbacks of Finding the Optimal Policy of MDP;189
5.3.1.5.2;5.2 Dynamic Element Matching;190
5.3.1.5.3;5.3 Modified Value Iteration Method;191
5.3.1.6;6 Distributed Data Fusion Architecture;193
5.3.1.6.1;6.1 Issues in Distributed Data Fusion;193
5.3.1.6.2;6.2 Data Fusion Control as a Decision-Based Approach;196
5.3.1.6.3;6.3 Data Lookup;196
5.3.1.6.4;6.4 MDP-Based Multisensor Fusion for Multitarget Tracking;197
5.3.1.6.4.1;6.4.1 Set of States: S;199
5.3.1.6.4.2;6.4.2 Set of Actions: A;199
5.3.1.6.4.3;6.4.3 Transition Probabilities: P;200
5.3.1.6.4.4;6.4.4 Real-Valued Reward Function on States: R;200
5.3.1.7;7 Distributed Tracking Algorithms Implementing MDP-Based Data Fusion System;201
5.3.1.7.1;7.1 Associated Measurements Fusion;201
5.3.1.7.2;7.2 Track-to-Track Fusion;202
5.3.1.7.3;7.3 Tracklet Fusion;203
5.3.1.8;8 Simulation Results;204
5.3.1.8.1;8.1 Resource Management;204
5.3.1.8.2;8.2 Information Management;209
5.3.1.9;9 Communication Data Rate and Computational Load in Distributed Tracking Algorithms;213
5.3.1.9.1;9.0.1 Communication and Computational Load Results;215
5.3.1.10;10 Conclusions;217
5.3.1.11;References;219
6;Part III Practice;224
6.1;Deployment Techniques for Sensor Networks ;225
6.1.1;Jan Beutel, Kay Römer, Matthias Ringwald, and Matthias Woehrle;225
6.1.1.1;1 Introduction;225
6.1.1.2;2 Wireless Sensor Network Deployments;226
6.1.1.2.1;2.1 Great Duck Island;227
6.1.1.2.2;2.2 A Line in the Sand;228
6.1.1.2.3;2.3 Oceanography;229
6.1.1.2.4;2.4 GlacsWeb;229
6.1.1.2.5;2.5 Structural Health Monitoring;229
6.1.1.2.6;2.6 Pipenet;230
6.1.1.2.7;2.7 Redwood Trees;230
6.1.1.2.8;2.8 LOFAR-agro;231
6.1.1.2.9;2.9 Volcanoes;231
6.1.1.2.10;2.10 Soil Ecology;232
6.1.1.2.11;2.11 Luster;232
6.1.1.2.12;2.12 SensorScope;233
6.1.1.3;3 Deployment Problems;233
6.1.1.3.1;3.1 Node Problems;234
6.1.1.3.2;3.2 Link Problems;234
6.1.1.3.3;3.3 Path Problems;235
6.1.1.3.4;3.4 Global Problems;236
6.1.1.3.5;3.5 Discussion;237
6.1.1.4;4 Understanding the System;237
6.1.1.4.1;4.1 Hardware;237
6.1.1.4.2;4.2 Software;239
6.1.1.4.3;4.3 Communication;239
6.1.1.4.4;4.4 Environment;240
6.1.1.5;5 Node Instrumentation;240
6.1.1.5.1;5.1 Software Instrumentation;240
6.1.1.5.2;5.2 Hardware Instrumentation;242
6.1.1.6;6 Network Instrumentation Methods;244
6.1.1.7;7 Analyzing the System;246
6.1.1.7.1;7.1 Monitoring and Visualization;246
6.1.1.7.2;7.2 Inferring Network State from Node States;247
6.1.1.7.3;7.3 Failure Detection;247
6.1.1.7.4;7.4 Root Cause Analysis;248
6.1.1.7.5;7.5 Node-Level Debugging;249
6.1.1.7.6;7.6 Replay and Checkpointing;250
6.1.1.8;8 Concluding Remarks;251
6.1.1.9;References;251
6.2;Static and Dynamic Localization Techniques for Wireless Sensor Networks ;255
6.2.1;Jean-Michel Dricot, Gianluca Bontempi, and Philippe De Doncker;255
6.2.1.1;1 Introduction;255
6.2.1.2;2 Static Localization Techniques -- Range-free;257
6.2.1.2.1;2.1 Weighted Centroid;258
6.2.1.2.2;2.2 Bounding Box;259
6.2.1.2.3;2.3 Point-in-Triangle;260
6.2.1.3;3 Distance Estimation Techniques;262
6.2.1.3.1;3.1 Estimation of the Distance Based on the Received Power;262
6.2.1.3.2;3.2 Angle-of-Arrival;263
6.2.1.3.3;3.3 Time-of-Flight;265
6.2.1.4;4 Static Localization Techniques -- Range-based;266
6.2.1.4.1;4.1 Circular Lateration and Multilateration;266
6.2.1.4.2;4.2 Hyperbolic Lateration;269
6.2.1.5;5 Dynamic Localization and Tracking;270
6.2.1.5.1;5.1 Kalman Filtering Loop;272
6.2.1.5.2;5.2 Filtering Process when the Speed and the Position are Unknown;273
6.2.1.5.3;5.3 Filtering Process for an Unknown Position and an Approximated Speed;275
6.2.1.6;6 Accuracy and Precision;275
6.2.1.7;7 Advanced Localization Techniques -- Data Fusion by means of Machine Learning;277
6.2.1.7.1;7.1 Introduction;278
6.2.1.7.2;7.2 Observation of the Sensor Data -- Motion vs. Static Classification;278
6.2.1.7.3;7.3 A Data Fusion Approach for the Kalman Filter;281
6.2.1.7.4;7.4 Performance Evaluation of the Fusion Schemes;282
6.2.1.7.5;7.5 Fusion of Localization Estimators;283
6.2.1.8;8 Open Issues in Localization and Conclusion;284
6.2.1.9;References;286
6.3;Enhancing Underwater Acoustic Sensor Networks Using Surface Radios: Issues, Challenges and Solutions ;288
6.3.1;Zhong Zhou, Hai Yan, Saleh Ibrahim, Jun-Hong Cui, Zhijie Shi,and Reda Ammar;288
6.3.1.1;1 Introduction;288
6.3.1.2;2 UASN-MG Architecture and Its Benefits;290
6.3.1.3;3 Research Challenges and Design Issues;292
6.3.1.4;4 Design Examples;294
6.3.1.4.1;4.1 Optimal Surface Node Deployment;294
6.3.1.4.1.1;4.1.1 Network Model;294
6.3.1.4.1.2;4.1.2 Problem Formulation;295
6.3.1.4.1.3;4.1.3 Simulation Results;298
6.3.1.4.2;4.2 Efficient Routing Protocol;299
6.3.1.4.2.1;4.2.1 Network Model;300
6.3.1.4.2.2;4.2.2 Protocol Overview;300
6.3.1.4.2.3;4.2.3 Redundant Packet Suppression;301
6.3.1.4.2.4;4.2.4 Holding Time Calculation;301
6.3.1.4.2.5;4.2.5 Protocol Summary;303
6.3.1.4.2.6;4.2.6 Simulation Results;304
6.3.1.4.3;4.3 Cross Layer Design;305
6.3.1.4.3.1;4.3.1 Multi-Path Routing;306
6.3.1.4.3.2;4.3.2 Source Initiated Power-Control Transmission;307
6.3.1.4.3.3;4.3.3 Destination Packet Combining;307
6.3.1.4.3.4;4.3.4 Optimal Energy Distribution;308
6.3.1.4.3.5;4.3.5 Simulation Results;309
6.3.1.5;5 Conclusions;310
6.3.1.6;References;311
6.4;Communication Through Soil in Wireless Underground Sensor Networks -- Theory and Practice ;313
6.4.1;M. Can Vuran and Agnelo R. Silva;313
6.4.1.1;1 Introduction;313
6.4.1.2;2 Classification of Underground Communication Networks;315
6.4.1.3;3 Recent Developments;316
6.4.1.4;4 Underground Channel Model: The Theory;318
6.4.1.4.1;4.1 Signal Propagation Through Soil;320
6.4.1.4.2;4.2 Underground Channel Characteristics;323
6.4.1.4.2.1;4.2.1 Reflection from Ground Surface;324
6.4.1.4.2.2;4.2.2 Multi Path Fading and Bit Error Rate;327
6.4.1.4.3;4.3 Effects of Volumetric Water Content Variations in Soil;329
6.4.1.4.3.1;4.3.1 Long-Term VWC Effects;330
6.4.1.4.3.2;4.3.2 Transient VWC Effects;333
6.4.1.5;5 Underground Experiments -- The Practice;335
6.4.1.5.1;5.1 Antenna Orientation;337
6.4.1.5.2;5.2 Effects of Burial Depth;339
6.4.1.5.3;5.3 Effects of Inter-Node Distance;342
6.4.1.5.4;5.4 Temporal Characteristics;342
6.4.1.5.5;5.5 Effects of Soil Moisture;344
6.4.1.6;6 Open Research Issues;347
6.4.1.6.1;6.1 Energy Efficiency;347
6.4.1.6.2;6.2 Topology Design;348
6.4.1.6.3;6.3 Operating Frequency;348
6.4.1.6.4;6.4 Cross-Layer and Environment-Aware Protocol Design;349
6.4.1.7;References;350
6.5;Body Sensor Networks for Sport, Wellbeing and Health ;352
6.5.1;Douglas McIlwraith and Guang-Zhong Yang;352
6.5.1.1;1 Introduction;352
6.5.1.1.1;1.1 Body Sensor Networks;354
6.5.1.1.1.1;1.1.1 Network Topology;354
6.5.1.1.1.2;1.1.2 Requirements of Body Sensor Networks;356
6.5.1.1.1.3;1.1.3 Operating Modes;357
6.5.1.1.2;1.2 Sensors and Modalities of Sensing;358
6.5.1.1.2.1;1.2.1 Biomechanical;359
6.5.1.1.2.2;1.2.2 Ambient and Visual Sensors;360
6.5.1.1.2.3;1.2.3 Respiratory and Circulatory Monitoring;361
6.5.1.1.2.4;1.2.4 Neural, Biological and Chemical Analysis;362
6.5.1.1.2.5;1.2.5 Implants, Ingests, Actuation and Feedback;363
6.5.1.1.3;1.3 Directions and Challenges;365
6.5.1.2;2 Data Modelling and Pattern Recognition;366
6.5.1.2.1;2.1 Signal Processing and Reconditioning;367
6.5.1.2.2;2.2 Sensor Placement and Feature Selection;368
6.5.1.2.3;2.3 Data Modelling and Inference;368
6.5.1.2.4;2.4 Context Awareness;370
6.5.1.3;3 Emerging Applications;371
6.5.1.3.1;3.1 Sport;371
6.5.1.3.2;3.2 Wellbeing;375
6.5.1.3.3;3.3 Healthcare;376
6.5.1.4;4 Conclusions;377
6.5.1.5;References;378
7;Index;385