Computational Sensor Networks

By: Thomas C. Henderson

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Hardcover | 1 April 2009

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Hardcover
240 Pages

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24.13 x 15.88 x 1.91

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A model-based approach to the design and implementation of Computational Sensor Networks (CSNs) is proposed. This high-level paradigm for the development and application of sensor device networks provides a strong scientific computing foundation, as well as the basis for robust software engineering practice. The three major components of this approach include (1) models of phenomena to be monitored, (2) models of sensors and actuators, and (3) models of the sensor network computation. We propose guiding principles to identify the state or structure of the phenomenon being sensed, or of the sensor network itself. This is called computational modeling. These methods are then incorporated into the operational system of the sensor network and adapted to system performance requirements to produce a mapping of the computation onto the system architecture. This is called real-time computational mapping and allows modification of system parameters according to real-time performance measures. This book deals with the development of a mathematical and modular software development framework to achieve computational sensor networks.

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Dedication	p. v
Preface	p. vii
Introduction	p. 1
Background	p. 2
The CSN Approach	p. 4
CSN: Overview of Approach	p. 7
Scenario: Monitor Temperature	p. 7
Models	p. 8
Temperature Phenomenon Model	p. 8
Temperature Sensor Model	p. 8
CSN Design	p. 9
System Component Models	p. 11
Simulation	p. 11
Method	p. 11
Verification	p. 12
Input Streams	p. 12
Known Result Comparison	p. 13
Data, Analysis and Interpretation	p. 13
Validation	p. 19
Summary	p. 19
Leadership Algorithms	p. 21
Leadership Protocol	p. 22
Correctness	p. 23
SNL Simulation	p. 25
The Simulation Logic	p. 27
Verification	p. 29
Validation	p. 29
SNL Protocol Statistics	p. 29
Irregular Broadcast Region Shape	p. 34
Implementation	p. 34
Berkeley Motes	p. 36
JStamp Processors	p. 38
Summary and Conclusions	p. 40
Coöet;rdinate Frames and Gradient Calculation	p. 43
Local and Global Coöet;rdinate Frames	p. 43
Incorporating Points into a Coöet;rdinate Frame	p. 44
Constructing a Local Frame	p. 46
Moving between Local Frames	p. 49
Gradient Calculation	p. 50
Gradient Calculation	p. 52
Simulation Experiments	p. 55
Conclusion	p. 55
Pattern Formation in S-Nets	p. 61
Regular Geometric Figures	p. 65
Reaction-Diffusion Patterns	p. 69
Level Set Methods in S-Nets	p. 74
Simple Level Set Example	p. 77
Shortest Path Problem	p. 77
Future Directions	p. 80
Logical Sensors and Computational Mapping	p. 83
Logical Sensors	p. 84
Formal Aspects of Logical Sensors	p. 88
Logical Sensor Specification Language	p. 89
Fault Tolerance	p. 91
Ramifications a Replacement Scheme	p. 92
Features and Their Propagation	p. 94
Instrumented Logical Sensor Systems	p. 96
Sensor Modeling	p. 97
Performance Semantics of Sensor Systems	p. 100
Sensor System Specification	p. 102
Construction Operators	p. 104
Implementation	p. 106
Example: Wall Pose Estimation	p. 108
System Modeling and Specification	p. 108
Performance Semantic Equations	p. 109
Experimental Results	p. 113
Conclusions	p. 116
Mobile Robot Performance Analysis	p. 117
Study Design	p. 118
Mobile Robot Model	p. 119
Communication Model	p. 121
Simulation Model	p. 125
Goal Achievement	p. 127
Multiple Robot Behaviors	p. 129
One Robot Goes to a Temperature Source	p. 131
Multiple Robots Surround Temperature Source Evenly	p. 138
Multiple Robots Go Back and Forth to the Temperature Source	p. 150
CSN: The Heat Equation	p. 161
Sensor Node Localization	p. 162
Generate and Test	p. 165
Dense Sample Method	p. 166
Nonlinear Optimization Method	p. 168
Polynomial System Localization (PSL)	p. 168
Sensor Bias Estimation	p. 170
Future Directions	p. 173
Bayesian Estimation of Distributed Phenomena	p. 175
Sensor Networks for Distributed Phenomena	p. 176
Prospective Application Scenarios	p. 177
Parameter Identification (SRI method)	p. 178
Node Localization (SRL method)	p. 179
Problem Formulation	p. 180
Probabilistic Finite-Dimensional Models	p. 182
Probabilistic System Model	p. 184
Probabilistic Measurement Model	p. 188
Reconstruction of Distributed Phenomena	p. 189
Reconstruction based on Precise Mathematical Models	p. 190
Incorrect Model Parameters	p. 193
Augmented Model for Node Localization	p. 197
Decomposition of the Estimation Problem	p. 198
General Prediction and Measurement Step	p. 199
The Sliced Gaussian Mixture Filter (SGMF)	p. 200
Application: Node Localization	p. 204
Conclusions and Future Work	p. 207
Bibliography	p. 209
Index	p. 223
Table of Contents provided by Ingram. All Rights Reserved.

Computational Sensor Networks

At a Glance

Hardcover

Shipping

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Defective items

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