Wireless Sensor Networks: An Information Processing ApproachDesigning, implementing, and operating a wireless sensor network involves a wide range of disciplines and many application-specific constraints. To make sense of and take advantage of these systems, a holistic approach is neededand this is precisely what Wireless Sensor Networks delivers. Inside, two eminent researchers review the diverse technologies and techniques that interact in todays wireless sensor networks. At every step, they are guided by the high-level information-processing tasks that determine how these networks are architected and administered. Zhao and Guibas begin with the canonical problem of localizing and tracking moving objects, then systematically examine the many fundamental sensor network issues that spring from it, including network discovery, service establishment, data routing and aggregation, query processing, programming models, and system organization. The understanding gained as a resulthow different layers support the needs of different applications, and how a wireless sensor network should be built to optimize performance and economyis sure to endure as individual component technologies come and go. Features: Written for practitioners, researchers, and students and relevant to all application areas, including environmental monitoring, industrial sensing and diagnostics, automotive and transportation, security and surveillance, military and battlefield uses, and large-scale infrastructural maintenance. Skillfully integrates the many disciplines at work in wireless sensor network design: signal processing and estimation, communication theory and protocols, distributed algorithms and databases, probabilistic reasoning, energy-aware computing, design methodologies, evaluation metrics, and more. Demonstrates how querying, data routing, and network self-organization can support high-level information-processing tasks. About the Authors: Feng Zhao is a senior researcher at Microsoft, where he manages the Networked Embedded Computing Group. He received his Ph.D. in Electrical Engineering and Computer Science from MIT and has taught at at Stanford University and Ohio State University. Dr. Zhao was a principal scientist at Xerox PARC and directed PARCs sensor network research effort. He is serving as the Editor-In-Chief of ACM Transactions on Sensor Networks. Professor Guibas heads the Geometric Computation group in the Computer Science Department of Stanford University, where he works on algorithms for sensing, modeling, reasoning about, rendering, and acting on the physical world. He is well-known for his work in computational geometry, computer graphics, and discrete algorithms. Professor Guibas obtained his Ph.D. from Stanford, has worked at PARC, MIT, and DEC/SRC, and was recently elected an ACM Fellow. |
Contents
Chapter 1 Introduction | 1 |
Chapter 2 Canonical Roblem Localization and Tracking | 23 |
Chapter 3 Networking Sensors | 63 |
Chapter 4 Infrastructure Establishment | 103 |
Chapter 5 Sensor Tasking and Control | 135 |
Chapter 6 Sensor Network Databases | 189 |
Chapter 7 Sensor Network Platforms and Tools | 239 |
Chapter 8 Applications and Future Directions | 291 |
Appendix A Optimal Estimator Design | 307 |
Appendix B Particle Filter | 309 |
Appendix C Information Utility Measures | 313 |
Appendix D Sample Sensor Selection Criteria | 321 |
Bibliography | 323 |
347 | |
Other editions - View all
Wireless Sensor Networks: An Information Processing Approach Feng Zhao,Leonidas Guibas Limited preview - 2004 |
Wireless Sensor Networks: An Information Processing Approach Feng Zhao,Leonidas J. Guibas No preview available - 2004 |
Common terms and phrases
aggregation algorithms assume Bayesian belief broadcast Chapter clock clock skew cluster-head collaborative communication component computation cost covariance defined Delaunay triangulation denotes detection directed diffusion discussed distance distributed dynamic embedded embedded systems energy estimate event example GADT Gaussian geographic global GPSR graph hardware hash identity management implementation in-network information processing information utility interface k-d tree Kalman filter landmarks layers leader node likelihood function localization location server Mahalanobis distance mation method monitoring motes multiple mutual information neighbors nesC objective function operating optimal packet parameter path physical port agents position principal Proc programming propagation protocols radio region routing S-MAC Section sensing sensor data sensor measurements sensor nodes sensor selection sensor tasking signal simulation space spatial storage stored structure synchronization target techniques TinyDB TinyGALS TinyOS tion topology tracking problem tree typically uncertainty update variable wireless sensor networks
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