"The six years since the first edition appeared has seen the field of sensor networks ripen. The number of researchers working in the field and the number of technical (journal and conference) publications related to sensor networks have exploded. Those of us working in the field since its infancy have been gratified by the large number of practical sensor network applications that are being used. Not surprisingly, a number of lessons have been learned. The current state of affairs could hardly have beenpredicted when the first edition went to press. Partly because of this, we were extremely gratified when Chapman & Hall contacted us and suggested printing a revised second edition of Distributed Sensor Networks"--
The best-selling Distributed Sensor Networks became the definitive guide to understanding this far-reaching technology. Preserving the excellence and accessibility of its predecessor, Distributed Sensor Networks, Second Edition once again provides all the fundamentals and applications in one complete, self-contained source. Ideal as a tutorial for students or as research material for engineers, the book gives readers up-to-date, practical insight on all aspects of the field.
Revised and expanded, this second edition incorporates contributions from many veterans of the DARPA ISO SENSIT program as well as new material from distinguished researchers in the field. Image and Sensor Signal Processing focuses on software issues and the history and future of sensor networks. The book also covers information fusion and power management.
Readers of this book may also be interested in Distributed Sensor Networks, Second Edition: Sensor Networking and Applications (ISBN: 9781439862872).
| Preface to the Second Edition |
|
xiii | |
| Preface to the First Edition |
|
xv | |
| Editors |
|
xvii | |
| Contributors |
|
xix | |
|
|
|
|
1 Microsensor Applications |
|
|
3 | (18) |
|
|
|
|
|
2 Interfacing Distributed Sensor Actuator Networks with the Physical World |
|
|
21 | (12) |
|
|
|
|
|
3 Contrast with Traditional Systems |
|
|
33 | (8) |
|
|
|
PART II Distributed Sensing and Signal Processing |
|
|
|
4 Digital Signal Processing Backgrounds |
|
|
41 | (18) |
|
|
|
5 Image Processing Background |
|
|
59 | (34) |
|
|
|
|
|
6 Object Detection and Classification |
|
|
93 | (18) |
|
|
|
|
|
111 | (20) |
|
|
|
8 Target Tracking with Self-Organizing Distributed Sensors |
|
|
131 | (60) |
|
|
|
|
|
|
|
|
|
|
|
9 Collaborative Signal and Information Processing |
|
|
191 | (16) |
|
|
|
|
|
|
|
|
|
|
|
|
|
207 | (12) |
|
|
|
11 Detecting and Counteracting Atmospheric Effects |
|
|
219 | (26) |
|
|
|
12 Signal Processing and Propagation for Aeroacoustic Sensor Networks |
|
|
245 | (46) |
|
|
|
|
|
|
|
13 Distributed Multi-Target Detection in Sensor Networks |
|
|
291 | (16) |
|
|
|
|
|
|
|
14 Symbolic Dynamic Filtering for Pattern Recognition in Distributed Sensor Networks |
|
|
307 | (28) |
|
|
|
|
|
|
|
|
|
|
|
335 | (42) |
|
|
|
|
|
PART III Information Fusion |
|
|
|
16 Foundations of Data Fusion for Automation |
|
|
377 | (10) |
|
|
|
|
|
|
|
17 Measurement-Based Statistical Fusion Methods for Distributed Sensor Networks |
|
|
387 | (28) |
|
|
|
18 Soft Computing Techniques |
|
|
415 | (14) |
|
|
|
19 Estimation and Kalman Filters |
|
|
429 | (26) |
|
|
|
|
|
455 | (40) |
|
|
|
|
|
|
|
|
|
21 Signal Calibration, Estimation for Real-Time Monitoring and Control |
|
|
495 | (18) |
|
|
|
|
|
22 Semantic Information Extraction |
|
|
513 | (10) |
|
|
|
23 Fusion in the Context of Information Theory |
|
|
523 | (18) |
|
|
|
|
|
|
|
541 | (12) |
|
|
|
25 Chebyshev's Inequality-Based Multisensor Data Fusion in Self-Organizing Sensor Networks |
|
|
553 | (16) |
|
|
|
|
|
|
|
|
|
|
|
|
|
26 Markov Model Inferencing in Distributed Systems |
|
|
569 | (12) |
|
|
|
|
|
|
|
|
|
|
|
27 Emergence of Human-Centric Information Fusion |
|
|
581 | (14) |
|
|
|
|
|
|
28 Designing Energy-Aware Sensor Systems |
|
|
595 | (14) |
|
|
|
|
|
|
|
|
|
|
|
|
|
29 Operating System Power Management |
|
|
609 | (30) |
|
|
|
|
|
30 An Energy-Aware Approach for Sensor Data Communication |
|
|
639 | (16) |
|
|
|
|
|
|
|
|
|
|
|
31 Compiler-Directed Communication Energy Optimizations for Microsensor Networks |
|
|
655 | (24) |
|
|
|
|
|
|
|
|
|
|
|
|
|
32 Jamming Games in Wireless and Sensor Networks |
|
|
679 | (14) |
|
|
|
|
|
|
|
33 Reasoning about Sensor Networks |
|
|
693 | (18) |
|
|
|
|
|
|
|
34 Testing and Debugging Sensor Network Applications |
|
|
711 | (18) |
|
|
|
|
|
|
| Index |
|
729 | |
S. Sitharama Iyengar is the director of and Ryder Professor in the School of Computing and Information Sciences at Florida International University. A SIAM distinguished lecturer, ACM national lecturer, IEEE distinguished scientist, and fellow of the IEEE, ACM, AAAS, and SDPS, Dr. Iyengar is the founding editor-in-chief of the International Journal of Distributed Sensor Networks. He is a world-class expert in computational aspects of sensor networks, data structures, and algorithms for various distributed applications. Many of his techniques have been used at institutions such as NASA and Oak Ridge National Laboratory.
Richard R. Brooks is an associate professor of electrical and computer engineering at Clemson University. A senior member of the IEEE, Dr. Brooks is the associate managing editor of the International Journal of Distributed Sensor Networks. His research interests include distributed systems, network security, sensor networks, and self-organizing systems. His work has been sponsored by DARPA, the U.S. Office of Naval Research, and other high-level organizations.
Lisainfo e-raamatute kohta