Innovations diffusion: A spatial sampling scheme for distributed estimation and detection

IEEE Transactions on Signal Processing

Volumn 57, Issue 2, 2009, Pages 738-751

  Quan, Zhi ^a   Kaiser, Williaim J ^a   Sayed, Ali H ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Diffusion; Distributed processing; Estimation and detection; Innovations; Sampling; Wireless networks

Indexed keywords

DIFFUSION; DISTRIBUTED PARAMETER NETWORKS; ENERGY CONSERVATION; ESTIMATION; SENSOR NETWORKS; SENSOR NODES; SENSORS; SIGNAL PROCESSING; TELECOMMUNICATION EQUIPMENT; TELECOMMUNICATION NETWORKS;

ACTIVE SENSORS; CONSERVE ENERGIES; DISTRIBUTED ESTIMATIONS; DISTRIBUTED PROCESSING; ESTIMATION AND DETECTION; EXTENSIVE SIMULATIONS; INNOVATIONS DIFFUSIONS; LIMITED COMMUNICATIONS; LOCAL COMMUNICATIONS; SAMPLING SCHEMES; SENSOR MEASUREMENTS; SENSOR SELECTIONS; SPATIAL SAMPLINGS;

WIRELESS NETWORKS;

EID: 60549101930     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2008.2008264     Document Type: Article

References (25)

1. Z. Quan, W. J. Kaiser, and A. H. Sayed, "A spatial sampling scheme based on innovations diffusion in sensor networks," in Proc. Inf. Process. Sens. Netw. (IPSN), Cambridge, MA, Apr. 2007, pp. 323-330.
2. I. F. Akyildiz, W. Su, Y. Sankarsubramaniam, and E. Cayirci, "Wireless sensor networks: A survey," Comput. Netw., vol. 38, pp. 393-422, Mar. 2002.
3. M. Perillo, Z. Ignjatovic, and W. Heinzelman, "An energy conservation method for wireless sensor networks employing a blue noise spatial sampling technique," in Proc. Inf. Process. Sens. Netw. (IPSN), Berkeley, CA, Apr. 2004, pp. 116-123.
4. A. H. Sayed, Fundamentals of Adaptive Filtering. New York: Wiley, 2003.
5. S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory. Englewood Cliffs, NJ: Prentice-Hall, 1993.
6. V. Delouille, R. Neelamani, and R. Baraniuk, "Robust distributed estimation in sensor networks using the embedded polygons algorithm," in Proc. Inf. Process. Sens. Netw. (IPSN), Berkeley, CA, Apr. 2004, pp. 405-413.
7. R. Nowak, U. Mitra, and R.Willett, "Estimating inhomogeneous fields using wireless sensor networks," IEEE J. Select. Areas Commun., vol. 22, pp. 999-1006, Aug. 2004.
8. M. Rabbat and R. Nowak, "Distributed optimization in sensor networks," in Proc. Inf. Process. Sens. Netw. (IPSN), Berkeley, CA, Apr. 2004, pp. 20-27.
9. L. Xiao, S. Boyd, and S. Lall, "A scheme for robust distributed sensor fusion based on average consensus," in Proc. Inf. Process. Sens. Netw. (IPSN), Los Angeles, CA, Apr. 2005, pp. 63-70.
10. Y. Sung, S. Misra, L. Tong, and A. Ephremides, "Signal processing for application-specific ad hoc networks," IEEE Signal Process. Mag., vol. 23, no. 5, pp. 74-83, Sep. 20006.
11. V. Saligrama, M. Alanyali, and O. Savas, "Distributed detection in sensor networks with packet losses and finite capacity links," IEEE Trans. Signal Process., vol. 54, pp. 4118-4132, Nov. 2006.
12. C. G. Lopes and A. H. Sayed, "Incremental adaptive strategies over distributed networks," IEEE Trans. Signal Process., vol. 55, pp. 4064-4077, Aug. 2007.
13. F. Zhao, J. Shin, and J. Reich, "Information-driven dynamic sensor collaboration," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 61-72, 2002.
14. E. Ertin, J. Fisher, and L. Potter, "Maximum mutual information principle for dynamic sensor query problems," in Proc. Inf. Process. Sens. Netw. (IPSN), Palo Alto, CA, Apr. 2003, pp. 405-416.
15. H.Wang, G. Pottie, K.Yao, and D. Estrin, "Entropy-based sensor selection heuristic for target localization," in Proc. Inf. Process. Sens. Netw. (IPSN), Berkeley, CA, Apr. 2004, pp. 36-45.
16. V. Isler and R. Bajcsy, "The sensor selection problem for bounded uncertainty sensing models," in Proc. Inf. Process. Sens. Netw. (IPSN), Los Angeles, CA, Apr. 2005, pp. 151-158.
17. Z. Hu, J. Zhang, and L. Tong, "Adaptive sensor activity control in many-to-one sensor networks," IEEE J. Sel. Areas Commun., vol. 24, pp. 1525-1534, Aug. 2006.
18. Z. Quan and A. H. Sayed, "Innovations-based sampling over spatially-correlated sensors," in Proc. IEEE ICASSP, Honolulu, HI, Apr. 2007, pp. 509-512.
19. J. Xiao, S. Cui, Z. Q. Luo, and A. J. Goldsmith, "Power scheduling of universal decentralized estimation in sensor networks," IEEE Trans. Signal Process., vol. 54, pp. 413-422, Feb. 2006.
20. N. A. C. Cressie, Statistics for Spatial Data. New York:WIley, 1993.
21. M. C. Vuran, O. B. Akan, and I. F. Akyildiz, "Spatio-temporal correlation: Theory and applications for wireless sensor networks," Comput. Netw., vol. 45, pp. 245-259, 2004.
22. C. H. Papadimitriou and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity. Englewood Cliffs, NJ: Prentice-Hall, 1982.
23. T. Kailath, A. H. Sayed, and B. Hassibi, Linear Estimation. Englewood Cliffs, NJ: Prentice-Hall, 2000.
24. R. G. Gallager, Discrete Stochastic Processes. New York: Kluwer, 1996.
25. D. P. Bertsimas and J. N. Tsitsiklis, Parallel and Distributed Computation: Numerical Methods. Englewood Cliffs, NJ: Prentice-Hall, 1989.