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Issue No.09 - September (2009 vol.58)
pp: 1185-1197
Michalis P. Michaelides , University of Cyprus, Nicosia
Christos G. Panayiotou , University of Cyprus, Nicosia
ABSTRACT
This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal that propagates over a large region. In this context, we assume that the sensors make binary observations and report the event (positive observations) if the measured signal at their location is above a threshold; otherwise, they remain silent (negative observations). Based on the sensor binary beliefs, a likelihood matrix is constructed whose maximum value points to the event location. The main contribution of this work is Subtract on Negative Add on Positive (SNAP), an estimation algorithm that provides an efficient way of constructing the likelihood matrix by simply adding \pm 1 contributions from the sensor nodes depending on their alarm state (positive or negative). This simple estimation procedure provides very accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations.
INDEX TERMS
Wireless sensor networks, event localization, maximum likelihood estimation, binary data, fault tolerance.
CITATION
Michalis P. Michaelides, Christos G. Panayiotou, "SNAP: Fault Tolerant Event Location Estimation in Sensor Networks Using Binary Data", IEEE Transactions on Computers, vol.58, no. 9, pp. 1185-1197, September 2009, doi:10.1109/TC.2009.60
REFERENCES
[1] F. Zhao and L. Guibas, Wireless Sensor Networks: An Information Processing Approach. Morgan Kaufmann, 2004.
[2] I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A Survey on Sensor Networks,” IEEE Comm. Magazine, vol. 40, no. 8, pp. 102-114, Aug. 2002.
[3] C. Chong and S. Kumar, “Sensor Networks: Evolution, Opportunities, and Challenges,” Proc. IEEE, vol. 91, no. 8, pp. 1247-1256, Aug. 2003.
[4] S. Kumar, F. Zhao, and D. Shepherd, “Collaborative Signal and Information Processing in Microsensor Networks,” IEEE Signal Processing Magazine, vol. 19, no. 2, pp. 13-14, Mar. 2002.
[5] J. Chen, R. Hudson, and K. Yao, “Maximum-Likelihood Source Localization and Unknown Sensor Location Estimation for Wideband Signals in the Near-Field,” IEEE Trans. Signal Processing, vol. 50, no. 8, pp. 1843-1854, Aug. 2002.
[6] X. Sheng and Y. Hu, “Energy Based Acoustic Source Localization,” Proc. Second Int'l Workshop Information Processing in Sensor Networks (IPSN), pp. 286-300, Apr. 2003.
[7] D. Li, K. Wong, Y. Hu, and A. Sayeed, “Detection, Classification, and Tracking of Targets,” IEEE Signal Processing Magazine, vol. 19, no. 3, pp. 17-29, Mar. 2002.
[8] R. Niu and P. Varshney, “Target Location Estimation in Wireless Sensor Networks Using Binary Data,” Proc. 38th Ann. Conf. Information Sciences and Systems, Mar. 2004.
[9] G. Nofsinger, “Tracking Based Plume Detection,” PhD thesis, Dartmouth College, Ha nover, 2006.
[10] B. Krishnamachari and S. Iyengar, “Distributed Bayesian Algorithms for Fault-Tolerant Event Region Detection in Wireless Sensor Networks,” IEEE Trans. Computers, vol. 53, no. 3, pp. 241-250, Mar. 2004.
[11] A. Arora et al. “A Line in the Sand: A Wireless Sensor Network for Target Detection, Classification and Tracking,” Computer Networks, vol. 46, pp. 605-634, 2004.
[12] M. Ding, F. Liu, A. Thaeler, D. Chen, and X. Cheng, “Fault-Tolerant Target Localization in Sensor Networks,” EURASIP J. Wireless Comm. and Networking, vol. 2007, no. 1, p. 19, 2007.
[13] M.P. Michaelides and C.G. Panayiotou, “Subtract on Negative Add on Positive (SNAP) Estimation Algorithm for Sensor Networks,” Proc. Seventh IEEE Int'l Symp. Signal Processing and Information Technology, Dec. 2007.
[14] M.P. Michaelides and C.G. Panayiotou, “Event Detection Using Sensor Networks,” Proc. 45th IEEE Conf. Decision and Control, pp.6784-6789, Dec. 2006.
[15] R. Niu, P. Varshney, M. Moore, and D. Klamer, “Decision Fusion in a Wireless Sensor Network with a Large Number of Sensors,” Proc. Seventh IEEE Int'l Conf. Information Fusion (ICIF '04), June 2004.
[16] S. Ross, Introduction to Probability Models, eighth ed. Academic Press, 2003.
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