article

Discrete stochastic approximation algorithms for design of optimal sensor fusion rules

Authors:

Vikram KrishnamurthyAuthors Info & Claims

International Journal of Sensor Networks, Volume 2, Issue 3/4

Pages 211 - 217

https://doi.org/10.1504/IJSNET.2007.013201

Published: 01 April 2007 Publication History

Abstract

The basic idea of distributed detection is to have a number of independent sensors, each to make a local decision (typically a binary one) and then to combine their decisions at a fusion centre to make a global decision. Fault-tolerance has been considered as one of the main characteristics of wireless sensor networks. A fusion rule in the form of an error correction code has been recently proposed for better fault-tolerance in distributed sensor networks. In this paper, we propose to employ the powerful discrete stochastic approximation techniques to optimise the code matrix, that is, the fusion rule, with the objective of minimising the probability of decision error. We consider both the standard stochastic approximation algorithm and two newly proposed ones for this application. Extensive simulation results are provided to demonstrate the effectiveness of the proposed design paradigm in obtaining optimal fusion rules in distributed wireless sensor networks.

References

[1]

Akyildiz, I.F., Su, W., Sankarasubramaniam, Y. and Cayirci, E. (2002) 'A survey on sensor networks', IEEE Communications Magazine, Vol. 40, No. 8, pp. 102-114.

Digital Library

[2]

Alrefaei, M. and Andradottir, S. (2001) 'A modification of the stochastic ruler method for discrete stochastic optimization', European Journal of Operational Research, Vol. 133, pp. 160-182.

[3]

Andradottir, S. (1995) 'A method for discrete stochastic optimization', Managament Science, Vol. 41, No. 12, pp. 1946-1961.

[4]

Andradottir, S. (1996) 'A global search method for discrete stochastic optimization', SIAM Journal of Optimization, May, Vol. 6, No. 2, pp. 513-530.

[5]

Andradottir, S. (1999) 'Accelerating the convergence of random search methods for discrete stochastic optimization', ACM Transactions on Modeling and Computer Simulation, October, Vol. 9, No. 4, pp. 349-380.

[6]

Berenguer, I., Wang, X. and Krishnamurthy, V. (2005) 'Adaptive MIMO antenna selection via discrete stochastic optimization', IEEE Transactions on Signal Processing, November, Vol. 53, No. 11, pp. 4315-4329.

Digital Library

[7]

Chen, B. and Willet, P.K. (2005) 'On the optimality of the likelihood-ratio test for local sensor decision rules in the presence of nonideal channels', IEEE Transations on Information Theory, Vol. 51, No. 2, pp. 693-699.

Digital Library

[8]

D'Costa, A. and Sayeed, A.M. (2003a) 'Data versus decision fusion in sensor networks', Proceeding of 2003 IEEE International Conferences Acoustics, Speech and Signal Processing, April, Hong Kong, China.

[9]

D'Costa, A. and Sayeed, A.M. (2003b) 'Data versus decision fusion for classification in sensor networks', Proceeding of Sixth International Conference on Information Fusion, July, pp. 889-895.

[10]

Krishnamurthy, V., Wang, X. and Yin, G. (2004) 'Spreading code optimization and adaptation in CDMA via discrete stochastic optimization', IEEE Transactions on Informations Theory, September, Vol. 50, No. 9, pp. 1927-1949.

[11]

Krishnamurthy, V., Vora, T. and Chung, S.H. (to appear) 'Adaptive Brownian dynamics simulation algorithms for shape estimation of membrane ion channels', IEEE Transactions on Nanobioscience.

[12]

Meyer, G.G.L. and Weinert, H.L. (1986) 'On the design of fault-tolerant signal detectors', IEEE Transactions on Acoustics, Speechand, Signal Processing, August, Vol. ASSP-34, pp. 973-978.

[13]

Reibman, A.R. and Nolte, L.W. (1990a) 'Optimal fault-tolerant signal detection', IEEE Transactions on Acoustics, Speech and Signal Processing, January, Vol. 38, No. 1, pp. 179-180.

[14]

Reibman, A.R. and Nolte, L.W. (1990b) 'Optimal design and performance of distributed signal detection systems with faults', IEEE Transactions on Acoustics, Speech and Signal Processing, October, Vol. 38, No. 10, pp. 1771-1782.

[15]

Ross, S. (2002) Simulation, 3rd edition, Academic Press.

[16]

Tenney, R.R. and Sandell Jr., N.R. (1981) Detection with distributed sensors', IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-17, pp. 501-510.

[17]

Thomopoulos, S.C. and Zhang, L. (1992) 'Distributed decision fusion in the presence of networking delays and channel errors', Information Science, December, Vol. 66, No. 1.2, pp. 91-118.

Digital Library

[18]

Wang, T., Han, Y.S., Varshney, P.K. and Chen, P. (2005) 'Distributed fault-tolerant classification in wireless sensor networks', IEEE Journal on Selected Areas in Communications, April, Vol. 23, No. 4, pp. 724-734.

Digital Library

[19]

Zhu, X., Kam, M. and Rorres, C. (1998) 'M-ary hypothesis testing with binary local decisions', Proceeding of 1998 Conference Information Science System, March, pp. 107-112.

Cited By

Liu XLiu XLi ZWang B(2014)The portable distributed fusion algorithm between loss and lossless systemsInternational Journal of Sensor Networks10.1504/IJSNET.2014.06578116:1(16-22)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1504/IJSNET.2014.065781
Miao LChen XZhao SBai Z(2014)Establish the multi-source data fusion model of the shape of blast furnace burden surface based on co-universal kriging estimation methodInternational Journal of Sensor Networks10.1504/IJSNET.2014.06443015:4(231-237)Online publication date: 1-Aug-2014
https://dl.acm.org/doi/10.1504/IJSNET.2014.064430
Yin YZhang CLi Y(2014)A two-stage data fusion model for wireless sensor networksInternational Journal of Sensor Networks10.1504/IJSNET.2014.06389715:3(163-170)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.1504/IJSNET.2014.063897
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Discrete stochastic approximation algorithms for design of optimal sensor fusion rules

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Published In

cover image International Journal of Sensor Networks

International Journal of Sensor Networks Volume 2, Issue 3/4

June 2007

137 pages

ISSN:1748-1279

EISSN:1748-1287

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Publisher

Inderscience Publishers

Geneva 15, Switzerland

Publication History

Published: 01 April 2007

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Cited By

Liu XLiu XLi ZWang B(2014)The portable distributed fusion algorithm between loss and lossless systemsInternational Journal of Sensor Networks10.1504/IJSNET.2014.06578116:1(16-22)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1504/IJSNET.2014.065781
Miao LChen XZhao SBai Z(2014)Establish the multi-source data fusion model of the shape of blast furnace burden surface based on co-universal kriging estimation methodInternational Journal of Sensor Networks10.1504/IJSNET.2014.06443015:4(231-237)Online publication date: 1-Aug-2014
https://dl.acm.org/doi/10.1504/IJSNET.2014.064430
Yin YZhang CLi Y(2014)A two-stage data fusion model for wireless sensor networksInternational Journal of Sensor Networks10.1504/IJSNET.2014.06389715:3(163-170)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.1504/IJSNET.2014.063897
Zou PLiu Y(2014)Low energy WSN data aggregation algorithm based on improved aggregation tree modelInternational Journal of Sensor Networks10.1504/IJSNET.2014.06389515:3(149-156)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.1504/IJSNET.2014.063895
Xiao YZhang YGibson JXie GChen H(2011)Performance analysis of ALOHA and p-persistent ALOHA for multi-hop underwater acoustic sensor networksCluster Computing10.1007/s10586-009-0093-z14:1(65-80)Online publication date: 1-Mar-2011
https://dl.acm.org/doi/10.1007/s10586-009-0093-z

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