This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Fault Tolerance in Collaborative Sensor Networks for Target Detection
March 2004 (vol. 53 no. 3)
pp. 320-333

Abstract—Collaboration in sensor networks must be fault-tolerant due to the harsh environmental conditions in which such networks can be deployed. This paper focuses on finding algorithms for collaborative target detection that are efficient in terms of communication cost, precision, accuracy, and number of faulty sensors tolerable in the network. Two algorithms, namely, value fusion and decision fusion, are identified first. When comparing their performance and communication overhead, decision fusion is found to become superior to value fusion as the ratio of faulty sensors to fault free sensors increases. As robust data fusion requires agreement among nodes in the network, an analysis of fully distributed and hierarchical agreement is also presented. The impact of hierarchical agreement on communication cost and system failure probability is evaluated and a method for determining the number of tolerable faults is identified.

[1] M. Barborak, M. Malek, and A. Dahbura, The Consensus Problem in Fault-Tolerant Computing ACM Computing Surveys, vol. 25, no. 2, pp. 171-220, June 1993.
[2] R. Blum, S. Kassam, and H.V. Poor, Distributed Detection with Multiple Sensors: Part II-Advanced Topics Proc. IEEE, pp. 64-79, Jan. 1997.
[3] J. Bray and C.F. Sturman, Bluetooth: Connect without Cables. Prentice Hall PTR, 2000.
[4] R. Brooks and S. Iyengar, Robust Distributed Computing and Sensing Algorithms Computer, vol. 29, no. 6, pp. 53-60, June 1996.
[5] R.R. Brooks and S.S. Iyengar, Multi-Sensor Fusion: Fundamentals and Applications with Software. Prentice Hall, 1998.
[6] T. Clouqueur, P. Ramanathan, K.K. Saluja, and K.-C. Wang, Value-Fusion versus Decision-Fusion for Fault-Tolerance in Collaborative Target Detection in Sensor Networks Proc. Fourth Ann. Conf. Information Fusion, pp. TuC2/25-TuC2/30, Aug. 2001.
[7] D. Dolev et al., Reaching Approximate Agreement in the Presence of Faults J. ACM, pp. 499-516, July 1986.
[8] D. Dolev, M.J. Fischer, R. Fower, N.A. Lynch, and H.R. Strong, An Efficient Algorithm for Byzantine Agreement without Authentication Information and Control, vol. 52, pp. 257-274, 1982.
[9] D. Dolev and H. Strong, Polynomial Algorithms for Multiple Processor Agreement Proc. 14th ACM Symp. Theory of Computing, pp. 401-407, 1982.
[10] D. Dolev, R. Reischuk, and H.R. Strong, `Eventual' Is Earlier than `Immediate' Proc. 23rd Ann. Symp. Foundations of Computer Science, pp. 196-203, 1982.
[11] M.J. Fischer, The Consensus Problem in Unreliable Distributed Systems (a Brief Survey) Fundamentals of Computation Theory, pp. 127-140, 1983.
[12] J.D. Gibsin and J.L. Melsa, Introduction to Nonparametric Detection with Application. Academic Press, 1975.
[13] G. Grimmett and D. Stirzaker, Probability and Random Processes. Oxford Science Publications, 1992.
[14] D.L. Hall, Mathematical Techniques in Multisensor Data Fusion. Artech house, Inc., 1992.
[15] M. Hata, Empirical Formula for Propagation Loss in Land Mobile Radio Services IEEE Trans. Vehicular Technology, vol. 29, pp. 317-325, Aug. 198.0
[16] IEEE Std 802.11-1997, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, 1997.
[17] I.F. Akyildiz et al., "A Survey on Sensor Networks," IEEE Comm., vol. 40, no. 8, Aug. 2002, pp.102-114.
[18] L. Lamport, R. Shostak, and M. Pease, The Byzantine Generals Problem ACM Trans. Programming Languages and Systems, vol. 4, no. 3, pp. 382-401, July 1982.
[19] C. Lee and J. Chao, Optimum Local Decision Space Partitioning for Distributed Detection IEEE Trans. Aerospace and Electronic Systems, vol. 25, no. 4, pp. 536-544, July 1989.
[20] S. Mahaney and F. Schneider, Inexact Agreement: Accuracy, Precision and Graceful Degradation Proc. Fourth ACM Symp. Principles of Distributed Computing, pp. 237-249, 1985.
[21] M. Pease, R. Shostak, and L. Lamport, Reaching Agreement in the Presence of Faults J. ACM, vol. 27, no. 2, pp. 228-234, Apr. 1980.
[22] L.L. Scharf, Statistical Signal Processing: Detection, Estimation, and Time Series Analysis. Addison-Wesley, 1991.
[23] Sensor Information Technology Website,http://www.darpa.mil/ito/research/sensit index.html, year?
[24] J. Shao, Mathematical Statistics. Springer, 1999.
[25] P.R.U. Shashi, Survey on Sensor Networks year?
[26] K.G. Shin and P. Ramanathan, Diagnosis of Processors with Byzantine Faults in a Distributed Computing System Proc. Fault-Tolerant Computing Symp., pp. 55-60, July 1987.
[27] P. Varshney, Distributed Detection and Data Fusion. New York: Springer-Verlag, 1996.
[28] R. Viswanathan and P. Varshney, Distributed Detection with Multiple Sensors: Part I-Fundamentals Proc. IEEE, pp. 54-63, Jan. 1997.

Index Terms:
Collaborative target detection, decision fusion, fault tolerance, sensor networks, value fusion.
Citation:
Thomas Clouqueur, Kewal K. Saluja, Parameswaran Ramanathan, "Fault Tolerance in Collaborative Sensor Networks for Target Detection," IEEE Transactions on Computers, vol. 53, no. 3, pp. 320-333, March 2004, doi:10.1109/TC.2004.1261838
Usage of this product signifies your acceptance of the Terms of Use.