Fault tolerance in collaborative sensor networks for target detection

Collaboration in sensor networks must be fault-tolerant due to the harsh environmental conditions in which such networks can be deployed. We focus 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]  Danny Dolev,et al.  'Eventual' is earlier than 'immediate' , 1982, 23rd Annual Symposium on Foundations of Computer Science (sfcs 1982).

[2]  M. Hata,et al.  Empirical formula for propagation loss in land mobile radio services , 1980, IEEE Transactions on Vehicular Technology.

[3]  S. Sitharama Iyengar,et al.  Robust Distributed Comupting and Sensing Algorithm , 1996, Computer.

[4]  BarborakMichael,et al.  The consensus problem in fault-tolerant computing , 1993 .

[5]  Ali Esmaili,et al.  Probability and Random Processes , 2005, Technometrics.

[6]  Edward J. Wegman,et al.  Statistical Signal Processing , 1985 .

[7]  Nancy A. Lynch,et al.  An Efficient Algorithm for Byzantine Agreement without Authentication , 1982, Inf. Control..

[8]  S. Iyengar,et al.  Multi-Sensor Fusion: Fundamentals and Applications With Software , 1997 .

[9]  Fred B. Schneider,et al.  Inexact agreement: accuracy, precision, and graceful degradation , 1985, PODC '85.

[10]  M. Kanefsky.  Introduction to nonparametric detection with applications , 1977, Proceedings of the IEEE.

[11]  Nancy A. Lynch,et al.  Reaching approximate agreement in the presence of faults , 1986, JACM.

[12]  Leslie Lamport,et al.  The Byzantine Generals Problem , 1982, TOPL.

[13]  L. Scharf,et al.  Statistical Signal Processing: Detection, Estimation, and Time Series Analysis , 1991 .

[14]  Michael J. Fischer,et al.  The Consensus Problem in Unreliable Distributed Systems (A Brief Survey) , 1983, FCT.

[15]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[16]  Leslie Lamport,et al.  Reaching Agreement in the Presence of Faults , 1980, JACM.

[17]  Jennifer Bray,et al.  Bluetooth: Connect Without Cables , 2000 .

[18]  Pramod K. Varshney,et al.  Distributed Detection and Data Fusion , 1996 .

[19]  Jerry D. Gibson,et al.  Introduction to Nonparametric Detection with Applications , 1995 .

[20]  R. F.,et al.  Mathematical Statistics , 1944, Nature.

[21]  Danny Dolev,et al.  Polynomial algorithms for multiple processor agreement , 1982, STOC '82.

[22]  D. L. Hall,et al.  Mathematical Techniques in Multisensor Data Fusion , 1992 .

[23]  Rick S. Blum,et al.  Distributed detection with multiple sensors I. Advanced topics , 1997, Proc. IEEE.

[24]  Pramod K. Varshney,et al.  Distributed detection with multiple sensors I. Fundamentals , 1997, Proc. IEEE.

[25]  Voon Chin Phua,et al.  Wireless lan medium access control (mac) and physical layer (phy) specifications , 1999 .

[26]  Chung Chieh Lee,et al.  Optimum local decision space partitioning for distributed detection , 1989 .

[27]  Kang G. Shin,et al.  DIAGNOSIS OF PROCESSORS WITH BYZANTINE FAULTS IN A DISTRIBUTED COMPUTING SYSTEM. , 1987 .

[28]  Kuang-Ching Wang,et al.  Value-Fusion versus Decision-Fusion for Fault-tolerance in Collaborative Target Detection in Sensor Networks , 2001 .