On distributed fault-tolerant detection in wireless sensor networks

In this paper, we consider two important problems for distributed fault-tolerant detection in wireless sensor networks: 1) how to address both the noise-related measurement error and sensor fault simultaneously in fault-tolerant detection and 2) how to choose a proper neighborhood size n for a sensor node in fault correction such that the energy could be conserved. We propose a fault-tolerant detection scheme that explicitly introduces the sensor fault probability into the optimal event detection process. We mathematically show that the optimal detection error decreases exponentially with the increase of the neighborhood size. Experiments with both Bayesian and Neyman-Pearson approaches in simulated sensor networks demonstrate that the proposed algorithm is able to achieve better detection and better balance between detection accuracy and energy usage. Our work makes it possible to perform energy-efficient fault-tolerant detection in a wireless sensor network.

[1]  Gregory J. Pottie,et al.  Wireless integrated network sensors , 2000, Commun. ACM.

[2]  W. Hoeffding.  Probability Inequalities for sums of Bounded Random Variables , 1963 .

[3]  V. Ramachandran,et al.  Distributed classification of Gaussian space-time sources in wireless sensor networks , 2004, IEEE Journal on Selected Areas in Communications.

[4]  Ramesh Govindan,et al.  Localized edge detection in sensor fields , 2003, Ad Hoc Networks.

[5]  Venugopal V. Veeravalli,et al.  Decentralized detection in sensor networks , 2003, IEEE Trans. Signal Process..

[6]  Deborah Estrin,et al.  GPS-less low-cost outdoor localization for very small devices , 2000, IEEE Wirel. Commun..

[7]  Urbashi Mitra,et al.  Boundary Estimation in Sensor Networks: Theory and Methods , 2003, IPSN.

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

[9]  Wendi Heinzelman,et al.  Proceedings of the 33rd Hawaii International Conference on System Sciences- 2000 Energy-Efficient Communication Protocol for Wireless Microsensor Networks , 2022 .

[10]  Matt Welsh,et al.  Simulating the power consumption of large-scale sensor network applications , 2004, SenSys '04.

[11]  Richard D. Wesel,et al.  Optimal bi-level quantization of i.i.d. sensor observations for binary hypothesis testing , 2002, IEEE Trans. Inf. Theory.

[12]  Nael B. Abu-Ghazaleh,et al.  Infrastructure tradeoffs for sensor networks , 2002, WSNA '02.

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

[14]  J. Tsitsiklis.  Decentralized Detection' , 1993 .

[15]  S. Sitharama Iyengar,et al.  Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks , 2004, IEEE Transactions on Computers.

[16]  Richard D. Wesel,et al.  Quasi-convexity and optimal binary fusion for distributed detection with identical sensors in generalized Gaussian noise , 2001, IEEE Trans. Inf. Theory.

[17]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[18]  John W. Byers,et al.  Utility-based decision-making in wireless sensor networks , 2000, MobiHoc.

[19]  Vijay Kumar,et al.  Environmental monitoring using wireless sensors , 2004 .

[20]  Parameswaran Ramanathan,et al.  Fault tolerance in collaborative sensor networks for target detection , 2004, IEEE Transactions on Computers.

[21]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[22]  H. Chernoff.  A Measure of Asymptotic Efficiency for Tests of a Hypothesis Based on the sum of Observations , 1952 .

[23]  Ramanarayanan Viswanathan,et al.  Optimal distributed decision fusion , 1989 .