On the Impact of Node Failures and Unreliable Communications in Dense Sensor Networks

We consider the problem of decentralized detection in failure-prone tree networks with bounded height. Specifically, we study and contrast the impact on the detection performance of either node failures (modeled by a Galton-Watson branching process) or unreliable communications (modeled by binary symmetric channels). In both cases, we focus on ldquodenserdquo networks, in which we let the degree of every node (other than the leaves) become large, and we characterize the asymptotically optimal detection performance. We develop simple strategies that nearly achieve the optimal performance, and compare the performance of the two types of networks.

[1]  Nils Sandell,et al.  Detection with Distributed Sensors , 1980, IEEE Transactions on Aerospace and Electronic Systems.

[2]  L. Ekchian,et al.  Detection networks , 1982, 1982 21st IEEE Conference on Decision and Control.

[3]  John N. Tsitsiklis,et al.  On the complexity of decentralized decision making and detection problems , 1985 .

[4]  P.K. Varshney,et al.  Optimal Data Fusion in Multiple Sensor Detection Systems , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[5]  L.W. Nolte,et al.  Design and Performance Comparison of Distributed Detection Networks , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[6]  John N. Tsitsiklis,et al.  Decentralized detection by a large number of sensors , 1988, Math. Control. Signals Syst..

[7]  J. Tsitsiklis,et al.  Explicit Solutions for Some Simple Decentralized Detection Problems , 1990, 1989 American Control Conference.

[8]  R. Durrett.  Probability: Theory and Examples , 1993 .

[9]  Michael. Athans,et al.  On optimal distributed decision architectures in a hypothesis testing environment , 1992 .

[10]  D. Kleinman,et al.  Optimization of detection networks. I. Tandem structures , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[11]  D. Kleinman,et al.  Optimization of Detection Networks: Part 11-Tree Structures , 1993 .

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

[13]  D. Kleinman,et al.  Optimization of detection networks with multiple event structures , 1994, IEEE Trans. Autom. Control..

[14]  W. W. Irving,et al.  Some properties of optimal thresholds in decentralized detection , 1994, IEEE Trans. Autom. Control..

[15]  Pramod K. Varshney,et al.  A unified approach to the design of decentralized detection systems , 1995 .

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

[17]  Amir Dembo,et al.  Large Deviations Techniques and Applications , 1998 .

[18]  Pramod K. Varshney,et al.  A Bayesian sampling approach to decision fusion using hierarchical models , 2002, IEEE Trans. Signal Process..

[19]  Klaus Kursawe,et al.  Optimistic Byzantine agreement , 2002, 21st IEEE Symposium on Reliable Distributed Systems, 2002. Proceedings..

[20]  Anantha Chandrakasan,et al.  MobiCom poster: top five myths about the energy consumption of wireless communication , 2003, MOCO.

[21]  P.K. Varshney,et al.  Decision fusion rules in multi-hop wireless sensor networks , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[22]  Pramod K. Varshney,et al.  Distributed Detection and Fusion in a Large Wireless Sensor Network of Random Size , 2005, EURASIP J. Wirel. Commun. Netw..

[23]  Wenjun Li,et al.  Distributed Detection in Large-Scale Sensor Networks with Correlated Sensor Observations , 2005 .

[24]  Lang Tong,et al.  A Large Deviation Analysis of Detection Over Multi-Access Channels with Random Number of Sensors , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[25]  Venugopal V. Veeravalli,et al.  How Dense Should a Sensor Network Be for Detection With Correlated Observations? , 2006, IEEE Transactions on Information Theory.

[26]  Bin Liu,et al.  Channel-optimized quantizers for decentralized detection in sensor networks , 2006, IEEE Transactions on Information Theory.

[27]  Akshay Kashyap.  Comments on "On the optimality of the likelihood-ratio test for local sensor decision rules in the presence of nonideal channels" , 2006, IEEE Trans. Inf. Theory.

[28]  Moe Z. Win,et al.  Energy Efficiency of Dense Wireless Sensor Networks: To Cooperate or Not to Cooperate , 2006, 2006 IEEE International Conference on Communications.

[29]  Lang Tong,et al.  Distributed Inference in the Presence of Byzantine Sensors , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[30]  Anders Ahlén,et al.  Two Hops is One too Many in an Energy-Limited Wireless Sensor Network , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[31]  Moe Z. Win,et al.  On the Subexponential Decay of Detection Error Probabilities in Long Tandems , 2007, IEEE Transactions on Information Theory.

[32]  Moe Z. Win,et al.  Data Fusion Trees for Detection: Does Architecture Matter? , 2008, IEEE Transactions on Information Theory.

[33]  Peter Willett,et al.  On the optimality of the likelihood-ratio test for local sensor decision rules in the presence of nonideal channels , 2005, IEEE Transactions on Information Theory.

[34]  Moe Z. Win,et al.  Bayesian Detection in Bounded Height Tree Networks , 2007, IEEE Transactions on Signal Processing.