Decentralized Sequential Change Detection Using Physical Layer Fusion

We study the problem of decentralized sequential change detection with conditionally independent observations. The sensors form a star topology with a central node called fusion center as the hub. The sensors transmit a simple function of their observations in an analog fashion over a wireless Gaussian multiple access channel and operate under either a power constraint or an energy constraint. Simulations demonstrate that the proposed techniques have lower detection delays when compared with existing schemes. Moreover we demonstrate that the energy-constrained formulation enables better use of the total available energy than a power-constrained formulation.

[1]  E. S. Page.  CONTINUOUS INSPECTION SCHEMES , 1954 .

[2]  Alʹbert Nikolaevich Shiri︠a︡ev,et al.  Optimal stopping rules , 1977 .

[3]  Michael Gastpar,et al.  Scaling Laws for Homogeneous Sensor Networks , 2003 .

[4]  Ke Liu,et al.  Type-Based Decentralized Detection in Wireless Sensor Networks , 2007, IEEE Transactions on Signal Processing.

[5]  Toby Berger,et al.  Rate distortion theory : a mathematical basis for data compression , 1971 .

[6]  David K. Smith,et al.  Dynamic Programming and Optimal Control. Volume 1 , 1996 .

[7]  Amos Lapidoth,et al.  Sending a Bi-Variate Gaussian Source over a Gaussian MAC , 2006, 2006 IEEE International Symposium on Information Theory.

[8]  H. Vincent Poor,et al.  Decentralized Sequential Detection with a Fusion Center Performing the Sequential Test , 1992, 1992 American Control Conference.

[9]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[10]  Lang Tong,et al.  Asymptotic Detection Performance of Type-Based Multiple Access Over Multiaccess Fading Channels , 2007, IEEE Transactions on Signal Processing.

[11]  G. Lorden.  PROCEDURES FOR REACTING TO A CHANGE IN DISTRIBUTION , 1971 .

[12]  Raghuraman Mudumbai,et al.  On the Feasibility of Distributed Beamforming in Wireless Networks , 2007, IEEE Transactions on Wireless Communications.

[13]  Dimitri P. Bertsekas,et al.  Stochastic optimal control : the discrete time case , 2007 .

[14]  Lang Tong,et al.  Type based estimation over multiaccess channels , 2006, IEEE Transactions on Signal Processing.

[15]  Rajesh Sundaresan,et al.  Separable Convex Optimization Problems with Linear Ascending Constraints , 2007, SIAM J. Optim..

[16]  Ke Liu,et al.  Decentralized Inference Over Multiple-Access Channels , 2007, IEEE Transactions on Signal Processing.

[17]  Anurag Kumar,et al.  Optimizing Delay in Sequential Change Detection on Ad Hoc Wireless Sensor Networks , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.

[18]  Michael Gastpar,et al.  To code, or not to code: lossy source-channel communication revisited , 2003, IEEE Trans. Inf. Theory.

[19]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[20]  S. M. Ali,et al.  A General Class of Coefficients of Divergence of One Distribution from Another , 1966 .

[21]  Venugopal V. Veeravalli.  Decentralized quickest change detection , 2001, IEEE Trans. Inf. Theory.

[22]  Michael Gastpar,et al.  Power, spatio-temporal bandwidth, and distortion in large sensor networks , 2005, IEEE Journal on Selected Areas in Communications.