TY - GEN

T1 - Distributed detection in noisy sensor networks

AU - Kar, Soummya

AU - Tandon, Ravi

AU - Poor, H. Vincent

AU - Cui, Shuguang

PY - 2011/10/26

Y1 - 2011/10/26

N2 - This paper considers distributed detection over a noisy network, in which each connected sensor pair can communicate over an additive noise channel. With non-identically distributed generic sensor observations, a mixed time scale recursive algorithm for binary hypothesis testing over such networks is proposed. Under some mild assumptions on network connectivity and global detectability (the positivity of the global or centralized Kullback-Liebler divergence), this algorithm yields asymptotically zero probabilities of Type-I and Type-II errors (henceforth referred to as probabilities of error). When sensor observations are identically distributed, a simplified single time scale version of the proposed algorithm is shown to achieve asymptotically zero probabilities of error. Convergence rate guarantees in terms of asymptotic normality of certain scaled decision variables are provided for this simplified procedure. As an example, a practical Gaussian sensor network is considered, for which the error decay exponents are explicitly characterized in terms of the network and noise parameters.

AB - This paper considers distributed detection over a noisy network, in which each connected sensor pair can communicate over an additive noise channel. With non-identically distributed generic sensor observations, a mixed time scale recursive algorithm for binary hypothesis testing over such networks is proposed. Under some mild assumptions on network connectivity and global detectability (the positivity of the global or centralized Kullback-Liebler divergence), this algorithm yields asymptotically zero probabilities of Type-I and Type-II errors (henceforth referred to as probabilities of error). When sensor observations are identically distributed, a simplified single time scale version of the proposed algorithm is shown to achieve asymptotically zero probabilities of error. Convergence rate guarantees in terms of asymptotic normality of certain scaled decision variables are provided for this simplified procedure. As an example, a practical Gaussian sensor network is considered, for which the error decay exponents are explicitly characterized in terms of the network and noise parameters.

UR - http://www.scopus.com/inward/record.url?scp=80054820086&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80054820086&partnerID=8YFLogxK

U2 - 10.1109/ISIT.2011.6034097

DO - 10.1109/ISIT.2011.6034097

M3 - Conference contribution

AN - SCOPUS:80054820086

SN - 9781457705953

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 2856

EP - 2860

BT - 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011

T2 - 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011

Y2 - 31 July 2011 through 5 August 2011

ER -