On Decentralized Detection With Partial Information Sharing Among Sensors

• Published in: IEEE transactions on signal processing 2011-04, Vol.59 (4), p.1759-1765
• Main Authors: Kreidl, O P, Tsitsiklis, J N, Zoumpoulis, S I
• Format: Article
• Language: English
• Subjects: Applied sciences; Architecture; Asymptotic properties; Bayesian methods; Broadcasting; Decentralized; Decentralized detection; Detection, estimation, filtering, equalization, prediction; error exponent; Exact sciences and technology; Exponents; Information, signal and communications theory; Messages; nontree network; Optimization; Permissible error; Probabilistic logic; Random variables; Sensor fusion; sensor networks; Sensors; Signal and communications theory; Signal, noise; Telecommunications and information theory; Wireless sensor networks
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2010.2099223

We study a decentralized detection architecture in which each of a set of sensors transmits a highly compressed summary of its observations (a binary message) to a fusion center, which then decides on one of two alternative hypotheses. In contrast to the star (or "parallel") architecture considered in most of the literature, we allow a subset of the sensors to both transmit their messages to the fusion center and to also broadcast them to the remaining sensors. We focus on the following architectural question: Is there a significant performance improvement when we allow such a message broadcast? We consider the error exponent (asymptotically, in the limit of a large number of sensors) for the Neyman-Pearson formulation of the detection problem. We prove that the sharing of messages does not improve the optimal error exponent.