Distributed Detection in Sensor Networks with Limited Range Sensors

We consider a multi-target detection problem over a sensor network (SNET) with limited range sensors and communication constraints. This problem complements the widely considered decentralized detection problem where all sensors observe the same target. While the necessity for global collaboration is clear in the decentralized detection problem, the benefits of collaboration with limited range sensors is unclear and has not been widely explored. In this paper we develop a distributed detection approach based on recent development of the false discovery rate (FDR). We show that collaboration among sensors can not only be beneficial, but also necessary, even in the limit of non-overlapping sensing regions for each sensor. Our scheme characterizes the optimal achievable performance, i.e., maximizes the detection rate subject to false discovery rate and communication constraints. We further demonstrate scalability to large SNETs by showing that the upper bound on the communication complexity scales linearly with the number of actual targets and is independent of the number of sensors. Finally, we deal with situations where the sensing model may be uncertain and establish robustness of our techniques to such uncertainties.