Distributed Detection in Sensor Networks With Limited Range Multimodal Sensors

We consider a multiobject detection problem over a sensor network (SNET) with limited range multimodal sensors. The general problem complements the widely considered decentralized detection problem where all sensors observe the same object. We develop a distributed detection approach based on recent development of the false discovery rate (FDR) and the associated Benjamini-Hochberg (BH) procedure, which rank orders scalar test statistics. We first develop scalar test statistics for multidimensional data to handle multimodal sensor observations and establish its optimality in terms of the BH procedure. We then propose a distributed algorithm for an idealized model to detect the sensors that are in the immediate vicinity of an object. We show that the number of binary messages that need to be transmitted (communication cost) is upper bounded by the number of sensors that are in the vicinity of objects and is independent of the total number of sensors in the SNET. This brings forth an important principle for evaluating the performance of an SNET, namely, the need for scalability of communications and performance with respect to the number of objects or events in an SNET irrespective of the network size. We then account for nonideal models by developing robust extensions to our developments under the idealized model. The robustness properties ensure that both the error performance and communication cost degrade gracefully with interference.