  We consider a multi-object detection problem over a sensor network (SNET)
with limited range multi-modal sensors. Limited range sensing environment
arises in a sensing field prone to signal attenuation and path losses. The
general problem complements the widely considered decentralized detection
problem where all sensors observe the same object. In this paper we develop a
distributed detection approach based on recent development of the false
discovery rate (FDR) and the associated BH test procedure. The BH procedure is
based on rank ordering of scalar test statistics. We first develop scalar test
statistics for multidimensional data to handle multi-modal sensor observations
and establish its optimality in terms of the BH procedure. We then propose a
distributed algorithm in the ideal case of infinite attenuation for
identification of sensors that are in the immediate vicinity of an object. We
demonstrate communication message scalability to large SNETs by showing that
the upper bound on the communication message complexity scales linearly with
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 finite attenuation by modeling sensor observations as corrupted by
uncertain interference arising from distant objects and developing robust
extensions to our idealized distributed scheme. The robustness properties
ensure that both the error performance and communication message complexity
degrade gracefully with interference.
