Energy Scaling Laws for Distributed Inference in Random Networks

The energy scaling laws of multihop data fusion networks for distributed inference are considered. The fusion network consists of randomly located sensors independently distributed according to a general spatial distribution in an expanding region. Among the class of data fusion schemes that enable optimal inference at the fusion center for Markov random field hypotheses, the minimum per-sensor energy cost is bounded below by a minimum spanning tree data fusion and above by a suboptimal scheme referred to as Data Fusion for Markov Random Field (DFMRF). Scaling laws are derived for the optimal and suboptimal fusion policies.