Energy Scaling Laws for Distributed Inference in Random Fusion Networks

TL;DR

This paper derives energy scaling laws for distributed inference in random sensor networks, showing bounds and conditions under which energy consumption remains finite for Markov random field models.

Contribution

It introduces and analyzes the energy scaling laws for optimal and suboptimal data fusion schemes in randomly deployed sensor networks for MRF hypotheses.

Findings

Energy bounds are established for fusion schemes.

DFMRF scheme has finite average energy for certain MRFs.

Scaling laws describe how energy consumption grows with network size.

Abstract

The energy scaling laws of multihop data fusion networks for distributed inference are considered. The fusion network consists of randomly located sensors distributed i.i.d. 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 (MRF) hypotheses, the scheme with minimum average energy consumption is bounded below by average energy of fusion along the minimum spanning tree, and above by a suboptimal scheme, referred to as Data Fusion for Markov Random Fields (DFMRF). Scaling laws are derived for the optimal and suboptimal fusion policies. It is shown that the average asymptotic energy of the DFMRF scheme is finite for a class of MRF models.