Nearly optimal distributed configuration management using probabilistic graphical models

This work studies distributed configuration management of large wireless sensor networks, where management objectives are achieved by local cooperation of individual nodes. Specifically, we study when distributed configuration management is nearly optimal, and how to obtain a nearly-optimal configuration through decentralized adaptation. We first derive a spatial network model that is determined by internal network characteristics and management requirements. We next show that a sufficient condition for distributed configuration management to be nearly-optimal is that the spatial network model belongs to a class of coupled Markov random fields also known as random-bond ising model. Such graphs possess a cross-layer spatial Markov property. We specify the sufficient conditions for the nearly-optimality under different channels and density of nodes. We derive a nearly-optimal distributed algorithm using the probabilistic inference based on the derived network model. The algorithm is applied to spatial-reuse TDMA which configures a logical topology