An SMF approach to distributed average consensus in clustered sensor networks

Distributed sensor networks employ multiple nodes to collectively estimate or track parameter(s) of interest without any central fusion node. Individual nodes may observe (sense) and estimate the parameter of concern as well as cooperate with other nodes to arrive at a global consensus estimate. We propose a simple heuristic algorithm using a set-membership filtering approach to adaptively determine the weights of an average consensus estimator in a clustered network. Here, all the nodes in a cluster, called clustermembers, send their estimates to a clusterhead which computes the average consensus estimate. In this approach, the nodes with low signal-to-noise ratios are tagged as noisy and their estimates are accordingly given less weight. Simulation results show the ability of the proposed scheme to effectively weigh the estimates according to their SNRs to yield performance similar to a best linear unbiased estimator.