A distributed randomized algorithm for relative localization in sensor networks

This paper regards the relative localization problem in sensor networks. We propose for its solution a distributed randomized algorithm, which is based on input-driven consensus dynamics and features pairwise “gossip” communications and updates. Due to the randomness of the updates, the state of this algorithm oscillates in time around a certain limit value. We show that the time-average of the state asymptotically converges, in the mean-square sense, to the least-squares solution of the localization problem. Furthermore, we describe an update scheme ensuring that the time-averaging process is accomplished in a fully distributed way.