Average consensus in asymmetric broadcasting wireless sensor networks through gossiping

Average consensus algorithms have attracted increasing interest in the last decade because of their potential for use in high-density wireless sensor networks. This paper analyzes an algorithm that is based on a model of asymmetric broadcasting on a random geometric graph, in which nodes broadcast and listen only intermittently. We show that each node can easily estimate its update weight from the degree of silence in its neighborhood, and, after the weights have been assigned in this way, that the consensus algorithm converges to the true average. Both a synchronous and an asynchronous update model are analyzed.