Distributed Averaging in Dense Wireless Networks

The authors consider the effect of network throughput on the convergence of a specific class of distributed averaging algorithms, called consensus algorithms. These algorithms rely on iterative computation of the desired average by message passing among the nodes. It is thus assumed that the rate of convergence should benefit from greater network connectivity. However, one must also account for the additional network resources that establishing such a connectivity would entail. In this paper, they study this problem in the context of randomly-placed consensus-seeking nodes that are connected through a dense wireless network, i.e., whose capacity is interference-limited.