Analysis of Noise Impact on Distributed Average Consensus

This paper considers the problem of distributed average consensus in a noisy sensor network in which noise will cause error bits and detriment the accuracy of the results. We use a bit-flipping model to model the noise effect and show that it will lead to biased results. We propose here an unbiased average consensus algorithm for noisy networks with dynamic topologies. We analyze the convergence speed and the mean square error and show that the noise can be suppressed by our method. The proposed algorithm is found effective in a network simulation with and without perfect bit error rate information.