Average consensus in sensor networks via broadcast multi-gossip algorithms

Motivated by applications to wireless sensor, peer-to-peer, and ad hoc networks, we propose a distributed algorithm called broadcast based multi-gossiping algorithm (BMGA), which is designed for exchanging information and computing in an arbitrarily connected network of nodes. Unlike traditional randomized gossip algorithms, push-sum mechanism based BMGA preserves the sums and weights, and admits stochastic spreading, we derive a lower bound on the weight, and give an approximate value for this bound. By introducing a potential function, we show that BMGA converges almost surely to the average of initial node measurements with probability one. Specifically, we further provide the upper bounds on the diffusion speed, E-convergence time and the number of radio transmissions. Finally, we present a numerical example to assess and compare the communication cost with several gossip-based algorithms to achieve a given performance. & 2013 Elsevier B.V. All rights reserved.