Distributed consensus algorithms for estimation of parameters or detection of events in wireless sensor networks have attracted considerable attention in recent years. A necessary condition to achieve a consensus on the average of the initial
values is that the topology of the underlying graph is balanced or symmetric at every time instant. However, communication
impairments can make the topology vary randomly in time, and instantaneous link symmetry between pairs of nodes is not
guaranteed unless an acknowledgment protocol or an equivalent approach is implemented. In this paper, we evaluate the convergence of the consensus algorithm in the mean square sense in wireless sensor networks with random asymmetric topologies. For the case of links with equal probability of connection, a closed form expression for the mean square error of the state along with
the dynamical range and the optimum value of the link weights that guarantee convergence are derived. For the case of links with
different probabilities of connection, an upper bound for the mean square error of the state is derived. This upper bound can be
computed for any time instant and can be employed to compute a link weight that reduces the convergence time of the algorithm.
CitationSilva, S.; Pages, A. Mean square convergence of consensus algorithms in random WSNs. "IEEE transactions on signal processing", Maig 2010, vol. 58, núm. 5, p. 2866-2874.
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