Distributed Bayesian estimation of arrival rates in asynchronous monitoring networks

In this paper we consider a network of agents monitoring a spatially distributed traffic process. Each node measures the number of arrivals seen at its monitoring point in a given time-interval. We propose an asynchronous distributed approach based on a hierarchical Bayes model with unknown hyperparameter, which allows each node to compute the minimum mean square error (MMSE) estimator of the local arrival rate by suitably fusing the information from the whole network. Simulation results show that the distributed scheme improves the estimation accuracy compared to a purely decentralized setup and is reliable even in presence of limited local data. An ad-hoc algorithm with reduced complexity is also proposed, which performs very closely to the optimal MMSE estimator.