Adaptive Sensor Networks for Consensus Based Distributed Estimation

In this paper consensus based algorithms for distributed estimation in sensor networks are discussed and a new algorithm with decentralized adaptation is proposed for solving the problem where the state of a monitored process is observed only by a relatively small percentage of the sensors at each iteration of the algorithm. The given analysis shows that adaptation of the gains in the consensus scheme is of crucial importance for getting simple yet efficient estimation algorithms. It is also shown that the exchange of an additional binary information between the nodes on whether or not a node has received the observation, along with the information on state estimates, is sufficient to obtain a robust and efficient tool for practice. Selected examples illustrate performance of the proposed algorithm in terms of the mean square estimation error and the disagreement between the nodes.