Distributed Node Selection for Sequential Estimation over Noisy Communication Channels

This paper proposes a framework for distributed sequential parameter estimation in wireless sensor networks. In the proposed scheme, the estimator is updated sequentially at the current node with its new measurement and the noisy corrupted local estimator from the previous node. Since all nodes in the network may not carry useful information, methodologies to find the best set of nodes and the corresponding node ordering for the sequential estimation process are investigated. It is shown that the determining the optimal set of nodes that leads to the globally optimal performance is computationally complex when the network size is large. We develop two distributed greedy type node selection algorithms with reduced computational and communication complexities. In these algorithms, the next best node is selected at the current node such that it optimizes a certain reward function. It is shown that the performance of both proposed greed type schemes leads to exact, or close to exact, results to the optimal scheme computed via forward dynamic programming, under certain conditions. Moreover, contrast to existing methodologies, our work considers the node selection and inter-node communication noise jointly in the sequential estimation process.