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 estimator from the previous node. Since all nodes in the network may not carry useful information, methodologies to find the optimal 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 objective function. It is shown that the performance of both proposed greed type schemes leads to exact, or close to exact, resu...

• Type: Journal
• Year: 2010
• Where: TWC
• Authors: Thakshila Wimalajeewa, Sudharman K. Jayaweera
• Keywords: Artificial Intelligence, Current Node, Node, Sequential Estimation Process, TWC 2010