Decentralized estimation over noisy channels in cluster-based wireless sensor networks

In this paper, the problem of decentralized parameter estimation over noisy channels in a cluster-based sensor network is studied. Each cluster head generates a local estimate by adopting a sample mean estimator. The local estimates from all cluster heads are compressed by using a one-bit quantizer and then the bits are transmitted to a fusion center over independent binary symmetric channels. Two maximum likelihood estimators are proposed for estimating the parameter based on the received bits in two different scenarios: (i) all clusters have the same number of cluster members and (ii) the number of cluster members in the clusters are different. The Cramer–Rao lower bounds for the proposed estimators in both scenarios are derived. Simulation results show that the estimation performance for clustering with the same number of cluster members can be comparable to that for cluster-free sensor networks. However, the estimation performance for clustering with a different number of cluster members is not as good as that for cluster-free sensor networks. The trade off between estimation performance and energy consumption exists in both scenarios. The energy consumed for executing the estimation task in cluster-based sensor networks is less than that in cluster-free sensor networks. Copyright © 2011 John Wiley & Sons, Ltd.

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