Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Convex Optimization
Group-Based Trust Management Scheme for Clustered Wireless Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Energy planning for progressive estimation in multihop sensor networks
IEEE Transactions on Signal Processing
Power constrained distributed estimation with cluster-based sensor collaboration
IEEE Transactions on Wireless Communications
Minimizing effective energy consumption in multi-cluster sensor networks for source extraction
IEEE Transactions on Wireless Communications
Estimation over fading channels with limited feedback using distributed sensing
IEEE Transactions on Signal Processing
Energy-efficient cluster-based distributed estimation in wireless sensor networks
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Distributed EM algorithms for density estimation and clustering in sensor networks
IEEE Transactions on Signal Processing
Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case
IEEE Transactions on Signal Processing
Power scheduling of universal decentralized estimation in sensor networks
IEEE Transactions on Signal Processing
Constrained Decentralized Estimation Over Noisy Channels for Sensor Networks
IEEE Transactions on Signal Processing
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
Distributed estimation over fading channels using one-bit quantization
IEEE Transactions on Wireless Communications
Decentralized estimation in an inhomogeneous sensing environment
IEEE Transactions on Information Theory
Multiterminal Source–Channel Communication Over an Orthogonal Multiple-Access Channel
IEEE Transactions on Information Theory
On rate-constrained distributed estimation in unreliable sensor networks
IEEE Journal on Selected Areas in Communications
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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 Cramér–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.