Network lifetime maximization for estimation in multihop wireless sensor networks
IEEE Transactions on Signal Processing
Power constrained distributed estimation with correlated sensor data
IEEE Transactions on Signal Processing
Distributed estimation in energy-constrained wireless sensor networks
IEEE Transactions on Signal Processing
Power constrained distributed estimation with cluster-based sensor collaboration
IEEE Transactions on Wireless Communications
Hyperplane-based vector quantization for distributed estimation in wireless sensor networks
IEEE Transactions on Information Theory
IEEE Transactions on Signal Processing
Linear coherent distributed estimation over unknown channels
Signal Processing
| Hi-index | 35.76 |
We study the problem of minimal-energy decentralized estimation via sensor networks with the best-linear-unbiased-estimator fusion rule. While most of the existing solutions require the knowledge of instantaneous noise variances for energy allocation, the proposed approach instead relies on an associated statistical model. The minimization of total energy is subject to a performance constraint in terms of the reciprocal of mean square errors averaged over the considered distribution. A closed-form formula for such a mean distortion metric, as well as an associated tractable lower bound, is derived. By imposing a target distortion constraint in terms of this bound and further through feasible set relaxation, the problem can be reformulated in the form of convex optimization and is then analytically solved. The proposed method shares several attractive features of the existing designs via instantaneous noise variances. Through simulations it is seen to significantly improve the energy efficiency against the uniform allocation scheme.