Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Minimum energy decentralized estimation in a wireless sensor network with correlated sensor noises
EURASIP Journal on Wireless Communications and Networking
IEEE Transactions on Signal Processing - Part II
IEEE Transactions on Signal Processing
Rate-Constrained Distributed Estimation in Wireless Sensor Networks
IEEE Transactions on Signal Processing
Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case
IEEE Transactions on Signal Processing
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
Energy-constrained modulation optimization
IEEE Transactions on Wireless Communications
Sequential signal encoding from noisy measurements using quantizers with dynamic bias control
IEEE Transactions on Information Theory
Universal decentralized estimation in a bandwidth constrained sensor network
IEEE Transactions on Information Theory
Decentralized estimation in an inhomogeneous sensing environment
IEEE Transactions on Information Theory
Network lifetime maximization for estimation in multihop wireless sensor networks
IEEE Transactions on Signal Processing
Practical data compression in wireless sensor networks: A survey
Journal of Network and Computer Applications
Multi-rate distributed fusion estimation for sensor networks with packet losses
Automatica (Journal of IFAC)
Hi-index | 35.69 |
In this paper, we consider distributed estimation of a noise-corrupted deterministic parameter in energy-constrained wireless sensor networks from energy-distortion perspective. Given a total energy budget allowable to be used by all sensors, there exists a tradeoff between the subset of active sensors and the energy used by each active sensor in order to minimize the estimation MSE. To determine the optimal quantization bit rate and transmission energy of each sensor, a concept of equivalent unit-energy MSE function is introduced. Based on this concept, an optimal energy-constrained distributed estimation algorithm for homogeneous sensor networks and a quasi-optimal energy-constrained distributed estimation algorithm for heterogeneous sensor networks are proposed. Moreover, the theoretical energy-distortion performance bound for distributed estimation is addressed and it is shown that the proposed algorithm is quasi-optimal within a factor 2 of the theoretical lower bound. Simulation results also show that the proposed method can achieve a significant reduction in the estimation MSE when compared with other uniform schemes. Finally, the proposed algorithm is easy to implement in a distributed manner and it adapts well to the dynamic sensor environments.