IEEE Transactions on Signal Processing
Network lifetime maximization for estimation in multihop wireless sensor networks
IEEE Transactions on Signal Processing
Distributed estimation in energy-constrained wireless sensor networks
IEEE Transactions on Signal Processing
Energy planning for progressive estimation in multihop sensor networks
IEEE Transactions on Signal Processing
Gossip consensus algorithms via quantized communication
Automatica (Journal of IFAC)
Optimal rate allocation for multi-sensor distributed estimation
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Quantization, channel compensation, and energy allocation for estimation in wireless sensor networks
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
State estimation with quantized measurements in wireless sensor networks
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
State estimation with quantized measurements in wireless sensor networks
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Brief paper: Min-max optimal data encoding and fusion in sensor networks
Automatica (Journal of IFAC)
IEEE Transactions on Signal Processing
Signal recovery with cost-constrained measurements
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
Brief paper: An efficient sensor quantization algorithm for decentralized estimation fusion
Automatica (Journal of IFAC)
Quantization, channel compensation, and optimal energy allocation for estimation in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Decentralized estimation over noisy channels in cluster-based wireless sensor networks
International Journal of Communication Systems
| Hi-index | 754.86 |
We consider decentralized estimation of a noise-corrupted deterministic parameter by a bandwidth-constrained sensor network with a fusion center. The sensor noises are assumed to be additive, zero mean, spatially uncorrelated, but otherwise unknown and possibly different across sensors due to varying sensor quality and inhomogeneous sensing environment. The classical best linear unbiased estimator (BLUE) linearly combines the real-valued sensor observations to minimize the mean square error (MSE). Unfortunately, such a scheme cannot be implemented in a practical bandwidth-constrained sensor network due to its requirement to transmit real-valued messages. In this paper, we construct a decentralized estimation scheme (DES) where each sensor compresses its observation to a small number of bits with length proportional to the logarithm of its local signal-to-noise ratio (SNR). The resulting compressed bits from different sensors are then collected and combined by the fusion center to estimate the unknown parameter. The proposed DES is universal in the sense that each sensor compression scheme requires only the knowledge of local SNR, rather than the noise probability distribution functions (pdf), while the final fusion step is also independent of the local noise pdfs. We show that the MSE of the proposed DES is within a constant factor of 25/8 of that achieved by the classical centralized BLUE estimator.