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
Adaptive quantized target tracking in wireless sensor networks
Wireless Networks
Distributed iterative quantization for interference characterization in wireless networks
Digital Signal Processing
Identification of ARMA models using intermittent and quantized output observations
Automatica (Journal of IFAC)
Adaptive quantizers for estimation
Signal Processing
Robust distributed maximum likelihood estimation with dependent quantized data
Automatica (Journal of IFAC)
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We consider distributed parameter estimation using quantized observations in wireless sensor networks (WSNs) where, due to bandwidth constraint, each sensor quantizes its local observation into one bit of information. A conventional fixed quantization (FQ) approach, which employs a fixed threshold for all sensors, incurs an estimation error growing exponentially with the difference between the threshold and the unknown parameter to be estimated. To address this difficulty, we propose a distributed adaptive quantization (AQ) approach, which, with sensors sequentially broadcasting their quantized data, allows each sensor to adaptively adjust its quantization threshold. Three AQ schemes are presented: (1) AQ-FS that involves distributed delta modulation (DM) with a fixed stepsize, (2) AQ-VS that employs DM with a variable stepsize, and (3) AQ-ML that adjusts the threshold through a maximum likelihood (ML) estimation process. The ML estimators associated with the three AQ schemes are developed and their corresponding Cramer-Rao bounds (CRBs) are analyzed. We show that our 1-bit AQ approach is asymptotically optimum, yielding an asymptotic CRB that is only pi/2 times that of the clairvoyant sample-mean estimator using unquantized observations.