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TSP
2008

Distributed Adaptive Quantization for Wireless Sensor Networks: From Delta Modulation to Maximum Likelihood

13 years 5 months ago
Distributed Adaptive Quantization for Wireless Sensor Networks: From Delta Modulation to Maximum Likelihood
Abstract-- We consider distributed parameter estimation using quantized observations in wireless sensor networks 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, including 1) AQ-FS that involves distributed Delta modulation (DM) with a fixed stepsize, 2) AQVS 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 (MLEs) associated with the three AQ sc...
Jun Fang, Hongbin Li
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2008
Where TSP
Authors Jun Fang, Hongbin Li
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