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...

• Type: Journal
• Year: 2008
• Where: TSP
• Authors: Jun Fang, Hongbin Li
• Keywords: AQ Schemes, Conventional Fixed Quantization, Sensor, TSP 2008