Table of Contents Author Guidelines Submit a Manuscript
Journal of Sensors
Volume 2014 (2014), Article ID 368643, 11 pages
Research Article

Distributed Binary Quantization of a Noisy Source in Wireless Sensor Networks

Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2V4

Received 30 October 2013; Revised 21 May 2014; Accepted 18 July 2014; Published 12 August 2014

Academic Editor: Mike McShane

Copyright © 2014 Sahar Movaghati and Masoud Ardakani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In distributed (decentralized) estimation in wireless sensor networks, an unknown parameter must be estimated from some noisy measurements collected at different sensors. Due to limited communication resources, these measurements are typically quantized before being sent to a fusion center, where an estimation of the unknown parameter is calculated. In the most stringent condition, each measurement is converted to a single bit. In this study, we propose a distributed quantization scheme which is based on single-bit quantized data from each sensor and achieves high estimation accuracy at the fusion centre. We do this by designing some local binary quantizers which define a multithreshold quantization rule for each sensor. These local binary quantizers are initially designed so that together they mimic the functionality of a multilevel quantizer. Later, their design is improved to include some error-correcting capability, which further improves the estimation accuracy from the sensors’ binary data. The distributed quantization formed by such local binary quantizers along with the proper estimator proposed in this work achieves better performance, compared to the existing distributed binary quantization methods, specially when fewer sensors with low measurement noise are available.