Quantization for Robust Distributed Coding. (16th May 2016)

• Record Type: Journal Article
• Authors: Wu, Xiaolin; Bais, Abdul; Sarshar, Nima
• Is Part Of: International journal of distributed sensor networks. Volume 12:Number 5(2016)
• Publication Date: 2016-05-16
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2016/6308410
• Languages: English
• ISSNs: 1550-1329

Abstract

A distributed source coding approach is proposed for robust data communications in sensor networks. When sensor measurements are quantized, possible correlations between the measurements can be exploited to reduce the overall rate of communication required to report these measurements. Robust distributed source coding (RDSC) approaches differentiate themselves from other works in that the reconstruction error of all sources will not exceed a given upper bound, even if only a subset of the multiple descriptions of the distributed source code are received. We deal with practical aspects of RDSC in the context of scalar quantization of two correlated sources. As a benchmark to evaluate the performance of the proposed scheme, we derive theoretically achievable distortion-rate performances of an RDSC for two jointly Gaussian sources by applying known results on the classical multiple description source coding.