Joint source-decoding in large scale sensor networks using Markov random field models

An approach to scalable joint source decoding in large-scale sensor networks, based on Markov-random filed (MRF) modeling of the spatio-temporal correlation in the observations is presented. This approach exploits the correlation among a multitude of sensors for joint decoding at a central decoder,...

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Bibliographic Details
Published in:	Signal processing Vol. 90; no. 12; pp. 3134 - 3146
Main Author:	Yahampath, Pradeepa
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.12.2010 Elsevier
Subjects:	Applied sciences Channels Coding, codes Correlation Decoders Decoding Detection, estimation, filtering, equalization, prediction Distributed quantization Exact sciences and technology Information, signal and communications theory Magnetorheological fluids Markov-random field models Multi-terminal source coding Networks Partitioning Sampling, quantization Sensors Signal and communications theory Signal, noise Slepian–Wolf coding Telecommunications and information theory Multi-terminal source coding Markov-random field models Distributed quantization Slepian–Wolf coding Performance evaluation Probabilistic approach Source coding Scalability Partition method Time correlation Slepian-Wolf coding Decoding Markov model Joint Channel coding Modeling Random field Simulation Signal quantization Quantifier Sensor array Signal detection
ISSN:	0165-1684, 1872-7557
Online Access:	Get full text
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