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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Description
Summary: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, while using simple distributed quantizers in individual sensors. The decoder derivations are provided for Slepian–Wolf coded quantization based on both sample-by-sample (scalar) binning and vector binning schemes constructed via channel code partitioning. Simulation results are presented to demonstrate the performance achievable with the proposed decoding approach.
Bibliography:ObjectType-Article-2
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ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2010.05.019