Sparse random linear network coding for data compression in WSNs

This paper addresses the information theoretical analysis of data compression achieved by random linear network coding in wireless sensor networks. A sparse network coding matrix is considered with columns having possibly different sparsity factors. For stationary and ergodic sources, necessary and...

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Bibliographic Details
Published in:Proceedings / IEEE International Symposium on Information Theory pp. 2729 - 2733
Main Authors: Wenjie Li, Bassi, Francesca, Kieffer, Michel
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.07.2016
Subjects:
Asymptotic properties
Coding
Data compression
Decoding
Encoding
Information theory
Network coding
Networks
Noise
Noise measurement
Remote sensors
Sensors
Sparse matrices
Wireless networks
Wireless sensor networks
ISSN:2157-8117
Online Access:Get full text
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Summary:This paper addresses the information theoretical analysis of data compression achieved by random linear network coding in wireless sensor networks. A sparse network coding matrix is considered with columns having possibly different sparsity factors. For stationary and ergodic sources, necessary and sufficient conditions are provided on the number of required measurements to achieve asymptotically vanishing reconstruction error. To ensure the asymptotically optimal compression ratio, the sparsity factor can be arbitrary close to zero in absence of additive noise. In presence of noise, a sufficient condition on the sparsity of the coding matrix is also proposed.
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ISSN:2157-8117
DOI:10.1109/ISIT.2016.7541795