Low-Complexity Coding and Source-Optimized Clustering for Large-Scale Sensor Networks

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dc.creatorMaierbacher, G.
dc.creatorBarros, J.
dc.date2008-09-08
dc.date.accessioned2026-07-07T10:01:24Z
dc.date.available2026-07-07T10:01:24Z
dc.descriptionWe consider the distributed source coding problem in which correlated data picked up by scattered sensors has to be encoded separately and transmitted to a common receiver, subject to a rate-distortion constraint. Although near-tooptimal solutions based on Turbo and LDPC codes exist for this problem, in most cases the proposed techniques do not scale to networks of hundreds of sensors. We present a scalable solution based on the following key elements: (a) distortion-optimized index assignments for low-complexity distributed quantization, (b) source-optimized hierarchical clustering based on the Kullback-Leibler distance and (c) sum-product decoding on specific factor graphs exploiting the correlation of the data.
dc.description26 pages
dc.identifierhttps://arxiv.org/abs/0809.1330
dc.identifierhttp://arxiv.org/abs/0809.1330
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/168623
dc.subjectInformation Theory
dc.subjectE.4; H.1.1; E.1; G.3; C.2.4
dc.titleLow-Complexity Coding and Source-Optimized Clustering for Large-Scale Sensor Networks
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