NDLI: Minimum Cost Data Aggregation with Localized Processing for Statistical Inference

Content Provider	IEEE Xplore Digital Library
Author	Anandkumar, A. Lang Tong Swami, A. Ephremides, A.
Copyright Year	2008
Description	Author affiliation: ECE Dept., Cornell Univ., Ithaca, NY (Anandkumar, A.; Lang Tong)
Abstract	The problem of minimum cost in-network fusion of measurements, collected from distributed sensors via multihop routing is considered. A designated fusion center performs an optimal statistical-inference test on the correlated measurements, drawn from a Markov random field. Conditioned on the delivery of a sufficient statistic for inference to the fusion center, the structure of optimal routing and fusion is shown to be a Steiner tree on a transformed graph. This Steiner-tree reduction preserves the approximation ratio, which implies that any Sterner- tree approximation can be employed for minimum cost fusion with the same approximation ratio. The proposed fusion scheme involves routing packets of two types viz., raw measurements sent for local processing, and aggregates obtained on combining these processed values. The performance of heuristics for minimum cost fusion are evaluated through theory and simulations, showing a significant saving in routing costs, when compared to routing all the raw measurements to the fusion center.
Starting Page	780
Ending Page	788
File Size	210242
Page Count	9
File Format	PDF
ISBN	9781424420254
ISSN	0743166X
DOI	10.1109/INFOCOM.2008.129
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Routing Statistics Statistical distributions Cost function Sensor fusion Markov random fields Collaborative work Government Power generation economics Communications Society
Content Type	Text
Resource Type	Article
Subject	Computer Science Electrical and Electronic Engineering

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