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Proceedings Paper

Data compression trade-offs in sensor networks
Author(s): Mo Chen; Mark L. Fowler
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Paper Abstract

This paper first discusses the need for data compression within sensor networks and argues that data compression is a fundamental tool for achieving trade-offs in sensor networks among three important sensor network parameters: energy-efficiency, accuracy, and latency. Next, it discusses how to use Fisher information to design data compression algorithms that address the trade-offs inherent in accomplishing multiple estimation tasks within sensor networks. Results for specific examples demonstrate that such trades can be made using optimization frameworks for the data compression algorithms.

Paper Details

Date Published: 18 October 2004
PDF: 12 pages
Proc. SPIE 5561, Mathematics of Data/Image Coding, Compression, and Encryption VII, with Applications, (18 October 2004); doi: 10.1117/12.562187
Show Author Affiliations
Mo Chen, SUNY/Binghamton (United States)
Mark L. Fowler, SUNY/Binghamton (United States)


Published in SPIE Proceedings Vol. 5561:
Mathematics of Data/Image Coding, Compression, and Encryption VII, with Applications
Mark S. Schmalz, Editor(s)

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