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Probabilistic inference of lossy links using end-to-end data in sensor networks

Authors:

Wei Zeng,

Bing Wang,

Krishna R. PattipatiAuthors Info & Claims

CoNEXT '07: Proceedings of the 2007 ACM CoNEXT conference

Article No.: 48, Pages 1 - 2

https://doi.org/10.1145/1364654.1364711

Published: 10 December 2007 Publication History

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Abstract

Lossy links used in a sensor network affect network performance, and hence need to be detected and repaired [1, 2]. One approach to detect lossy links is that each node monitors the loss rates on its neighboring links and reports them to the sink. This approach, although straightforward, causes large amount of traffic. Another approach to detect lossy links is through end-to-end data that are transmitted periodically from sources to the sink(s) [3, 1, 2]. This end-to-end approach has the advantage of not generating any additional monitoring traffic. The challenge is, however, to develop accurate inference algorithms for lossy link detection based on end-to-end measurements.

References

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H. X. Nguyen and P. Thiran, "Using end-to-end data to infer lossy links in sensor networks," in Proc. of IEEE INFOCOM, 2006.

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CoNEXT '07: Proceedings of the 2007 ACM CoNEXT conference

December 2007

448 pages

ISBN:9781595937704

DOI:10.1145/1364654

General Chairs:
Jim Kurose
University of Massachusetts
,
Henning Schulzrinne
Columbia University

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Association for Computing Machinery

New York, NY, United States

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Published: 10 December 2007

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A lifetime-optimized end-to-end encryption scheme for sensor networks allowing in-network processing

Density-varying high-end sensor placement in heterogeneous wireless sensor networks

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