NDLI: infer: A Bayesian Inference Approach towards Energy Efficient Data Collection in Dense Sensor Networks

Content Provider	IEEE Xplore Digital Library
Author	Hartl, G. Baochun Li
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Toronto Univ., Ont. (Hartl, G.; Baochun Li)
Abstract	In this paper, we propose a novel approach for efficiently sensing a remote field using wireless sensor networks. Our approach, the infer algorithm, is fully distributed, has low overhead and saves considerable energy compared to using just the data aggregation communication paradigm. This is accomplished by using a distributed algorithm to put nodes into sleep mode for a given period of time, thereby trading off energy usage for the accuracy of the data received at the sink. Bayesian inference is used to infer the missing data from the nodes that were not active during each sensing epoch. As opposed to other methods that have been considered, such as wavelet compression and distributed source coding, our algorithm has lower overhead in terms of both inter-node communication and computational complexity. Our simulations show that on average our algorithm produces energy savings of 59% while still maintaining data that is accurate to within 7.9%. We also show how the parameters of the algorithm may be tuned to optimize network lifetime for a desired level of data accuracy
Sponsorship	IEEE Comput. Soc. Tech. Comm. on Distributed Process. (TCDP)
Starting Page	371
Ending Page	380
File Size	522940
Page Count	10
File Format	PDF
ISBN	0769523315
ISSN	10636927
DOI	10.1109/ICDCS.2005.43
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Energy efficiency Intelligent networks Wireless sensor networks Source coding Large-scale systems Temperature sensors Batteries Distributed algorithms Inference algorithms
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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infer: A Bayesian inference approach towards energy efficient data collection in dense sensor networks (2005)

Infer: a bayesian inference approach towards energy efficient data collection in dense sensor networks (2005).

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infer: A Bayesian Inference Approach towards Energy Efficient Data Collection in Dense Sensor Networks

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