NDLI: Energy-efficient self-adapting online linear forecasting for wireless sensor network applications

Content Provider	IEEE Xplore Digital Library
Author	Jai-Jin Lim Shin, K.G.
Copyright Year	2005
Description	Author affiliation: Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI (Jai-Jin Lim; Shin, K.G.)
Abstract	New energy-efficient linear forecasting methods are proposed for various sensor network applications, including in-network data aggregation and mining. The proposed methods are designed to minimize the number of trend changes for a given application-specified forecast quality metric. They also self-adjust the model parameters, the slope and the intercept, based on the forecast errors observed via measurements. As a result, they incur O(1) space and time overheads, a critical advantage for resource-limited wireless sensors. An extensive simulation study based on real-world and synthetic time-series data shows that the proposed methods reduce the number of trend changes by 20%~50% over the existing well-known methods for a given forecast quality metric. That is, they are more predictive than the others with the same forecast quality metric
Starting Page	8
Ending Page	379
File Size	430324
Page Count	372
File Format	PDF
ISBN	0780394658
DOI	10.1109/MAHSS.2005.1542822
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wireless sensor networks Load forecasting Design methodology Energy measurement Predictive models Prediction algorithms Energy efficiency Data mining Application software Monitoring
Content Type	Text
Resource Type	Article

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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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