NDLI: Energy efficient outlier detection in WSNs based on temporal and attribute correlations

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Energy efficient outlier detection in WSNs based on temporal and attribute correlations

Content Provider	IEEE Xplore Digital Library
Author	Shahid, N. Naqvi, I.H.
Copyright Year	2011
Description	Author affiliation: LUMS School of Science and Engineering (SSE), Sector U, D.H.A. Lahore Cantt, Pakistan (Shahid, N.; Naqvi, I.H.)
Abstract	Support vector machines (SVM) have formulated the main concepts of machine learning, ever since their introduction. The one-class quarter sphere SVM has received recent interest, as it extends the concepts of machine learning to the domain of linear optimization problems with cost efficiency. This paper deals with the novel idea of a quarter-sphere SVM based only on temporal-attribute correlations. To avoid communication overhead the system complexity at individual sensor nodes is slightly increased. The outlier and event detection rate keeps up with the detection rate obtained via previous approaches with an added advantage of no communication cost.
Starting Page	1
Ending Page	6
File Size	244460
Page Count	6
File Format	PDF
ISBN	9781457707698
e-ISBN	9781457707681
DOI	10.1109/ICET.2011.6048470
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-05
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Wireless sensor networks Correlation Event detection attribute correlations Silicon Spatio-temporal correlations Arrays Optimization quarter-sphere SVM
Content Type	Text
Resource Type	Article

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