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

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 p...

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Bibliographic Details
Published in2011 7th International Conference on Emerging Technologies pp. 1 - 6
Main Authors Shahid, N., Naqvi, I. H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2011
Subjects
Arrays
attribute correlations
Correlation
Event detection
Optimization
quarter-sphere SVM
Silicon
Spatio-temporal correlations
Support vector machines
Wireless sensor networks
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Summary: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.
ISBN:9781457707698
1457707691
DOI:10.1109/ICET.2011.6048470