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Streaming analysis in wireless sensor networks
Version 2 2024-06-04, 06:12Version 2 2024-06-04, 06:12
Version 1 2016-11-23, 13:22Version 1 2016-11-23, 13:22
journal contribution
posted on 2024-06-04, 06:12 authored by M Moshtaghi, JC Bezdek, TC Havens, C Leckie, S Karunasekera, Sutharshan RajasegararSutharshan Rajasegarar, M PalaniswamiTwo new incremental models for online anomaly detection in data streams at nodes in wireless sensor networks are discussed. These models are incremental versions of a model that uses ellipsoids to detect first, second, and higher-ordered anomalies in arrears. The incremental versions can also be used this way but have additional capabilities offered by processing data incrementally as they arrive in time. Specifically, they can detect anomalies 'on-the-fly' in near real time. They can also be used to track temporal changes in near real-time because of sensor drift, cyclic variation, or seasonal changes. One of the new models has a mechanism that enables graceful degradation of inputs in the distant past (fading memory). Three real datasets from single sensors in deployed environmental monitoring networks are used to illustrate various facets of the new models. Examples compare the incremental version with the previous batch and dynamic models and show that the incremental versions can detect various types of dynamic anomalies in near real time. Copyright © 2012 John Wiley & Sons, Ltd.
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Journal
Wireless communications and mobile computingVolume
14Pagination
905-921Location
Chichester, Eng.Publisher DOI
Open access
- Yes
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ISSN
1530-8669eISSN
1530-8677Language
engPublication classification
C Journal article, C1.1 Refereed article in a scholarly journalCopyright notice
2014, WileyIssue
9Publisher
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