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Streaming analysis in wireless sensor networks

Moshtaghi, Masud, Bezdek, James C, Havens, Timothy C, Leckie, Christopher, Karunasekera, Shanika, Rajasegarar, Sutharshan and Palaniswami, Marimuthu 2014, Streaming analysis in wireless sensor networks, Wireless communications and mobile computing, vol. 14, no. 9, pp. 905-921, doi: 10.1002/wcm.2248.

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Title Streaming analysis in wireless sensor networks
Author(s) Moshtaghi, Masud
Bezdek, James C
Havens, Timothy C
Leckie, Christopher
Karunasekera, Shanika
Rajasegarar, Sutharshan
Palaniswami, Marimuthu
Journal name Wireless communications and mobile computing
Volume number 14
Issue number 9
Start page 905
End page 921
Total pages 17
Publisher Wiley
Place of publication Chichester, Eng.
Publication date 2014-06-25
ISSN 1530-8669
1530-8677
Keyword(s) anomaly detection
environmental monitoring
temporal chains
temporal data
wireless sensor networks
Summary Two 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.
Language eng
DOI 10.1002/wcm.2248
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Wiley
Persistent URL http://hdl.handle.net/10536/DRO/DU:30089213

Document type: Journal Article
Collection: School of Information Technology
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