Streaming analysis in wireless sensor networks

Masud Moshtaghi, James C Bezdek, Timothy C Havens, Christopher Leckie, Shanika Karunasekera, Sutharshan Rajasegarar, Marimuthu Palaniswami

Research output: Contribution to journalArticleResearchpeer-review

16 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)905-921
Number of pages17
JournalWireless Communications and Mobile Computing
Volume14
Issue number9
DOIs
Publication statusPublished - 2014
Externally publishedYes

Keywords

  • anomaly detection
  • environmental monitoring
  • temporal chains
  • temporal data
  • wireless sensor networks

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