Systematic distinction of events and errors in sensor data

X. Cui, B. Zhao, Qing Li, H. Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review


Due to neglecting the importance of distinguishing sensor data in event/anomaly detection, similarities and differences among event samples and error samples are analyzed based on the sensor data uncertainty, and a systematic distinction framework is designed to partition the raw data set into event subset, error subset and ordinary subset through node-level temporal processing, neighbor-level spatial processing, cluster-level ranking and network-level decision fusion. Experimental results on real-sensed data show that the framework achieves a distinction ratio as high as 97% in different network cases. Comparisons with traditional methods show that the proposed framework reduces the false-alarm rate to 1/10 of the traditional methods and does not exceed the traditional miss-hit rate.
Original languageEnglish
Pages (from-to)30-35
Number of pages6
JournalHsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University
Issue number10
Publication statusPublished - 1 Oct 2010
Externally publishedYes


  • Error
  • Event
  • Sensor data
  • Systematic distinction framework

ASJC Scopus subject areas

  • Engineering(all)


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