Segment-based anomaly detection with approximated sample covariance matrix in wireless sensor networks

Miao Xie, Jiankun Hu, Song Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

32 Citations (Scopus)

Abstract

In wireless sensor networks (WSNs), it has been observed that most abnormal events persist over a considerable period of time instead of being transient. As existing anomaly detection techniques usually operate in a point-based manner that handles each observation individually, they are unable to reliably and efficiently report such long-term anomalies appeared in an individual sensor node. Therefore, in this paper, we focus on a new technique for handling data in a segment-based manner. Considering a collection of neighbouring data segments as random variables, we determine those behaving abnormally by exploiting their spatial predictabilities and, motivated by spatial analysis, specifically investigate how to implement a prediction variance detector in a WSN. As the communication cost incurred in aggregating a covariance matrix is finally optimised using the Spearman's rank correlation coefficient and differential compression, the proposed scheme is able to efficiently detect a wide range of long-term anomalies. In theory, comparing to the regular centralised approach, it can reduce the communication cost by approximately 80 percent. Moreover, its effectiveness is demonstrated by the numerical experiments, with a real world data set collected by the Intel Berkeley ResearchLab (IBRL).
Original languageEnglish
Article number6748064
Pages (from-to)574-583
Number of pages10
JournalIEEE Transactions on Parallel and Distributed Systems
Volume26
Issue number2
DOIs
Publication statusPublished - 1 Feb 2015
Externally publishedYes

Keywords

  • anomaly detection
  • differential compression
  • distributed computing
  • spatial analysis
  • Spearman's rank correlation coefficient
  • Wireless sensor network

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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