Detection and accommodation of outliers in Wireless Sensor Networks within a multi-agent framework

P. Gil, Hugo Martins, F. Januário

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

This paper studies three techniques for outliers detection in the context of Wireless Sensor Networks, including a machine learning technique, a Principal Component Analysis-based methodology and an univariate statistics-based approach. The first methodology is based on a Least Squares-Support Vector Machine technique, together with a sliding window learning. A modification to this approach is also considered in order to improve its performance in non-stationary time-series. The second methodology relies on Principal Component Analysis, along with the robust orthonormal projection approximation subspace tracking with rank-1 modification, while the last approach is based on univariate statistics within an oversampling mechanism. All methods are implemented under a hierarchical multi-agent framework and compared through experiments carried out on a test-bed.
Original languageEnglish
Pages (from-to)204-214
JournalApplied Soft Computing
Volume42
DOIs
Publication statusPublished - May 2016

Keywords

  • SUPPORT VECTOR MACHINE
  • ANOMALY DETECTION

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