Subspace Energy Monitoring for Anomaly Detection @Sensor or @Edge

Alex Marchioni, Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

The amount of data generated by distributed monitoring systems that can be exploited for anomaly detection, along with real time, bandwidth, and scalability requirements leads to the abandonment of centralized approaches in favor of processing closer to where data are generated. This increases the interest in algorithms coping with the limited computational resources of gateways or sensor nodes. We here propose two dual and lightweight methods for anomaly detection based on generalized spectral analysis. We monitor the signal energy laying along with the principal and anti-principal signal subspaces, and call for an anomaly when such energy changes significantly with respect to normal conditions. A streaming approach for the online estimation of the needed subspaces is also proposed. The methods are tested by applying them to synthetic data and real-world sensor readings. The synthetic setting is used for design space exploration and highlights the tradeoff between accuracy and computational cost. The real-world example deals with structural health monitoring and shows how, despite the extremely low computations costs, our methods are able to detect permanent and transient anomalies that would classically be detected by full spectral analysis.
Original languageEnglish (US)
Pages (from-to)7575-7589
Number of pages15
JournalIEEE Internet of Things Journal
Volume7
Issue number8
DOIs
StatePublished - Aug 1 2020
Externally publishedYes

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Information Systems and Management
  • Computer Science Applications
  • Hardware and Architecture
  • Computer Networks and Communications

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