Scalable anomaly detection for smart city infrastructure networks

Djellel Eddine Difallah, Philippe Cudre-Mauroux, Sean A. McKenna

Research output: Contribution to journalArticle

Abstract

Dynamically detecting anomalies can be difficult in very large-scale infrastructure networks. The authors' approach addresses spatiotemporal anomaly detection in a smarter city context with large numbers of sensors deployed. They propose a scalable, hybrid Internet infrastructure for dynamically detecting potential anomalies in real time using stream processing. The infrastructure enables analytically inspecting and comparing anomalies globally using large-scale array processing. Deployed on a real pipe network topology of 1,891 nodes, this approach can effectively detect and characterize anomalies while minimizing the amount of data shared across the network.

Original languageEnglish (US)
Article number6576747
Pages (from-to)39-47
Number of pages9
JournalIEEE Internet Computing
Volume17
Issue number6
DOIs
StatePublished - Nov 2013

Keywords

  • array data processing
  • sensor networks
  • smart cities
  • stream processing
  • water data management

ASJC Scopus subject areas

  • Computer Networks and Communications

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    Difallah, D. E., Cudre-Mauroux, P., & McKenna, S. A. (2013). Scalable anomaly detection for smart city infrastructure networks. IEEE Internet Computing, 17(6), 39-47. [6576747]. https://doi.org/10.1109/MIC.2013.84