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Scalable Anomaly Detection for Smart City Infrastructure Networks
Nov.-Dec. 2013 (vol. 17 no. 6)
pp. 39-47
Djellel Eddine Difallah, University of Fribourg
Philippe Cudre-Mauroux, University of Fribourg
Sean A. McKenna, IBM Research Smarter Cities Technology Center
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.
Index Terms:
Monitoring,Cities and towns,Internet,Sensors,Smart buildings,Real-time systems,Wireless sensor networks,Urban areas,Network architecture,array data processing,smart cities,water data management,sensor networks,stream processing
Citation:
Djellel Eddine Difallah, Philippe Cudre-Mauroux, Sean A. McKenna, "Scalable Anomaly Detection for Smart City Infrastructure Networks," IEEE Internet Computing, vol. 17, no. 6, pp. 39-47, Nov.-Dec. 2013, doi:10.1109/MIC.2013.84
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