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Issue No.06 - Nov.-Dec. (2013 vol.17)
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.
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
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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