Anomaly detection in university campus WiFi zones

This paper focuses on a set of anomaly detection techniques based on the analysis of WiFi access point utilization patterns. The aim is to explore the possibilities of detecting patterns that diverge from the usual ones in spaces covered by wireless networks. In this way special events or happenings in physical places could be identified and fed to a world model as an additional source of information for context-aware applications to use.

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