Abstract
As location-sensing smart phones and location-based services gain mainstream popularity, there is increased interest in developing techniques that can detect anomalous activities. Anomaly detection capabilities can be used in theft detection, remote elder-care monitoring systems, and many other applications. In this paper we present an n-gram based model for modeling a user’s mobility patterns. Under the Markovian assumption that a user’s location at time t depends only on the last n − 1 locations until t − 1, we can model a user’s idiosyncratic location patterns through a collection of n-gram geo-labels, each with estimated probabilities. We present extensive evaluations of the n-gram model conducted on real-world data, compare it with the previous approaches of using T-Patterns and Markovian models, and show that for anomaly detection the n-gram model outperforms existing work by approximately 10%. We also show that the model can use a hierarchical location partitioning system that is able to obscure a user’s exact location, to protect privacy, while still allowing applications to utilize the obscured location data for modeling anomalies effectively.
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Buthpitiya, S., Zhang, Y., Dey, A.K., Griss, M. (2011). n-Gram Geo-trace Modeling. In: Lyons, K., Hightower, J., Huang, E.M. (eds) Pervasive Computing. Pervasive 2011. Lecture Notes in Computer Science, vol 6696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21726-5_7
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DOI: https://doi.org/10.1007/978-3-642-21726-5_7
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