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Le, Viet Duc and Scholten, J. and Havinga, P.J.M. (2013) FLEAD: online frequency likelihood estimation anomaly detection for mobile sensing. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp '13, 8-12 Sep 2013, Zurich, Switzerland. pp. 1159-1166. ACM. ISBN 978-1-4503-2215-7
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Official URL: http://dx.doi.org/10.1145/2494091.2499774
With the rise of smartphone platforms, adaptive sensing becomes an predominant key to overcome intricate constraints such as smartphone's capabilities and dynamic data. One way to do this is estimating the event probability based on anomaly detection to invoke heavy processes, such as switching on more sensors or retrieving information. However, most conventional anomaly detection methods are power hungry and computation consuming. This paper proposes a new online anomaly detection algorithm by capturing the likelihood of frequency histogram given features extracted from a stream of measurements from sensors of multiple smartphones. The algorithm then estimates the mixed density probability of anomalies. By doing so, the algorithm is lightweight and energy efficient, which underpins large scale mobile sensing applications. Experimental results run on Android phones are consistent with our theoretical analysis.
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