FLEAD: online frequency likelihood estimation anomaly detection for mobile sensing

L Duc Le Viet Duc, Johan Scholten, Paul J.M. Havinga

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    6 Citations (Scopus)
    144 Downloads (Pure)


    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.
    Original languageUndefined
    Title of host publicationProceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp '13
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery
    Number of pages8
    ISBN (Print)978-1-4503-2215-7
    Publication statusPublished - Sept 2013
    Event2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing - Zurich, Switzerland
    Duration: 8 Sept 201312 Sept 2013

    Publication series



    Workshop2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing
    Abbreviated titleUbiComp


    • EWI-23689
    • Energy efficient
    • Outlier Detection
    • METIS-297825
    • Anomaly Detection
    • Mobile platforms
    • Mobile sensing
    • IR-87304

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