Hyperellipsoidal SVM-Based Outlier Detection Technique for Geosensor Networks

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    7 Citations (Scopus)


    Recently, wireless sensor networks providing fine-grained spatio-temporal observations have become one of the major monitoring platforms for geo-applications. Along side data acquisition, outlier detection is essential in geosensor networks to ensure data quality, secure monitoring and re- liable detection of interesting and critical events. A key challenge for outlier detection in these geosensor networks is accurate identification of outliers in a distributed and online manner while maintaining resource consumption low. In this paper, we propose an online outlier detection technique based on one-class hyperellipsoidal SVM and take advantage of spatial and temporal correlations that exist between sensor data to cooperatively identify outliers. Experiments with both synthetic and real data show that our online outlier detection technique achieves better detection accuracy compared to the existing SVM-based outlier detection techniques designed for sensor networks. We also show that understanding data distribution and correlations among sensor data is essential to select the most suitable outlier detection technique.
    Original languageUndefined
    Title of host publicationThird International Conference on Geosensor Networks
    Place of PublicationBerlin
    Number of pages11
    Publication statusPublished - 13 Jul 2009
    EventThird International Conference on Geosensor Networks, Oxford, UK: Third International Conference on Geosensor Networks - Berlin
    Duration: 13 Jul 2009 → …

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    ConferenceThird International Conference on Geosensor Networks, Oxford, UK
    Period13/07/09 → …


    • METIS-263930
    • EWI-15723
    • IR-67815

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