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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 reliable 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, the authors propose an online outlier detection technique based on one-class hyper-ellipsoidal SVM and take advantage of spatial and temporal correlations that exist between sensor data to cooperatively identify outliers.
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