Abstract Online anomaly detection in wireless sensor networks (WSNs) has been explored extensively. In this paper, exploiting the spatio-temporal correlation existed in the sensed data collected from WSNs, an online anomaly detector for WSNs are built based on ensemble learning theory. Considering the resources constrained in WSNs, ensemble pruning based on bio-geographical based optimization (BBO) is conducted. Experiments conducted on a real WSN dataset demonstrate that the proposed method is effective.


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    Titel :

    Online Anomaly Detection Method Based on BBO Ensemble Pruning in Wireless Sensor Networks


    Beteiligte:
    Ding, Zhiguo (Autor:in) / Fei, Minrui (Autor:in) / Du, Dajun (Autor:in) / Xu, Sheng (Autor:in)


    Erscheinungsdatum :

    01.01.2014


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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