An efficient data extraction framework for mining wireless sensor networks

Md. Mamunur Rashid, Iqbal Gondal, Joarder Kamruzzaman

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    Behavioral patterns for sensors have received a great deal of attention recently due to their usefulness in capturing the temporal relations between sensors in wireless sensor networks. To discover these patterns, we need to collect the behavioral data that represents the sensor’s activities over time from the sensor database that attached with a wellequipped central node called sink for further analysis. However, given the limited resources of sensor nodes, an effective data collection method is required for collecting the behavioral data efficiently. In this paper, we introduce a new framework for behavioral patterns called associatedcorrelated sensor patterns and also propose a MapReduce based new paradigm for extract data from the wireless sensor network by distributed away. Extensive performance study shows that the proposed method is capable to reduce the data size almost 50% compared to the centralized model.

    Original languageEnglish
    Title of host publicationNeural Information Processing
    Subtitle of host publication23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part III
    EditorsAkira Hirose, Seiichi Ozawa, Kenji Doya, Kazushi Ikeda, Minho Lee, Derong Liu
    Place of PublicationCham, Switzerland
    Number of pages8
    ISBN (Electronic)9783319466750
    ISBN (Print)9783319466743
    Publication statusPublished - 2016
    EventInternational Conference on Neural Information Processing 2016 - Kyoto, Japan
    Duration: 16 Oct 201621 Oct 2016
    Conference number: 23rd (Proceedings)

    Publication series

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


    ConferenceInternational Conference on Neural Information Processing 2016
    Abbreviated titleICONIP 2016
    Internet address


    • Associated-correlated sensor pattern
    • Data extraction
    • Data mining
    • Knowledge discovery
    • Wireless sensor networks

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