Traffic deduction exploring sensor data's intra-correlations in train track monitoring WSN

Zhi Liu, Toshitaka Tsuda, Hiroshi Watanabe

    研究成果: Conference contribution

    7 被引用数 (Scopus)

    抄録

    WSNs are good options to help monitor the scene of interest and notify the unusual happening to control center. But sensors' high sampling rates lead to tremendous network traffic over the bandwidth-limited and energy-critical WSNs, hence how to reduce the network traffic while maintaining the unusual events monitoring function becomes important. In this paper, we investigate the intra-correlations of the data generated by each sensor at different time instances. And we propose a traffic deduction algorithm exploring the sensor data's intra-correlations which could reduce the data volume significantly and guarantee the parameters needed for unusual detection are delivered.

    本文言語English
    ホスト出版物のタイトル2015 IEEE SENSORS - Proceedings
    出版社Institute of Electrical and Electronics Engineers Inc.
    ISBN(印刷版)9781479982028
    DOI
    出版ステータスPublished - 2015 12 31
    イベント14th IEEE SENSORS - Busan, Korea, Republic of
    継続期間: 2015 11 12015 11 4

    Other

    Other14th IEEE SENSORS
    国/地域Korea, Republic of
    CityBusan
    Period15/11/115/11/4

    ASJC Scopus subject areas

    • 器械工学
    • 電子材料、光学材料、および磁性材料
    • 分光学
    • 電子工学および電気工学

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