NSSSD: A new semantic hierarchical storage for sensor data

Mehdi Gheisari, Ali Movassagh, Yongrui Qin, Jianming Yong, Xiaohui Tao, Ji Zhang, Haifeng Shen

    Research output: Contribution to conferencePaper

    13 Citations (Scopus)

    Abstract

    Sensor networks usually generate mass of data, which if not structured for future applications, will require much effort on analytical processing and interpretations. Thus, storing sensor data in an effective and structured format is a key issue in the area of sensor networks. In the meantime, even a little improvement on data storing structure may lead to a significant effect on the lifetime and performance of the sensor network. This paper describes a new method for sensor storage that combines semantic web concepts, a data aggregation method along with aligning sensors in hierarchical form. This solution is able to reduce the amount of data stored at the sink nodes significantly. At the same time, the method structures sensed data in a way that we can respond to semantic web-based queries with less consumption of energy compared to previous conventional methods. Results show that, in some situations especially when the diversity of query responses and life of network are vital, the efficiency of our new solution is much better.

    Original languageEnglish
    Pages174-179
    Number of pages6
    DOIs
    Publication statusPublished - 13 Sep 2016
    EventThe 20th IEEE International Conference on Computer Supported Cooperative Work in Design -
    Duration: 4 May 2016 → …

    Conference

    ConferenceThe 20th IEEE International Conference on Computer Supported Cooperative Work in Design
    Period4/05/16 → …

    Keywords

    • hierarchical storage
    • Knowledge modeling
    • sensor data

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