• DocumentCode
    1791551
  • Title

    Synthetic data generation for the internet of things

  • Author

    Anderson, Jason W. ; Kennedy, K.E. ; Ngo, Linh B. ; Luckow, Andre ; Apon, Amy W.

  • Author_Institution
    Sch. of Comput., Clemson Univ., Clemson, SC, USA
  • fYear
    2014
  • fDate
    27-30 Oct. 2014
  • Firstpage
    171
  • Lastpage
    176
  • Abstract
    The concept of Internet of Things (IoT) is rapidly moving from a vision to being pervasive in our everyday lives. This can be observed in the integration of connected sensors from a multitude of devices such as mobile phones, healthcare equipment, and vehicles. There is a need for the development of infrastructure support and analytical tools to handle IoT data, which are naturally big and complex. But, research on IoT data can be constrained by concerns about the release of privately owned data. In this paper, we present the design and implementation results of a synthetic IoT data generation framework. The framework enables research on synthetic data that exhibit the complex characteristics of original data without compromising proprietary information and personal privacy.
  • Keywords
    Big Data; Internet of Things; data privacy; Big Data; Internet of Things; IoT data handling; analytical tools; complex data; healthcare equipment; infrastructure support; mobile phones; personal privacy; proprietary information; synthetic IoT data generation framework; vehicles; Data mining; Generators; Sensor phenomena and characterization; Statistical distributions; Temperature sensors; XML;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (Big Data), 2014 IEEE International Conference on
  • Conference_Location
    Washington, DC
  • Type

    conf

  • DOI
    10.1109/BigData.2014.7004228
  • Filename
    7004228