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
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;
Conference_Titel :
Big Data (Big Data), 2014 IEEE International Conference on
Conference_Location :
Washington, DC
DOI :
10.1109/BigData.2014.7004228