SeaCloudDM: a database cluster framework for managing and querying massive heterogeneous sensor sampling data

  • Authors:

  • Zhiming Ding;Jiajie Xu;Qi Yang

  • Affiliations:

  • Institute of Software, Chinese Academy of Sciences, Zhong-Guan-Cun, Beijing, P.R. China 100190;Institute of Software, Chinese Academy of Sciences, Zhong-Guan-Cun, Beijing, P.R. China 100190;National Center of ITS Engineering & Technology, Haidian District, Beijing, P.R. China 100088

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2013

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Abstract

Recent advances in sensor networks and communication technologies have made the Internet of Things (IoT) a hot research issue. An IoT system can sample and manage the historical and present states of various kinds of physical and virtual objects such as vehicles, lakes, mountains, dams, city traffic conditions, atmosphere qualities, and so forth. It is well acknowledged that IoT will greatly change the way how people live and work. However, IoT also brings about great challenges to the data management community. For instance, the data to be managed in IoT are highly dynamic and heterogeneous. Meanwhile, since the sensor sampling data are managed in a centralized manner, the data size can be huge. Moreover, sensor data are intrinsically spatial-temporal data which may involve complicated spatial-temporal computations in query processing. To meet these challenges, we propose a novel Sea-Cloud-based Data Management (SeaCloudDM) mechanism in this paper. The experimental results show that the SeaCloudDM mechanism provides satisfactory performances in managing and querying massive sensor sampling data, and is thus a viable solution for IoT data management.