NSSSD: a new semantic hierarchical storage for sensor data

Gheisari, Mehdi and Movassagh, Ali Akbar and Qin, Yongrui and Yong, Jianming and Tao, Xiaohui and Zhang, Ji and Shen, Haifeng (2016) NSSSD: a new semantic hierarchical storage for sensor data. In: 20th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD 2016), 4-6 May 2016 , Nanchang, China.

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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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright ©2016 IEEE. Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 17 Feb 2017 04:05
Last Modified: 05 Jun 2017 00:53
Uncontrolled Keywords: knowledge modeling, sensor data, hierarchical storage
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
08 Information and Computing Sciences > 0804 Data Format > 080499 Data Format not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/CSCWD.2016.7565984
URI: http://eprints.usq.edu.au/id/eprint/30382

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