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NSSSD: A new semantic hierarchical storage for sensor data
Gheisari, Mehdi Movassagh, Ali Akbar Qin, Yongrui Yong, Jianming Tao, Xiaohui Zhang, Ji Shen, Haifeng
Gheisari, Mehdi
Movassagh, Ali Akbar
Qin, Yongrui
Yong, Jianming
Tao, Xiaohui
Zhang, Ji
Shen, Haifeng
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.
Keywords
knowledge modeling, sensor data, hierarchical storage
Date
2016
Type
Conference item
Journal
Book
Proceedings of the 2016 IEEE 20th international conference on computer supported cooperative work in design (CSCWD)
Volume
Issue
Page Range
174-179
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
License
File Access
Controlled