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International Journal of Distributed Sensor Networks
Volume 2012 (2012), Article ID 836362, 8 pages
http://dx.doi.org/10.1155/2012/836362
Research Article

The Construction of Inference Engine for Meaningful Context and Prediction Based on USN Environment

Department of Computer Science and Information Engineering, Chung-Ju National University, Chung-Ju, 50 Daehak-ro, Chungbuk 380-702, Republic of Korea

Received 7 September 2011; Accepted 4 December 2011

Academic Editor: Tai Hoon Kim

Copyright © 2012 So-Young Im and Ryum-Duck Oh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Currently, with gradually increasing movement to live with nature, artificial wetlands are increasing as well. All these change blows at rivers and streams thereby need for wetland management systems to increase. To measure environmental situations on the wetlands, people should go outside and check with measurement tools regularly. However, with these tools only it is difficult to know the exact situations on that wetland. Thus, we attached various sensors on the wetland and made sensor network environment. We used sensing data from sensor network to assume the situation of the wetland. This paper proposes a design for this through application of context inference of USN (Ubiquitous Sensor Network) and inference production rules for context inference engine of wetland management system by using JESS. In this study, we made rules using actual eutrophication criteria as a standard of water quality. The produced rules in this paper can decide the grade of eutrophication on wetland environment then predict the status of the wetland based on facts collected from sensor networks. Sensors sense data such as DO, BOD, SS, PH. And production rules divided the grades of each fact and then final rules can decide the eutrophication grades which mean water quality grades.