Inference Engine Design for USN Based Wetland Context Inference

  • So-Young Im
  • Ryum-Duck Oh
  • Yoon-Cheol Hwang
Part of the Communications in Computer and Information Science book series (CCIS, volume 151)


With gradually increasing movement to live together with nature, artificial wetlands are increasing as well. And the wind of change blows at the rivers and streams, thereby increasing the need for management systems. Accordingly this study proposes to design for this through application of context inference of USN and production rules for Context Inference Engine of wetland management system by using JESS. 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.


Wetlands Eutrophication Context-Inference JESS Sensor Network 


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  1. 1.
    water Environmental Kit,, 8010471250 _39398.pdf
  2. 2.
    Natlonal Institute of Environmental Research EnvironmentalKit (1990),
  3. 3.
    Lee, H.-S., Lee, J.-H.: A Study on the conceptual model of the ubiquitous residential environment based on the context-aware inference (2009)Google Scholar
  4. 4.
    Doorenbos, R.B.: Production Matching for Large Learning Systems, Computer Science Department, Carnegie Mellon Univ. (1995)Google Scholar
  5. 5.
    Forgy, C.: Rete: A Fast Algorithm for the Many Patterns/Many Objects Match Problem. Artificial Intelligence 19(1), 17–37 (1982)CrossRefGoogle Scholar
  6. 6.
    Jess, the Rule Engine for the Java Platform,
  7. 7.
    Friedman-Hill, E.J.: Jess, The Java Expert System Shell, December 3 (1998)Google Scholar
  8. 8.
    University of Alberta Press: Over fertilization of the World’s Freshwaters and EstuariesGoogle Scholar
  9. 9.
    Bartram, J., Carmichael, W.W., Chorus, I., Jones, G., Skulberg, O.M.: Chapter 1. Introduction. In: Toxic Cyanobacteria in Water: A Guide to Their Public Health Consequences, Monitoring and Management. World Health Organization (1999)Google Scholar
  10. 10.
    Horrigan, L., Lawrence, R.S., Walker, P.: How sustainable agriculture can address the environmental and human health harms of industrial agriculture (2002)Google Scholar
  11. 11.
    Park, S.-H.: Report for water quality of Artificial lake (2003)Google Scholar
  12. 12.
    DreamTest, Kinds of sensors,

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • So-Young Im
    • 1
  • Ryum-Duck Oh
    • 1
  • Yoon-Cheol Hwang
    • 1
  1. 1.Computer Science and Information EngineeringChung-ju National UniversityChungju-siSouth Korea

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