An approach for learnable context-awareness system by reflecting user feedback

Abstract

As the ubiquitous computing becomes popular, the context awareness computing also becomes more interesting reasearch issue. Despite of many important research results on this issue, there still are some limitations that can be enhanced to provide more reliable solution to the user. In this paper, we are describing a design and development of agent based context awareness system that can improve such limitations. The system composed of mainly three layers; the hardware layer receives numerical raw data from sensors and converts it into meaningful semantic data, the middleware layer takes care of ontology modeling, and finally the application layer makes adaptive inference and provides personalized solution to the user. Our approach focuses on the following two main issues. First, we have built ontology based context modeling using fuzzy data that can provide more reliable solution. Second, our CBR based inference engine can provide more personalized and adaptive service by interacting with users feedback. The simulated experimentation has been made and result shows some significant importance. © 2010 Springer-Verlag Berlin Heidelberg.