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Enhancing Situation-Aware Systems through Imprecise Reasoning
October 2008 (vol. 7 no. 10)
pp. 1153-1168
Christos Anagnostopoulos, University of Athens
Stathes Hadjiefthymiades, University of Athens
Context awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. We focus our work on situation awareness; a more holistic variant of context awareness where situations are regarded as logically aggregated contexts. One important problem that arises in such systems is the imperfect observations (e.g., sensor readings) that lead to the estimation of the current context of the user. Hence, the knowledge upon which the context / situation aware paradigm is built is rather vague. To deal with this shortcoming, we propose the use of Fuzzy Logic theory with the purpose of determining (inferring) and reasoning about the current situation of the involved user. We elaborate on the architectural model that enables the system to assume actions autonomously according to previous user reactions and current situation. The captured, imperfect contextual information is matched against pre-developed ontologies in order to approximately infer the current situation of the user. Finally, we present a series of experimental results that provide evidence of the flexible, efficient nature of the proposed situation awareness architecture.

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Index Terms:
Pervasive computing, Inference engines
Christos Anagnostopoulos, Stathes Hadjiefthymiades, "Enhancing Situation-Aware Systems through Imprecise Reasoning," IEEE Transactions on Mobile Computing, vol. 7, no. 10, pp. 1153-1168, Oct. 2008, doi:10.1109/TMC.2008.34
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