Extending Context Spaces Theory by Predicting Run-Time Context

Authors:
Andrey Boytsov;Arkady Zaslavsky;Kåre Synnes
Affiliations:
Department of Computer Science and Electrical Engineering, Luleå University of Technology, Luleå SE-971 87;Department of Computer Science and Electrical Engineering, Luleå University of Technology, Luleå SE-971 87;Department of Computer Science and Electrical Engineering, Luleå University of Technology, Luleå SE-971 87
Venue:
NEW2AN '09 and ruSMART '09 Proceedings of the 9th International Conference on Smart Spaces and Next Generation Wired/Wireless Networking and Second Conference on Smart Spaces
Year:
2009

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Abstract

Context awareness and prediction are important for pervasive computing systems. The recently developed theory of context spaces addresses problems related to sensor data uncertainty and high-level situation reasoning. This paper proposes and discusses componentized context prediction algorithms and thus extends the context spaces theory. This paper focuses on two questions: how to plug-in appropriate context prediction techniques, including Markov chains, Bayesian reasoning and sequence predictors, to the context spaces theory and how to estimate the efficiency of those techniques. The paper also proposes and presents a testbed for testing a variety of context prediction methods. The results and ongoing implementation are also discussed.