A Probabilistic Approach to Consistency Checking for Pervasive Context

Abstract

Context-awareness is a key issue in pervasive computing. Context-aware applications are prone to the context consistency problem, where applications are confronted with conflicting contexts and cannot decide how to adapt themselves. In pervasive computing environments, users are often willing to accept certain degree of context inconsistency, as long as it can reduce the consistency maintenance cost, e.g., query delay and battery power. However, existing consistency maintenance schemes do not enable the users to make such tradeoffs. To this end, we propose the probabilistic consistency checking for pervasive context (PCCPC) algorithm. Detailed performance analysis shows that PCCPC enables the users to check consistency over arbitrarily specified ratio of context. We also conduct experiments to study the cost reduced by probabilistic checking. The analytical and the experimental results show that PCCPC enables the users to efficiently make tradeoffs between context consistency and the associated checking cost.