Hybrid context inconsistency resolution for context-aware services

Context-aware applications automatically adapt their behavior according to environmental conditions, also known as contexts. However, in practice contexts are often inaccurate, noisy or even inconsistent (e.g., two RFID readers may report different numbers for the same set of goods processed). These kinds of problematic contexts may cause context-aware applications to behave abnormally or even fail. It is thus desirable to detect and resolve context inconsistency. In this paper, we propose a hybrid approach to detect problematic contexts and resolve resulting context inconsistencies with the help of context-aware application semantics. By combining low-level context inconsistency resolution with high-level application error recovery, our approach can resolve the inconsistent contexts more effectively. Moreover, error recovery cost for context-aware applications is reduced. Our experimental results show that our approach outperforms existing approaches in terms of more accurate inconsistency resolution and less error recovery cost.