A Comparison Study between RCCAR and Conventional Prediction Techniques for Resolving Context Conflicts in Pervasive Context-Aware Systems

摘要

In Pervasive computing environment, context-aware systems face many challenges to keep high quality performance. One-challenge faces context-aware systems is conflicted values come from different sensors because of different reasons. These conflicts affect the quality of context and as a result the quality of service as a whole. This paper is extension to our previous work, which is published in [15]. In our previous work, we presented an approach for resolving context conflicts in context-aware systems. This approach is could RCCAR (Resolving Context Conflicts Using Association Rules). RCCAR is implemented and verified well in [15], this paper conducts further experiments to explore the performance of RCCAR in comparison with the traditional prediction methods. The basic prediction methods that have been tested include simple moving average, weighted moving average, single exponential smoothing, double exponential smoothing, and ARMA. Experiments is conducted using Weka 3.7.7 and Excel; the results show better achievements for RCCAR against the conventional prediction methods. More researches are recommended to eliminate the cost of RCCAR.