Context Inconsistency Elimination Based on User Feedback and Modified Evidence Theory
Leitao Wang, Hongji Xu, Guoxia Sun, Zhengfeng Du, Zhigang Xie, Lina Zheng · International Journal of Machine Learning and Computing · 2013
In dynamic and open environment, context aware system obtains context information from the dynamic, distributed and heterogeneous sources, but the context information usually has inconsistencies which would lead to inappropriate services.We proposed a new context inconsistency elimination algorithm based on user feedback and modified evidence theory in this paper.Through user feedback, each sensor's perception precision can be acquired, and with the modified evidence theory, we can make full use of all context information and eliminate inconsistent context by adjusting the influence of every context on whole judgment based on sensor perception precision.In order to evaluate the performance of the proposed context inconsistency elimination algorithm, context aware rate is defined.The experiment results show that the proposed context inconsistency elimination algorithm can obtain the best context aware rate in most cases when the error rates of sensors are varied.