Effective context inconsistency elimination algorithm based on feedback and reliability distribution for IoV

In recent years, context aware technology has been widely used in many fields, such as internet of vehicles (IoV). Consistent context information plays a vital role in adapting a system to rapidly changing situations. However, sensor's precision variance, equipment heterogeneity, network delay and the difference of statistical algorithms can lead to inconsistency context and inappropriate services. In this paper, we present an effective algorithm of context inconsistent elimination which is based on feedback and adjusted basic reliability distribution. Through feedback, each sensor's perception precision can be obtained, and with the adjusted basic reliability distribution scheme, we can make full use of all context information by adjusting the influence of every context on whole judgment based on sensor's perception precision and threshold of sensor's perception precision, and then eliminate context inconsistency. In order to evaluate the performance of the proposed context inconsistency elimination algorithm, context aware rate is defined. The simulation results show that the proposed context inconsistency elimination algorithm can obtain the best context aware rate in most cases for the varied error rates of sensors.