Auto-Adaptive Trust Measurement Model Based on Multidimensional Decision-Making Attributes for Internet of Vehicles

As an important branch and application of the Internet of Things (IoT), the Internet of Vehicles (IoV) has the characteristics of wide distribution and dynamic connection. The current research on trust measurement and management in IoV, to some degree, solved vehicles reliability and QoS issues, but these models still have some drawbacks, like insufficient adaptability to the dynamic changes of the context. Therefore, this paper proposes an adaptive trust measurement model for IoV based on multidimensional decision-making attributes. The model not only takes full advantage of the central static trust management role of the local organization but also implements a distributed self-governing mechanism to tackle the dynamic trust management issues. In the process of trust management, the model allows vehicles to handle the trust evaluation according to the service preferences, and vehicles can select some or all of the attributes from the multidimensional trust decision attribute list. For the recommendation trust evaluation, vehicles can select those vehicles which have similar service preferences from the vehicle candidate list. When computing the recommendation trust, the recommendation trust dispersion model is used to handle evaluation bias problems. The method of information entropy is introduced to tackle the weight adaptation problem when computing comprehensive trust evaluation. The simulation results and analysis show that the model can detect and recognize the malicious vehicles in the network and mitigate the risk that malicious vehicles provide the service to normal vehicles.