Abstract
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.
Highlights
With the application of various connected devices and the rise of intelligent driving technology, IoV has become an important choice for people to travel intelligently [1]
In response to the above drawbacks, this paper proposes a novel trust measurement model, which makes the best of advantages of both central trust management and distributed trust management. e trust evaluation is based on the multidimensional decision-making attributes of the vehicles, and the model is auto-adaptive. is trust measurement model allows the trustor vehicles to take multiple factors into consideration when computing the trust value, such as package forwarding rate and package repetition rate. e model solves the problem of insufficient adaptability of traditional quantitative models to the dynamic changes of the IoV. e method to calculate the direct trust value fuses the historical statistical trust record and subjective interaction satisfaction to enhance the objectivity of the direct trust measurement
Suppose that β1, β2, . . . , βn represents the Internet of Vehicles nodes that have interactive behaviors in the network environment. e direct trust of a node is the quantified trust evaluation value calculated by the trustor node with reference to the interaction result; the interaction result is comprehensively calculated by fusing multidimensional trust evaluation factors
Summary
With the application of various connected devices and the rise of intelligent driving technology, IoV has become an important choice for people to travel intelligently [1]. Aiming at distributed trust modeling, Li et al [11] proposed an opportunistic network security routing decision-making method based on a trust mechanism, which relies on the message carrying method to realize the collection of evidence chains and uses a trust vector with signature and time stamp to trust the node and to provide effective feedback. Most of the existing trust research focuses on solving the trust problem in specific application scenarios and does not consider that normally in IoV there are two networking methods, that is, static and dynamic. E experiment proves that the model can effectively solve the problem of vehicle trust measurement in IoV, even in the environment of massive heterogeneous devices with huge computing capacity span and dynamic changes in network topology.
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