A content-oriented trust evaluation system and method in a vehicular ad hoc network environment

Abstract

The invention discloses a content-oriented trust evaluation method in a vehicle-mounted ad hoc network environment, which can learn historical trust evaluation results through a policy adjustment module to dynamically determine trust evaluation strategies in different situations, ensuring trust evaluation results in various situations In addition, the trust evaluation module obtains the best evaluation strategy through the trust evaluation request and related information of the trust evaluation request, and can maintain an evaluation accuracy rate of more than 90% when the proportion of false information transmitted in the network exceeds 50%. Thereby reducing the influence of false information in the node decision-making process and improving driving safety and comfort.

Description

technical field [0001] The invention belongs to the field of information security of the Internet of Vehicles, and in particular relates to a content-oriented trust evaluation system and method under a vehicle ad hoc network environment. Background technique [0002] Vehicular ad hoc network is a kind of mobile ad hoc network, which has the characteristics of fast node movement, open information transmission environment and highly dynamic topology. In the vehicle self-organizing network, the network nodes can transmit and share information through vehicle-to-vehicle communication technology (Vehicle to Vehicle, V2V) and vehicle-to-infrastructure communication (Vehicle to Infrastructure, V2I), so as to provide drivers and passengers with A safe, reliable driving environment and rich entertainment resources improve traffic efficiency and user experience. The premise of achieving this goal is that the messages transmitted in the network must be safe and reliable. However, sen...

AI Technical Summary

Problems solved by technology

However, the existing schemes use a fixed evaluation strategy, and do not dynamically update the strategy according to the evaluation effect and the driving environment of the vehicle node to ensure the accuracy of the evaluation results.

In addition, existing schemes can only be guaranteed to be effective when the proportion of false information transmitted in the network does not exceed 50%

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