TROVE: A Context-Awareness Trust Model for VANETs Using Reinforcement Learning

Jingjing Guo, Xinghua Li, Zhiquan Liu, Jianfeng Ma, Chao Yang, Junwei Zhang, Dapeng Wu

Abstract: Vehicular networks have become a visible reality enabling information sharing between vehicles to enhance driving safety and provide value-added services to drivers and passengers. However, false information might be injected into the network because of defective sensors, malicious vehicles, and so on. Therefore, an efficient mechanism to guarantee the reliability of information used by vehicles is of great importance in vehicular networks. To solve this problem, this article proposes a context-awareness trust management model to evaluate the trustworthiness of messages received by vehicles to ensure bogus information will not influence the driving decision-making process. In the proposed scheme, the trust evaluation result of an evaluation request is determined by available related information and the evaluation strategy in the current situation, which is unaffected by the presence of conflicting evidence and the trust level of entities in the network. Moreover, we design a reinforcement learning model that allows vehicles to adjust the evaluation strategy so as to maintain an accurate evaluation result in different driving scenarios. Extensive experiments were conducted in different driving scenarios to verify the effectiveness of the proposed model. The results show that our model is adaptive to different driving scenarios with negligible time overhead, regardless of the proportion of malicious nodes in the network. Furthermore, compared with three types of state-of-the-art trust models in different scenarios, our scheme can achieve a higher evaluation precision rate with no more computational and communication overhead in nonrandom road conditions.