In vehicular networks, trustworthy information sharing between vehicles is an important security issue. We find that existing trust systems in vehicular networks have the disadvantages of high assessment latency and high maintenance cost. In this paper, by introducing mobile edge computing (MEC), we propose a tree-searching based trust assessment method through communities, named TTAC method, for vehicular networks. The proposed TTAC method includes two parts. First, based on information interactions, TTAC gives a direct trust assessment method by utilizing Dempster-Shafer (D-S) evidence theory. Second, with the assistance of MEC base stations, TTAC designs a tree-searching based indirect trust calculation method by utilizing two neural networks through vehicles’ communities. In experiments, we use a dataset of Shenzhen taxicab traffic and simulate information interactions among vehicles. The experimental results show that TTAC method can ensure fast calculation time with high assessment accuracy in a distributed manner. Especially, in terms of the accuracy of the indirect trust assessment, the mean square error (MSE) of TTAC method is lower than that of two popular trust assessment methods, compared with 3VSL by 41.4% and with MoleTrust by 71.4%.

在车辆网络中，车辆之间可信赖的信息共享是一个重要的安全问题。我们发现，车载网络中现有的信任系统具有评估延迟高和维护成本高的缺点。在本文中，通过介绍移动边缘计算（MEC），我们提出了一种基于树搜索的社区信任评估方法，称为TTAC方法，用于车辆网络。拟议的TTAC方法包括两部分。首先，基于信息交互，TTAC利用Dempster-Shafer（DS）证据理论给出了直接信任评估方法。其次，在MEC基站的帮助下，TTAC通过利用通过车辆社区的两个神经网络，设计了一种基于树搜索的间接信任计算方法。在实验中 我们使用深圳出租车交通数据集并模拟车辆之间的信息交互。实验结果表明，TTAC方法可以保证分布式计算中快速的计算时间和较高的评估精度。特别是，就间接信任评估的准确性而言，TTAC方法的均方差（MSE）低于两种流行的信任评估方法的均方差（MSE），与3VSL相比，降低了41.4％，与MoleTrust相比，降低了71.4％。