Tree-searching based trust assessment through communities in vehicular networks

Abstract

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%.