Reputation-Based Collaborative Decision-Making in Hierarchical Blockchain-Enabled Vehicular Networks

In vehicular networks, collaborative decision-making can improve the recognition capability and driving efficiency of vehicles, while easy to suffer from false information injection from malicious nodes (MNs). Therefore, in this paper, we investigate collaborative decision-making in a hierarchical blockchain-enabled vehicular network in the presence of self-interested MNs. In order to improve the accuracy of collaborative decision-making, we propose a reputation-based decision-making approach, which consists of two parts: 1) We devise a Bayesian static game model for an attack-defense game between MNs and miners in the blockchains. By providing incentives in expected payoffs, the scheme motivates MNs to cease attacks, thereby reducing the dissemination of false information during the collaborative process. The pure-strategy Bayesian Nash equilibrium (BNE) of MN quitting attack is further studied. 2) We introduce a collaborative decision-making algorithm based on Dempster-Shafer Theory (DST) to facilitate precise decision-making in the presence of false information. This algorithm leverages the hierarchical blockchain architecture, integrating both historical and local reputation of a node to assess credibility, mitigating the influence of untrusted information. Simulation results demonstrate the effectiveness of our approach.