Two-Phase Optimization in Hashgraph-Based IoV: Enabling Trusted and Low-Cost Edge Services

The non-cooperative game between rational vehicle users will generate unnecessary costs, manifested as the gap between user equilibrium (UE) and system optimal (SO). This issue primarily arises due to the competition or the untrusted collaboration among vehicles. The traditional marginal cost pricing (MCP) method is constrained by factors such as vehicle density and communication protocols, resulting in suboptimal performance. In this paper, the immutability of Hashgraph is leveraged to enable trusted services in the Internet of Vehicles (IoV), transforming non-cooperative games into a global optimization problem, while a two-phase optimization method is proposed to achieve low-cost services. Firstly, this paper simplifies the process of determining consensus timestamps for Hashgraph and constrains the actions of participants through immutability, thereby guaranteeing trusted services more efficiently. Subsequently, this paper systematically analyzes the key factors affecting travel and network service costs to optimize them in turn. Specifically, regarding the travel costs, this paper introduces a segment shielding method in trusted scenarios to avoid Braess’s paradox. As for the network service costs of data sharing, this paper presents a latency-sensitive dynamic programming method to integrate each server’s status to optimize resource scheduling. When the vehicle density is 400, the proposed method reduces the total cost by 20.920% and improves the QoE by 122.535%. The advantages become more significant as vehicle density increases.