Dynamic-Pricing-Based Incentive Mechanism and Resource Allocation for Multi-RSU in Vehicular Edge Computing

In green transportation systems, Vehicular Edge Computing (VEC) is playing a critical role by enabling real-time responses to compute-intensive tasks. Furthermore, incentivizing diverse stakeholders in VEC to share resources and ensuring equitable resource allocation are pivotal to achieving sustainability. In existing studies, a key limitation is that resource price is often simplified to a static decision factor, thus failing to function as a dynamic indicator that reflects real-time load state of the RSUs and the essential balance between the total utility of RSUs and vehicle user experience (QoE). At the same time, the issue is more challenging when facing the load balancing within multi-RSU. To resolve this issue, the paper builds a differentiated resource price model and designs a pricing strategy that combines vehicle QoE and RSUs cost, in order to maximize the total utility of RSUs. Specifically, the paper propose an Incentive-Driven Collaborative Resource Management (IDCRM) algorithm, which integrates the Soft Actor-Critic (SAC) based Computing Resource Allocation and Pricing (SCRAP) algorithm for dynamic resource allocation and pricing by individual RSU, and RSUs cooperative offloading algorithm to address multi-RSU load balancing. The experimental results demonstrate that our proposed IDCRM effectively maximizes the provider’s revenue while ensuring vehicle QoE and significantly alleviates the resource bottleneck of a single RSU through a collaborative mechanism, achieving efficient load balancing across multi-RSU.