Joint Task Offloading and Pricing Strategy for Multi-Tier Vehicular Edge Computing Networks: A Multi-Leader Multi-Follower Stackelberg Game Approach
- 所属单位：山东大学控制科学与工程学院；山东省智能通信与感算融合重点实验室
- 发表刊物：IEEE Transactions on Cognitive Communications and Networking
- 项目来源：国家自然科学基金联合基金；山东省重点研发计划；泰山学者计划
- 关键字：Vehicular edge computing; Stackelberg game; matching game; task offloading; pricing strategy
- 摘要：With the explosion of computation-intensive and latency-sensitive vehicular applications, multi-tier edge computing systems consisting of vehicular edge computing (VEC) server, server vehicles (SeVs) and task vehicles (TaVs) have been proposed to provide task offloading services for resource-limited TaVs. Since normally both edge servers (VEC server and SeVs) and TaVs are selfish and care about only their own benefits, reasonable pricing strategies and task offloading decisions should be carefully designed to motivate them to help TaVs. To this end, we propose a multi-leader multi-follower Stackelberg game-based joint task offloading and pricing strategy for multi-tier VEC systems by assuming edge servers (ESs) to be leaders and TaVs to be the followers. With the game, ESs maximize their own utility by optimizing the pricing strategies of their computing resources. According to the computing resources’ prices set by ESs, the TaVs as the followers make task offloading decisions to maximize their individual utility by jointly designing user association, bandwidth purchasing volume and computing resources purchasing volume. In solving the optimization problem, we prove there exists a unique Stackelberg equilibrium (SE) solution. The task offloading problem of the followers is decomposed into a resource purchasing sub-problem and a user association sub-problem, which are first solved by a joint optimization of bandwidth and computing (JOBC) algorithm and a Gale-Shapley-based matching game bilateral selection (GSMB) algorithm, respectively. Then, based on the followers’ task offloading decisions, the pricing problem is solved by a bisection search method. Finally, a joint optimization task offloading and pricing strategy (JOTPS) algorithm is established to obtain the SE. Simulation results show that the proposed scheme can maximize the individual utility of ESs and TaVs compared to those existing benchmark schemes.
- 备注：中科院一区TOP期刊
- 论文类型：期刊论文
- 是否译文：否
- 发表时间：2025-08-20
- 收录刊物：SCI
