论文标题
在MEC辅助的车辆网络中，基于游戏理论的任务迁移的能源延迟最小化
Energy-Delay Minimization of Task Migration Based on Game Theory in MEC-assisted Vehicular Networks
论文作者
论文摘要
具有强大计算能力并且接近车辆节点的路边单元（RSU）已被广泛用于处理车辆节点的延迟和计算密集型任务。但是，由于其高移动性，在收到任务处理结果之前，车辆可能会从RSU的覆盖范围中排出。在本文中，我们提出了一个移动边缘计算辅助的车辆网络，在该网络中，车辆可以通过车辆对车辆（V2V）链接或附近的RSU通过车辆到基础设施链接将其任务卸载到附近的车辆。这些任务还通过V2V链接或基础架构到基础结构（I2I）链接迁移，以避免车辆无法从RSU中接收已处理的任务的情况。考虑从卸载任务和迁移任务的相同链接中的相互干扰，我们构建了一个车辆卸载基于决策的游戏，以最大程度地减少计算开销。我们证明，游戏始终可以利用有限的改进属性来达到NASH平衡和融合。然后，我们提出了一种任务迁移（TM）算法，其中包括三种任务处理方法和两种任务迁移方法。基于TM算法，提出了计算开销最小化卸载（COMO）算法。广泛的仿真结果表明，提出的TM和COMO算法减少了开销的计算并提高了任务处理的成功率。
Roadside units (RSUs), which have strong computing capability and are close to vehicle nodes, have been widely used to process delay- and computation-intensive tasks of vehicle nodes. However, due to their high mobility, vehicles may drive out of the coverage of RSUs before receiving the task processing results. In this paper, we propose a mobile edge computing-assisted vehicular network, where vehicles can offload their tasks to a nearby vehicle via a vehicle-to-vehicle (V2V) link or a nearby RSU via a vehicle-to-infrastructure link. These tasks are also migrated by a V2V link or an infrastructure-to-infrastructure (I2I) link to avoid the scenario where the vehicles cannot receive the processed task from the RSUs. Considering mutual interference from the same link of offloading tasks and migrating tasks, we construct a vehicle offloading decision-based game to minimize the computation overhead. We prove that the game can always achieve Nash equilibrium and convergence by exploiting the finite improvement property. We then propose a task migration (TM) algorithm that includes three task-processing methods and two task-migration methods. Based on the TM algorithm, computation overhead minimization offloading (COMO) algorithm is presented. Extensive simulation results show that the proposed TM and COMO algorithms reduce the computation overhead and increase the success rate of task processing.
