基于多重指标拍卖机制的车联网任务卸载策略

在5G移动边缘计算(MEC)的车联网场景中, 针对车辆任务卸载目标的选择问题, 设计了一种基于任务优先级的服务器选择方案. 综合考虑时间、能耗、成本等因素对卸载位置选择的影响, 提出了基于多重指标拍卖博弈的解决方法. 通过多重指标拍卖机制, 选择最优的MEC服务器为车辆提供任务卸载服务, 实现车辆与RSU协作的贝叶斯纳什均衡. 仿真结果表明, 该方案能在保障车辆任务卸载时间和能耗的约束条件下, 降低任务卸载的总费用, 满足多个性能指标.

Task Offloading Strategy for Internet of Vehicles Based on Multi-index Auction Mechanism

In the Internet of Vehicles (IoV) scenario of 5G mobile edge computing (MEC), a server selection scheme based on task priority is designed for the selection of vehicle task offloading targets. Considering the influence of time, energy consumption, costs, and other factors on the choice of offloading locations, a solution based on the multi-index auction game is proposed. Through the multi-index auction mechanism, the optimal MEC server is selected to provide task offloading services for vehicles, which realizes the Bayesian Nash equilibrium of the cooperation between vehicles and RSU. Simulations show that this scheme can reduce the total cost of task offloading and meet multiple performance indicators under the constraints of guaranteeing vehicle task offloading time and energy consumption.