Mobile-edge computing (MEC) has been witnessed as a promising solution for the vehicular task offloading. Due to the limited computing resource of individual MEC servers, it faces challenges when higher requirements are put forward for timely task processing of a large amount of computations in the emerging vehicular applications. In this article, we strive to realize the efficient vehicular task offloading via heat-aware MEC cooperation from the game theory perspective. Here, the heat indicates the vehicle density and is tightly related to the requests of vehicle users when they drive through the hot zones. Specifically, a deep learning-based prediction method is proposed, capturing the dynamic time-varying heat value of the hot zones based on the analysis of the real-world private car trajectory data. To identify the role of MEC in the cooperation, we take the time-delay constraint into consideration for the task offloading. To realize MEC grouping for task offloading in MEC cooperation, we formulate the MEC grouping as a utility maximization problem via designing a noncooperative game-theoretic strategy selection based on regret-matching. Furthermore, we derive the correlated equilibrium and prove that the fast convergence can be achieved. Extensive simulation results validate the effectiveness of the proposed vehicular task offloading approach under various system parameters, such as computation workload, time slots, and MEC servers number. The proposed method outperforms the existing methods, which is able to significantly reduce the task complete delay, and in the meantime enhance the MEC energy efficiency with end users’ quality-of-experience guaranteed.

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通过热感知MEC合作进行的车辆任务卸载：具有相关平衡的博弈论方法

移动边缘计算（MEC）已被证明是用于车辆任务卸载的有前途的解决方案。由于各个MEC服务器的计算资源有限，因此在新兴的车辆应用中提出了对大量计算的及时任务处理提出更高的要求时，它面临着挑战。在本文中，我们从博弈论的角度努力通过热感知MEC合作来实现有效的车辆任务卸载。在此，热量指示车辆密度，并且与车辆用户在炎热区域中行驶时的要求紧密相关。具体而言，提出了一种基于深度学习的预测方法，该方法基于对现实世界私家车轨迹数据的分析来捕获热点区域的动态时变热值。为了确定MEC在合作中的作用，我们考虑了任务卸载的时间延迟约束。为了实现MEC合作中任务分担的MEC分组，我们通过设计基于后悔匹配的非合作博弈策略选择，将MEC分组表述为效用最大化问题。此外，我们推导了相关均衡，并证明可以实现快速收敛。大量的仿真结果验证了所提出的车辆任务卸载方法在各种系统参数（例如计算工作量，时隙和MEC服务器数量）下的有效性。所提出的方法优于现有方法，能够显着减少任务完成的延迟，