The task offloading technology plays a vital role in the Internet of Vehicles (IoV), by satisfying the diversified demands of the vehicles, such as the energy consumption and processing latency of the computing task. Different from the previous works, on the one hand, they ignored the wireless interference of communications among vehicle-to-vehicle (V2V), as well as between vehicles and roadside units (RSU); on the other hand, the available resources of parked vehicles on the roadside and other moving vehicles on the road are also ignored. In this paper, first of all, we adopt a truncated Gaussian distribution for modeling the vehicle moving speed, instead of the simplistic average speed models in prior studies. Then, with the consideration of wireless interference and effective communication duration existing in V2V and RSUs, we establish an analytical framework of the task offloading, characterized by the energy consumption and processing delay, by integrating computing resources of parked/moving vehicles and RSUs. Furthermore, inspired by the method of multi-agent deterministic policy gradient (MADDPG), we address a joint optimization of the energy consumption and processing delay of the computing task, while ensuring the load balancing of the resources. Finally, the simulations demonstrate the effectiveness and correctness of the proposed MADDPG. In particular, compared with the current popular methods of the task offloading, the MADDPG shows the best performance, in terms of convergence speed, energy consumption and processing delay.

翻译：任务卸载技术在车辆互联网中发挥着至关重要的作用，它通过满足车辆对计算任务的能耗和处理时延等多样化需求来实现这一目标。与以往研究不同，一方面，先前工作忽略了车辆间通信以及车辆与路边单元之间通信存在的无线干扰；另一方面，也忽视了路边停放车辆及道路上其他行驶车辆的可利用资源。本文首先采用截断高斯分布对车辆移动速度进行建模，而非先前研究中采用的简化平均速度模型。随后，在考虑V2V及RSU通信中存在的无线干扰与有效通信时长的前提下，通过整合停放/行驶车辆与RSU的计算资源，建立了一个以能耗和处理时延为特征的任务卸载分析框架。进一步地，受多智能体确定性策略梯度方法的启发，我们在确保资源负载均衡的同时，对计算任务的能耗与处理时延进行了联合优化。最后，仿真实验验证了所提MADDPG方法的有效性与正确性。特别地，与当前主流的任务卸载方法相比，MADDPG在收敛速度、能耗和处理时延方面均表现出最优性能。