Chen Geng, Xu Xianjie, Zeng Qingtian, Zhang Yu-Dong
College of Electronic and Information Engineering, Shandong University of Science and Technology, 266590 Qingdao, China.
School of Computing and Mathematical Sciences, University of Leicester, LE1 7RH Leicester, UK.
Mob Netw Appl. 2023;28(6):2041-2055. doi: 10.1007/s11036-022-02029-y. Epub 2022 Sep 12.
With the continuous development of the Internet of Vehicles(IoV), Vehicle Edge Computing(VEC) has become a key technology for computational resource scheduling, but more and more smart devices are connected to the internet, which makes it difficult for traditional Vehicle Edge Networks(VEN) to deal with tasks in time. In this paper, in order to cope with the challenges of the large number of devices accessing the internet, we propose a vehicle-assisted computation offloading algorithm based on proximal policy optimization(VCOPPO) for User Equipment(UE) tasks, and it combines dynamic parked vehicles incentives mechanism and computational resource allocation strategy by using road vehicles and parked vehicles as edge servers. Firstly, a non-convex optimization problem combining VEN utility and task processing delay is formulated, subject to the constraints of the residual energy and the transmission rate of the task. Secondly, the proposed VCOPPO is used to solve the formulated non-convex optimization problem, and we use stochastic policy to obtain the optimal computation offloading decisions and resource allocation schemes. Finally, the experimental results have shown that the proposed VCOPPO has an excellent performance in network reward and task processing delay respectively, and it can effectively schedule and allocate computational resources. Compared with using Dueling Deep Q Network(Dueling DQN), Deep Q Network(DQN) and Q-learning methods, the proposed VCOPPO improves the network reward by 31%, 18% and 91%, reduces the delay in task processing by 78%, 63% and 74%, respectively.
随着车联网（IoV）的不断发展，车辆边缘计算（VEC）已成为计算资源调度的关键技术，但越来越多的智能设备接入互联网，这使得传统车辆边缘网络（VEN）难以及时处理任务。在本文中，为应对大量设备接入互联网的挑战，我们针对用户设备（UE）任务提出了一种基于近端策略优化的车辆辅助计算卸载算法（VCOPPO），该算法通过将行驶车辆和停放车辆作为边缘服务器，结合了动态停放车辆激励机制和计算资源分配策略。首先，构建了一个结合VEN效用和任务处理延迟的非凸优化问题，并受任务剩余能量和传输速率的约束。其次，使用所提出的VCOPPO来解决所构建的非凸优化问题，我们使用随机策略来获得最优的计算卸载决策和资源分配方案。最后，实验结果表明，所提出的VCOPPO在网络奖励和任务处理延迟方面分别具有优异的性能，并且能够有效地调度和分配计算资源。与使用决斗深度Q网络（Dueling DQN）、深度Q网络（DQN）和Q学习方法相比，所提出的VCOPPO分别将网络奖励提高了31%、18%和91%，将任务处理延迟分别降低了78%、63%和74%。
