With the continuous popularization and evolution of 5G and 6G, mobile edge computing has achieved rapid development. This study explores the New Generation Mobile Edge Computing (NGMEC) architecture, which leverages numerous mobile nodes to provide users with enhanced computing services. Despite its advantages, NGMEC faces challenges such as high node mobility, load balancing difficulties, and incomplete environmental perception by agents, particularly in intelligent driving task offloading scenarios. We address these challenges by introducing novel applications of NGMEC and proposing specialized algorithms for node selection and load balancing. Furthermore, to tackle the issue of environmental perception incompleteness in NGMEC task offloading, we develop the Gated Recurrent Self-Encoding Deep Reinforcement Learning (GRSE-DRL) algorithm. Our research also includes the development of two platforms: the End-Edge-Cloud Simulation Experiment Platform and the Edge Computing Offloading Algorithm Energy Efficiency Test Platform. Experimental results demonstrate that our proposed scheme effectively maintains load balance among nodes, enhances task completion and connection success rates, and optimizes the trade-off between transmission delay and intelligent driving algorithms’ effectiveness, enabling more efficient decision-making.

中文翻译：

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考虑移动性和负载平衡的新型车辆边缘架构中智能驾驶任务的高效调度方案


随着5G、6G的不断普及和演进，移动边缘计算取得了快速发展。本研究探讨了新一代移动边缘计算（NGMEC）架构，该架构利用大量移动节点为用户提供增强的计算服务。尽管NGMEC有其优势，但它也面临着节点移动性高、负载均衡困难以及代理环境感知不完整等挑战，特别是在智能驾驶任务卸载场景中。我们通过引入 NGMEC 的新颖应用并提出用于节点选择和负载平衡的专用算法来应对这些挑战。此外，为了解决 NGMEC 任务卸载中的环境感知不完整性问题，我们开发了门控循环自编码深度强化学习（GRSE-DRL）算法。我们的研究还包括两个平台的开发：端边云仿真实验平台和边缘计算分流算法能效测试平台。实验结果表明，我们提出的方案有效地保持了节点之间的负载平衡，提高了任务完成率和连接成功率，并优化了传输延迟和智能驾驶算法有效性之间的权衡，从而实现更高效的决策。