An efficient scheduling scheme for intelligent driving tasks in a novel vehicle-edge architecture considering mobility and load balancing

Abstract

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.