The demanding computing capacity of emerging vehicular applications has emerged as a challenge in Internet of vehicles (IoVs). Multi-access edge computing (MEC) can significantly enhance computing capability and prolong battery life of vehicles through offloading computation-intensive tasks for edge computing. Considering the impact of vehicles’ mobility on communication quality, this paper provides an energy-efficient computation offloading scheme in vehicular edge computing networks (VECN). An energy-efficiency cost (EEC) minimization problem is formulated to make a tradeoff between latency and energy consumption for completing computational tasks. Since multiple variables and time-varying channel conditions make the formulated problem difficult to solve, we transform the original non-convex problem into a two-level optimization problem and develop an iterative distributed algorithm to obtain an optimal solution. Numerical results verify the convergence and superiority of the proposed algorithm.

新兴的车辆应用程序对计算能力的苛刻要求已成为车辆互联网（IoV）的一项挑战。通过分担计算密集型任务的边缘计算，多路访问边缘计算（MEC）可以显着增强计算能力并延长车辆的电池寿命。考虑到车辆移动性对通信质量的影响，本文提供了一种在车辆边缘计算网络（VECN）中的节能计算卸载方案。制定了能效成本（EEC）最小化问题，以在延迟和能耗之间进行权衡以完成计算任务。由于多个变量和时变的信道条件使制定的问题难以解决，我们将原始的非凸问题转化为两级优化问题，并开发了一种迭代分布式算法来获得最优解。数值结果验证了该算法的收敛性和优越性。