Mobile-Aware Service Offloading for UAV-Assisted IoV: A Multiagent Tiny Distributed Learning Approach
Yan Liu, Peng Lin, Mengya Zhang, Zhizhong Zhang, F. Richard Yu
Abstract
Unmanned aerial vehicles (UAVs)-assisted multi-access edge computing (MEC) platforms are becoming an increasingly popular solution for infrastructure-less Internet of Vehicles (IoVs) due to their mobility and flexibility. To address the challenges of uneven task offloading and vehicle mobility, in this paper, we propose a mobility-aware service offloading and migration scheme for UAV-assisted IoVs. We formulate the service placement, service migration, and UAV deployment as an optimization problem to minimize the serving delay of task addressing for IoVs, under a predefined long-term migration cost budget. To solve the problem, we use the Lyapunov optimization method to transform the long-term optimization into a real-time optimization problem. Additionally, we design a multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve the problem. Compared with traditional central optimization methods, the proposed algorithm can achieve a near-global optimal policy by leveraging only local observation information. Simulation results show that the proposed MADDPG algorithm can achieve good convergence performance, and the proposed scheme can achieve quasi-optimal performance in terms of serving delay, service offloading rate, and service migration cost.