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Mobile-Aware Service Offloading for UAV-Assisted IoV: A Multiagent Tiny Distributed Learning Approach
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AI
DOI：10.1109/JIOT.2024.3373225.png)
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Abstract
En 中文
Unmanned aerial vehicles (UAVs)-assisted multiaccess edge computing (MEC) platforms are becoming an increasingly popular solution for infrastructure-less Internet of Vehicles (IoV) due to their mobility and flexibility. To address the challenges of uneven task offloading and vehicle mobility, in this article, we propose a mobility-aware service offloading and migration scheme for UAV-assisted IoV. We formulate the service placement, service migration (SM), and UAV deployment as an optimization problem to minimize the serving delay of task addressing for IoV, 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 multiagent 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 SM cost.
Keywords:
Autonomous aerial vehicles
Task analysis
Optimization
Costs
Delays
Resource management
Computational modeling
Multiaccess edge computing (MEC)
multiagent 23 deep deterministic policy gradient (MADDPG)
service migration 24 (SM)
service placement
unmanned aerial vehicle (UAV)
Journal
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