Abstract
Computation offloading in Mobile Edge Computing (MEC) represents a key technology for the future of the Internet of Vehicles (IoV), reducing the time and energy consumption of vehicles for computation tasks, while Unmanned Aerial Vehicles (UAVs) equipped with computation resources can act as aerial based stations to provide computation offloading services to vehicles moving on the road. In this paper, a joint dynamic resource allocation and UAV trajectory optimization scheme is proposed. In the scheme, an UAV is deployed with an edge server to execute the partially offloaded computation tasks from multiple vehicles. The goal of the problem is to maximize the total computation workload while minimizing the energy consumption of all vehicles by jointly optimizing the computation frequency, the wireless transmission power, the task offloading decisions of vehicles, as well as the flight angle of the UAV in each time slot. Since the problem is non-convex in continuous action space, we consider the Twin Delayed Deep Deterministic (TD3) policy gradient algorithm to solve the problem. Experimental results demonstrate the effectiveness of the TD3 policy gradient algorithm in the proposed optimization scheme in terms of the convergence speed and the system reward.
This work was supported in part by NSFC of China under Grant 62261051.
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Li, R., Sun, H. (2024). Joint Dynamic Resource Allocation and Trajectory Optimization for UAV-Assisted Mobile Edge Computing in Internet of Vehicles. In: Sellami, M., Vidal, ME., van Dongen, B., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2023. Lecture Notes in Computer Science, vol 14353. Springer, Cham. https://doi.org/10.1007/978-3-031-46846-9_28
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