Edge Computing Task Offloading Optimization for a UAV-Assisted Internet of Vehicles via Deep Reinforcement Learning

Abstract

In the context of the unmanned aerial vehicle (UAV)-assisted vehicular networking system, more network factors need to be considered to ensure the safe operation of connected vehicles. A large volume of delay-sensitive and computationally demanding tasks necessitate offloading to UAVs or roadside units for processing. And the efficient allocation of various network resources of vehicles, UAVs, and roadside units under constrained conditions determines the efficiency of task offloading. Deep reinforcement learning (DRL) has demonstrated its efficacy as an experienced approach for solving such problems. In this article, we delve into the utilization of deep reinforcement learning to design an efficient UAV-assisted vehicular edge computing task offloading strategy. Under the constraints of limited network bandwidth and limited UAV power, the trajectory and the task offloading strategy of the UAV are jointly optimized. The primary objective of our proposed strategy is to achieve a notable reduction in the system delay of the edge computing network. Given the dynamic variability of tasks arrival, we employ a long short-term memory (LSTM) network with the attention mechanism and a deep deterministic policy gradient (DDPG) algorithm to effectively model the optimization problem as a Markov decision process. This approach can obtain the optimal policy through interactive learning from the UAV and the vehicle environment. The experiment results illustrate that this strategy outperforms other baseline strategies in terms of convergence speed, network delay, and task offloading ratio.