Energy-Optimized Lightweight DRL for Computational Offloading in UAV-IoV Systems

Abstract

The integration of 5G/6G networks with the Internet of Vehicles (IoV) requires efficient computational offloading for data-intensive applications such as autonomous driving and augmented reality. Although Unmanned Aerial Vehicles (UAVs) offer agile mobile edge computing (MEC) capabilities, their operational efficiency is hampered by high mobility, limited battery life, and the complexity of joint resource optimization. Existing offloading strategies often fail to simultaneously optimize latency, energy consumption, and resource utilization under dynamic IoV conditions. This paper proposes a novel Energy-Optimized Lightweight Deep Reinforcement Learning (DRL) framework for intelligent task offloading in UAV-assisted IoV networks. Our approach leverages a simplified Double Deep Q-Network (DDQN) to dynamically manage task partitioning by intelligent offloading decisions, UAV trajectory planning through optimized path forecasting, and resource allocation through adaptive computation distribution. Key innovations include a streamlined state-space design that reduces computational overhead by 30% and a composite reward function that balances latency and energy objectives. These are realized by a prioritized experience replay mechanism and a target network separation strategy that enhances learning stability. Experimental results demonstrate that our framework achieves a task success rate of 98.5%, reduces latency by 40%, and maintains a 78.1%. The results confirm the framework’s superiority, demonstrating significant improvements over its base architecture (DQN), its enhanced variant (DDQN), and other state-of-the-art baselines like MADDPG and game-theoretic approaches, thereby providing a robust solution for practical UAV-IoV deployments.