论文标题
使用深度学习的增强边缘选择，以在无人驾驶汽车中进行强大的监视
Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles
论文作者
论文摘要
在本文中，我们提出了一种新型的深Q-Network（DQN）的边缘选择算法，专为无人机（UAV）网络的实时监视而设计。提出的算法是在考虑到延迟，能量和溢出作为优化的考虑方面设计的，以确保实时属性，同时为其他与环境相关的参数达到平衡。通过基于模拟的性能评估来验证所提出算法的优点。
In this paper, we propose a novel deep Q-network (DQN)-based edge selection algorithm designed specifically for real-time surveillance in unmanned aerial vehicle (UAV) networks. The proposed algorithm is designed under the consideration of delay, energy, and overflow as optimizations to ensure real-time properties while striking a balance for other environment-related parameters. The merit of the proposed algorithm is verified via simulation-based performance evaluation.
