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.

中文翻译：

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使用深度学习的强化边缘选择对无人驾驶飞行器进行鲁棒监视

在本文中，我们提出了一种新颖的基于深度 Q 网络 (DQN) 的边缘选择算法，专为无人机 (UAV) 网络中的实时监视而设计。所提出的算法是在考虑延迟、能量和溢出作为优化的情况下设计的，以确保实时性，同时平衡其他与环境相关的参数。通过基于仿真的性能评估验证了所提出算法的优点。