基于深度学习的无人机鲁棒监视增强边缘选择
Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles
摘要：
在本文中，我们提出了一种新的基于深度Q网络（DQN）的边缘选择算法，专为无人机（UAV）网络中的实时监控而设计。该算法的设计考虑了延迟，能量和溢出作为优化，以确保实时性，同时达到平衡的其他环境相关的参数。通过基于仿真的性能评估验证了该算法的优点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
