CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a
key technology for security in smart city environments. This paper creates a
case where the UAVs with CCTV-cameras fly over the city area for flexible and
reliable surveillance services. UAVs should be deployed to cover a large area
while minimize overlapping and shadow areas for a reliable surveillance system.
However, the operation of UAVs is subject to high uncertainty, necessitating
autonomous recovery systems. This work develops a multi-agent deep
reinforcement learning-based management scheme for reliable industry
surveillance in smart city applications. The core idea this paper employs is
autonomously replenishing the UAV's deficient network requirements with
communications. Via intensive simulations, our proposed algorithm outperforms
the state-of-the-art algorithms in terms of surveillance coverage, user support
capability, and computational costs.

该研究论文使用基于 CCTV 摄像头的无人机实现对智能城市的安全监测，并通过开发一种多智能体深度强化学习的管理方案，自主补充缺失网络要求，实现可靠的工业监测，该算法具有优于当前最先进算法的覆盖面积、用户支持能力和计算成本。