  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.
