Cooperative Multiagent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control
Won Joon Yun, Soohyun Park, Joongheon Kim, MyungJae Shin, Soyi Jung, Aziz Mohaisen, Jae‐Hyun Kim
Abstract
CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments.This article 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 minimizing overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This article develops a multiagent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this article 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.