Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control
This work addresses the need for flexible and reliable surveillance in smart city environments, though it appears incremental as it builds on existing multi-agent reinforcement learning approaches for UAV control.
The paper tackles the problem of reliable surveillance in smart cities using multi-UAV systems by developing a multi-agent deep reinforcement learning scheme to autonomously manage UAV deployment and communication, achieving improved performance in coverage, user support, and computational costs over state-of-the-art methods.
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.