Cooperative Multi-Agent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control

TL;DR

This paper proposes a multi-agent deep reinforcement learning approach for autonomous UAV control to enhance reliable city surveillance, focusing on coverage, communication, and recovery under uncertainty.

Contribution

It introduces a novel multi-agent deep reinforcement learning scheme for autonomous UAV management, improving coverage and reliability in smart city surveillance.

Findings

Outperforms existing algorithms in coverage and support capabilities

Reduces computational costs compared to state-of-the-art methods

Enhances autonomous recovery in uncertain UAV operations

Abstract

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.