Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles

TL;DR

This paper introduces a deep Q-network based edge selection algorithm tailored for UAV surveillance, optimizing delay, energy, and overflow to enable real-time performance in dynamic environments.

Contribution

It presents a novel DQN-based edge selection method specifically designed for UAV networks, balancing multiple operational constraints for improved real-time surveillance.

Findings

Algorithm outperforms existing methods in simulation

Balances delay, energy, and overflow effectively

Enables reliable real-time UAV surveillance

Abstract

In this paper, we propose a novel deep Q-network (DQN)-based edge selection algorithm designed specifically for real-time surveillance in unmanned aerial vehicle (UAV) networks. The proposed algorithm is designed under the consideration of delay, energy, and overflow as optimizations to ensure real-time properties while striking a balance for other environment-related parameters. The merit of the proposed algorithm is verified via simulation-based performance evaluation.