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arXiv · 2009.09647

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

Soohyun Park·Jeman Park·David Mohaisen·Joongheon Kim
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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.

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Soohyun Park, Jeman Park, David Mohaisen, Joongheon Kim. 2020-09-21. Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles. https://arxiv.org/abs/2009.09647

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