Manara - Qatar Research Repository
Browse
10.1109_ojcoms.2023.3251855.pdf (6.45 MB)

Deep Reinforcement Learning for Internet of Drones Networks: Issues and Research Directions

Download (6.45 MB)
journal contribution
submitted on 2024-02-19, 06:54 and posted on 2024-02-19, 06:54 authored by Noor Aboueleneen, Abdulmalik Alwarafy, Mohamed Abdallah

Internet of Drones (IoD) is one of the promising technologies to enhance the performance of wireless networks. Deploying IoD to assist wireless networks, however, needs to address various design issues. Due to the highly dynamic nature of IoD networks, conventional methods are expected to encounter inadequacies that can be resolved using emerging deep reinforcement learning (DRL) techniques. In this paper, we discuss the application of DRL for addressing various issues in IoD networks. We first overview the main features, types, applications, and services of IoD networks. Then, we briefly discuss some DRL algorithms used to address the issues and challenges of IoD networks. After that, we explain the most crucial issues in IoD networks and discuss some papers that show how DRL can address them. Finally, we provide insights into some promising research directions in the context of using DRL in IoD networks.

Other Information

Published in: IEEE Open Journal of the Communications Society
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1109/ojcoms.2023.3251855

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

IEEE

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU