Topology Maintenance Optimization Algorithm Based on Deep Reinforcement Learning in High Dynamic Flying Ad-Hoc Networks-Reference-Cited by-同舟云学术

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Topology Maintenance Optimization Algorithm Based on Deep Reinforcement Learning in High Dynamic Flying Ad-Hoc Networks

Published:2023-12-08 Issue: Volume:9 Page:192-198
ISSN:
Container-title:Proceedings of the 2nd International Conference on Signal Processing, Computer Networks and Communications
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Author:

Li Liheng¹^ORCID,Qiu Xiulin²^ORCID,Song Bo¹^ORCID,Ke Yaqi¹^ORCID,Yang Yuwang¹^ORCID

Affiliation:

1. School of computer science and engineering, Nanjing University of Science and Technology, China

2. School of Automation, jiangsu University of Science and Technology, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3654446.3654480

Reference11 articles.

1. Shakhatreh H, Sawalmeh AH, Al-Fuqaha A, Dou Z, Almaita E, Khalil I, Othman NS, Khreishah A, Guizani M. Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges. Ieee Access. 2019, Apr 9; 7: 48572-634.

2. Challenges of developing UAV applications: A project management view

3. Arafat MY, Poudel S, Moh S. Medium access control protocols for flying ad hoc networks: A review. IEEE Sensors Journal. 2020 ,Oct 29; 21(4):4097-121.

4. Malhotra A, Kaur S. A comprehensive review on recent advancements in routing protocols for flying ad hoc networks. Transactions on Emerging Telecommunications Technologies. 2022, Mar;33(3): e3688.

5. Wheeb AH, Al-Jamali NA. Performance analysis of OLSR protocol in mobile ad hoc networks. iJIM. 2022, 16(01):107.

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