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Review

A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks

Department of Computer Engineering, Chosun University, Gwangju 61452, Korea
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Author to whom correspondence should be addressed.
Academic Editor: Nurul I. Sarkar
Electronics 2021, 10(4), 449; https://doi.org/10.3390/electronics10040449
Received: 20 January 2021 / Revised: 8 February 2021 / Accepted: 8 February 2021 / Published: 11 February 2021
(This article belongs to the Special Issue Wireless Sensor Networks in Intelligent Transportation Systems)
Flying ad-hoc networks (FANET) are one of the most important branches of wireless ad-hoc networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks and communicating with each other. Nowadays FANETs are being used for commercial and civilian applications such as handling traffic congestion, remote data collection, remote sensing, network relaying, and delivering products. However, there are some major challenges, such as adaptive routing protocols, flight trajectory selection, energy limitations, charging, and autonomous deployment that need to be addressed in FANETs. Several researchers have been working for the last few years to resolve these problems. The main obstacles are the high mobility and unpredictable changes in the topology of FANETs. Hence, many researchers have introduced reinforcement learning (RL) algorithms in FANETs to overcome these shortcomings. In this study, we comprehensively surveyed and qualitatively compared the applications of RL in different scenarios of FANETs such as routing protocol, flight trajectory selection, relaying, and charging. We also discuss open research issues that can provide researchers with clear and direct insights for further research. View Full-Text
Keywords: flying Ad-hoc network; reinforcement learning; routing protocol; flight trajectory; unmanned air vehicles flying Ad-hoc network; reinforcement learning; routing protocol; flight trajectory; unmanned air vehicles
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MDPI and ACS Style

Rezwan, S.; Choi, W. A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks. Electronics 2021, 10, 449. https://doi.org/10.3390/electronics10040449

AMA Style

Rezwan S, Choi W. A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks. Electronics. 2021; 10(4):449. https://doi.org/10.3390/electronics10040449

Chicago/Turabian Style

Rezwan, Sifat, and Wooyeol Choi. 2021. "A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks" Electronics 10, no. 4: 449. https://doi.org/10.3390/electronics10040449

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