Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
In recent years, flying ad hoc networks have attracted the attention of many researchers in industry and universities due to easy deployment, proper operational costs, and diverse applications. Designing an efficient routing protocol is challenging due to unique characteristics of these networks suc...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/16/3017 |
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| author | Jan Lansky Saqib Ali Amir Masoud Rahmani Mohammad Sadegh Yousefpoor Efat Yousefpoor Faheem Khan Mehdi Hosseinzadeh |
| author_facet | Jan Lansky Saqib Ali Amir Masoud Rahmani Mohammad Sadegh Yousefpoor Efat Yousefpoor Faheem Khan Mehdi Hosseinzadeh |
| author_sort | Jan Lansky |
| collection | DOAJ |
| description | In recent years, flying ad hoc networks have attracted the attention of many researchers in industry and universities due to easy deployment, proper operational costs, and diverse applications. Designing an efficient routing protocol is challenging due to unique characteristics of these networks such as very fast motion of nodes, frequent changes of topology, and low density. Routing protocols determine how to provide communications between drones in a wireless ad hoc network. Today, reinforcement learning (RL) provides powerful solutions to solve the existing problems in the routing protocols, and designs autonomous, adaptive, and self-learning routing protocols. The main purpose of these routing protocols is to ensure a stable routing solution with low delay and minimum energy consumption. In this paper, the reinforcement learning-based routing methods in FANET are surveyed and studied. Initially, reinforcement learning, the Markov decision process (MDP), and reinforcement learning algorithms are briefly described. Then, flying ad hoc networks, various types of drones, and their applications, are introduced. Furthermore, the routing process and its challenges are briefly explained in FANET. Then, a classification of reinforcement learning-based routing protocols is suggested for the flying ad hoc networks. This classification categorizes routing protocols based on the learning algorithm, the routing algorithm, and the data dissemination process. Finally, we present the existing opportunities and challenges in this field to provide a detailed and accurate view for researchers to be aware of the future research directions in order to improve the existing reinforcement learning-based routing algorithms. |
| first_indexed | 2024-03-09T04:07:36Z |
| format | Article |
| id | doaj.art-4424623a16904190bcbb5134d2210487 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T04:07:36Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-4424623a16904190bcbb5134d22104872023-12-03T14:03:58ZengMDPI AGMathematics2227-73902022-08-011016301710.3390/math10163017Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A ReviewJan Lansky0Saqib Ali1Amir Masoud Rahmani2Mohammad Sadegh Yousefpoor3Efat Yousefpoor4Faheem Khan5Mehdi Hosseinzadeh6Department of Computer Science and Mathematics, Faculty of Economic Studies, University of Finance and Administration, 101 00 Prague, Czech RepublicDepartment of Information Systems, College of Economics and Political Science, Sultan Qaboos University, Al Khoudh, Muscat P.C.123, OmanFuture Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Douliou 64002, TaiwanDepartment of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful 5716963896, IranDepartment of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful 5716963896, IranDepartment of Computer Engineering, Gachon University, Seongnam 13120, KoreaMental Health Research Center, Psychosocial Health Research Institute, Iran University of Medical Sciences, Tehran 1449614535, IranIn recent years, flying ad hoc networks have attracted the attention of many researchers in industry and universities due to easy deployment, proper operational costs, and diverse applications. Designing an efficient routing protocol is challenging due to unique characteristics of these networks such as very fast motion of nodes, frequent changes of topology, and low density. Routing protocols determine how to provide communications between drones in a wireless ad hoc network. Today, reinforcement learning (RL) provides powerful solutions to solve the existing problems in the routing protocols, and designs autonomous, adaptive, and self-learning routing protocols. The main purpose of these routing protocols is to ensure a stable routing solution with low delay and minimum energy consumption. In this paper, the reinforcement learning-based routing methods in FANET are surveyed and studied. Initially, reinforcement learning, the Markov decision process (MDP), and reinforcement learning algorithms are briefly described. Then, flying ad hoc networks, various types of drones, and their applications, are introduced. Furthermore, the routing process and its challenges are briefly explained in FANET. Then, a classification of reinforcement learning-based routing protocols is suggested for the flying ad hoc networks. This classification categorizes routing protocols based on the learning algorithm, the routing algorithm, and the data dissemination process. Finally, we present the existing opportunities and challenges in this field to provide a detailed and accurate view for researchers to be aware of the future research directions in order to improve the existing reinforcement learning-based routing algorithms.https://www.mdpi.com/2227-7390/10/16/3017flying ad hoc networks (FANET)reinforcement learning (RL)routingartificial intelligence (AI)unmanned ariel vehicles (UAVs) |
| spellingShingle | Jan Lansky Saqib Ali Amir Masoud Rahmani Mohammad Sadegh Yousefpoor Efat Yousefpoor Faheem Khan Mehdi Hosseinzadeh Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review Mathematics flying ad hoc networks (FANET) reinforcement learning (RL) routing artificial intelligence (AI) unmanned ariel vehicles (UAVs) |
| title | Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review |
| title_full | Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review |
| title_fullStr | Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review |
| title_full_unstemmed | Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review |
| title_short | Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review |
| title_sort | reinforcement learning based routing protocols in flying ad hoc networks fanet a review |
| topic | flying ad hoc networks (FANET) reinforcement learning (RL) routing artificial intelligence (AI) unmanned ariel vehicles (UAVs) |
| url | https://www.mdpi.com/2227-7390/10/16/3017 |
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