IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i16p3017-d894229.html
   My bibliography  Save this article

Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review

Author

Listed:
  • Jan Lansky

    (Department of Computer Science and Mathematics, Faculty of Economic Studies, University of Finance and Administration, 101 00 Prague, Czech Republic)

  • Saqib Ali

    (Department of Information Systems, College of Economics and Political Science, Sultan Qaboos University, Al Khoudh, Muscat P.C.123, Oman)

  • Amir Masoud Rahmani

    (Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Douliou 64002, Taiwan)

  • Mohammad Sadegh Yousefpoor

    (Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful 5716963896, Iran)

  • Efat Yousefpoor

    (Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful 5716963896, Iran)

  • Faheem Khan

    (Department of Computer Engineering, Gachon University, Seongnam 13120, Korea)

  • Mehdi Hosseinzadeh

    (Mental Health Research Center, Psychosocial Health Research Institute, Iran University of Medical Sciences, Tehran 1449614535, Iran
    Computer Science, University of Human Development, Sulaymaniyah 0778-6, Iraq)

Abstract

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.

Suggested Citation

  • Jan Lansky & Saqib Ali & Amir Masoud Rahmani & Mohammad Sadegh Yousefpoor & Efat Yousefpoor & Faheem Khan & Mehdi Hosseinzadeh, 2022. "Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review," Mathematics, MDPI, vol. 10(16), pages 1-60, August.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:3017-:d:894229
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/16/3017/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/16/3017/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Amir Masoud Rahmani & Efat Yousefpoor & Mohammad Sadegh Yousefpoor & Zahid Mehmood & Amir Haider & Mehdi Hosseinzadeh & Rizwan Ali Naqvi, 2021. "Machine Learning (ML) in Medicine: Review, Applications, and Challenges," Mathematics, MDPI, vol. 9(22), pages 1-52, November.
    2. Amir Masoud Rahmani & Saqib Ali & Mohammad Sadegh Yousefpoor & Efat Yousefpoor & Rizwan Ali Naqvi & Kamran Siddique & Mehdi Hosseinzadeh, 2021. "An Area Coverage Scheme Based on Fuzzy Logic and Shuffled Frog-Leaping Algorithm (SFLA) in Heterogeneous Wireless Sensor Networks," Mathematics, MDPI, vol. 9(18), pages 1-41, September.
    3. Mohammad Fatin Fatihur Rahman & Shurui Fan & Yan Zhang & Lei Chen, 2021. "A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture," Agriculture, MDPI, vol. 11(1), pages 1-26, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Soukaina Bouarourou & Abderrahim Zannou & El Habib Nfaoui & Abdelhak Boulaalam, 2023. "An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs," Future Internet, MDPI, vol. 15(2), pages 1-27, February.
    2. Jan Lansky & Amir Masoud Rahmani & Mehdi Hosseinzadeh, 2022. "Reinforcement Learning-Based Routing Protocols in Vehicular Ad Hoc Networks for Intelligent Transport System (ITS): A Survey," Mathematics, MDPI, vol. 10(24), pages 1-45, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Muhammad Umair Khan & Mehdi Hosseinzadeh & Amir Mosavi, 2022. "An Intersection-Based Routing Scheme Using Q-Learning in Vehicular Ad Hoc Networks for Traffic Management in the Intelligent Transportation System," Mathematics, MDPI, vol. 10(20), pages 1-25, October.
    2. Amir Masoud Rahmani & Rizwan Ali Naqvi & Efat Yousefpoor & Mohammad Sadegh Yousefpoor & Omed Hassan Ahmed & Mehdi Hosseinzadeh & Kamran Siddique, 2022. "A Q-Learning and Fuzzy Logic-Based Hierarchical Routing Scheme in the Intelligent Transportation System for Smart Cities," Mathematics, MDPI, vol. 10(22), pages 1-31, November.
    3. Gabriel G. R. de Castro & Guido S. Berger & Alvaro Cantieri & Marco Teixeira & José Lima & Ana I. Pereira & Milena F. Pinto, 2023. "Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs," Agriculture, MDPI, vol. 13(2), pages 1-25, January.
    4. Aili Qu & Zhipeng Yan & Haiyan Wei & Liefei Ma & Ruipeng Gu & Qianfeng Li & Weiwei Zhang & Yutan Wang, 2022. "Research on Grape-Planting Structure Perception Method Based on Unmanned Aerial Vehicle Multispectral Images in the Field," Agriculture, MDPI, vol. 12(11), pages 1-16, November.
    5. Eugenio Vera-Salmerón & Carmen Domínguez-Nogueira & José L. Romero-Béjar & José A. Sáez & Emilio Mota-Romero, 2022. "Decision-Tree-Based Approach for Pressure Ulcer Risk Assessment in Immobilized Patients," IJERPH, MDPI, vol. 19(18), pages 1-9, September.
    6. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    7. Jerzy Chojnacki & Aleksandra Pachuta, 2021. "Impact of the Parameters of Spraying with a Small Unmanned Aerial Vehicle on the Distribution of Liquid on Young Cherry Trees," Agriculture, MDPI, vol. 11(11), pages 1-13, November.
    8. Benjamin T. Fraser & Christine L. Bunyon & Sarah Reny & Isabelle Sophia Lopez & Russell G. Congalton, 2022. "Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review," Geographies, MDPI, vol. 2(2), pages 1-38, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:3017-:d:894229. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.