IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i24p4673-d998981.html
   My bibliography  Save this article

Reinforcement Learning-Based Routing Protocols in Vehicular Ad Hoc Networks for Intelligent Transport System (ITS): A Survey

Author

Listed:
  • Jan Lansky

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

  • Amir Masoud Rahmani

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

  • Mehdi Hosseinzadeh

    (Pattern Recognition and Machine Learning Lab, Gachon University, 1342 Seongnamdaero, Sujeonggu, Seongnam 13120, Republic of Korea)

Abstract

Today, the use of safety solutions in Intelligent Transportation Systems (ITS) is a serious challenge because of novel progress in wireless technologies and the high number of road accidents. Vehicular ad hoc network (VANET) is a momentous element in this system because they can improve safety and efficiency in ITS. In this network, vehicles act as moving nodes and work with other nodes within their communication range. Due to high-dynamic vehicles and their different speeds in this network, links between vehicles are valid for a short time interval. Therefore, routing is a challenging work in these networks. Recently, reinforcement learning (RL) plays a significant role in developing routing algorithms for VANET. In this paper, we review reinforcement learning and its characteristics and study how to use this technique for creating routing protocols in VANETs. We propose a categorization of RL-based routing schemes in these networks. This paper helps researchers to understand how to design RL-based routing algorithms in VANET and improve the existing methods by understanding the challenges and opportunities in this area.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4673-:d:998981
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/24/4673/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/24/4673/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Fatima Belamri & Samra Boulfekhar & Djamil Aissani, 2021. "A survey on QoS routing protocols in Vehicular Ad Hoc Network (VANET)," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 78(1), pages 117-153, September.
    3. 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.
    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. Mohammad Arif & Wooseong Kim, 2023. "Analysis of U-V2X Communications with Non-Clustered and Clustered Jamming in the Presence of Fluctuating UAV Beam Width," Mathematics, MDPI, vol. 11(15), pages 1-28, August.

    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. Kumar Prateek & Nitish Kumar Ojha & Fahiem Altaf & Soumyadev Maity, 2023. "Quantum secured 6G technology-based applications in Internet of Everything," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(2), pages 315-344, February.
    2. 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.

    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:24:p:4673-:d:998981. 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.