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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
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    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.
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    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.

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