IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i20p3731-d938944.html
   My bibliography  Save this article

An Intersection-Based Routing Scheme Using Q-Learning in Vehicular Ad Hoc Networks for Traffic Management in the Intelligent Transportation System

Author

Listed:
  • Muhammad Umair Khan

    (School of Computing, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Korea)

  • Mehdi Hosseinzadeh

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    School of Medicine and Pharmacy, Duy Tan University, Da Nang 550000, Vietnam
    Computer Science, University of Human Development, Sulaymaniyah 0778-6, Iraq)

  • Amir Mosavi

    (Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
    Faculty of Informatics, Obuda University, 1034 Budapest, Hungary)

Abstract

Vehicular ad hoc networks (VANETs) create an advanced framework to support the intelligent transportation system and increase road safety by managing traffic flow and avoiding accidents. These networks have specific characteristics, including the high mobility of vehicles, dynamic topology, and frequent link failures. For this reason, providing an efficient and stable routing approach for VANET is a challenging issue. Reinforcement learning (RL) can solve the various challenges and issues of vehicular ad hoc networks, including routing. Most of the existing reinforcement learning-based routing methods are incompatible with the dynamic network environment and cannot prevent congestion in the network. Network congestion can be controlled by managing traffic flow. For this purpose, roadside units (RSUs) must monitor the road status to be informed about traffic conditions. In this paper, an intersection-based routing method using Q-learning (IRQ) is presented for VANETs. IRQ uses both global and local views in the routing process. For this reason, a dissemination mechanism of traffic information is introduced to create these global and local views. According to the global view, a Q-learning-based routing technique is designed for discovering the best routes between intersections. The central server continuously evaluates the created paths between intersections to penalize road segments with high congestion and improve the packet delivery rate. Finally, IRQ uses a greedy strategy based on a local view to find the best next-hop node in each road segment. NS2 software is used for analyzing the performance of the proposed routing approach. Then, IRQ is compared with three methods, including IV2XQ, QGrid, and GPSR. The simulation results demonstrate that IRQ has an acceptable performance in terms of packet delivery rate and delay. However, its communication overhead is higher than IV2XQ.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3731-:d:938944
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/20/3731/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/20/3731/
    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.
    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. 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. 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. 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. 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.
    4. 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.

    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:20:p:3731-:d:938944. 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.