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A Q-Learning and Fuzzy Logic-Based Hierarchical Routing Scheme in the Intelligent Transportation System for Smart Cities

Author

Listed:
  • Amir Masoud Rahmani

    (Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Douliou 64002, Taiwan
    These authors contributed equally to this work.)

  • Rizwan Ali Naqvi

    (School of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
    These authors contributed equally to this work.)

  • Efat Yousefpoor

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

  • Mohammad Sadegh Yousefpoor

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

  • Omed Hassan Ahmed

    (Department of Information Technology, University of Human Development, Sulaymaniyah 0778-6, Iraq)

  • Mehdi Hosseinzadeh

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

  • Kamran Siddique

    (Department of Information and Communication Technology, School of Computing and Data Science, Xiamen University Malaysia, Sepang 43900, Malaysia
    Department of Computer Science and Engineering, University of Alaska Anchorage, Anchorage, AK 99508, USA)

Abstract

A vehicular ad hoc network (VANET) is the major element of the intelligent transportation system (ITS). The purpose of ITS is to increase road safety and manage the movement of vehicles. ITS is known as one of the main components of smart cities. As a result, there are critical challenges such as routing in these networks. Recently, many scholars have worked on this challenge in VANET. They have used machine learning techniques to learn the routing proceeding in the networks adaptively and independently. In this paper, a Q-learning and fuzzy logic-based hierarchical routing protocol (QFHR) is proposed for VANETs. This hierarchical routing technique consists of three main phases: identifying traffic conditions, routing algorithm at the intersection level, and routing algorithm at the road level. In the first phase, each roadside unit (RSU) stores a traffic table, which includes information about the traffic conditions related to four road sections connected to the corresponding intersection. Then, RSUs use a Q-learning-based routing method to discover the best path between different intersections. Finally, vehicles in each road section use a fuzzy logic-based routing technique to choose the foremost relay node. The simulation of QFHR has been executed on the network simulator version 2 (NS2), and its results have been presented in comparison with IRQ, IV2XQ, QGrid, and GPSR in two scenarios. The first scenario analyzes the result based on the packet sending rate (PSR). In this scenario, QFHR gets better the packet delivery rate by 2.74%, 6.67%, 22.35%, and 29.98% and decreases delay by 16.19%, 22.82%, 34.15%, and 59.51%, and lowers the number of hops by 6.74%, 20.09%, 2.68%, and 12.22% compared to IRQ, IV2XQ, QGrid, and GPSR, respectively. However, it increases the overhead by approximately 9.36% and 11.34% compared to IRQ and IV2XQ, respectively. Moreover, the second scenario evaluates the results with regard to the signal transmission radius (STR). In this scenario, QFHR increases PDR by 3.45%, 8%, 23.29%, and 26.17% and decreases delay by 19.86%, 34.26%, 44.09%, and 68.39% and reduces the number of hops by 14.13%, 32.58%, 7.71%, and 21.39% compared to IRQ, IV2XQ, QGrid, and GPSR, respectively. However, it has higher overhead than IRQ (11.26%) and IV2XQ (25%).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4192-:d:967959
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    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.
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    Cited by:

    1. Boriana Vatchova & Yordanka Boneva, 2023. "Design of Fuzzy and Conventional Controllers for Modeling and Simulation of Urban Traffic Light System with Feedback Control," Mathematics, MDPI, vol. 11(2), pages 1-11, January.

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