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A Bio-Inspired Cluster Optimization Schema for Efficient Routing in Vehicular Ad Hoc Networks (VANETs)

Author

Listed:
  • Ghassan Husnain

    (Department of Computer Science, Iqra National University, Peshawar 25100, Pakistan
    Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar 25100, Pakistan)

  • Shahzad Anwar

    (Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar 25100, Pakistan
    Intelligent Information Processing Lab, National Centre of Artificial Intelligence (NCAI), University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Gulbadan Sikander

    (Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar 25100, Pakistan)

  • Armughan Ali

    (Attock Campus, COMSATS University Islamabad, Islamabad 43600, Pakistan)

  • Sangsoon Lim

    (Department of Computer Engineering, Sungkyul University, Anyang 14097, Republic of Korea)

Abstract

Vehicular ad hoc networks (VANETs) are vital to many Intelligent Transportation System (ITS)-enabled technologies, including efficient traffic control, media applications, and encrypted financial transactions. Due to an increase in traffic, vehicular network topology is constantly changing, and sparse vehicle distribution (on highways) hinders network scalability. Thus, there is a challenge for all vehicles (in the network) to maintain a stable route, which would increase network instability. Concerning IoT-based network transportation, this study proposes a bio-inspired, cluster-based algorithm for routing, i.e., the intelligent, probability-based, and nature-inspired whale optimization algorithm (p-WOA), which produces cluster formation in vehicular communication. Various parameters, such as communication range, number of nodes, velocity, and route along the highway were considered, and their probaabilities were incorporated into the fitness function, hence resulting in randomness reduction. Results were compared to existing methods such as Ant Lion Optimizer (ALO) and Grey Wolf Optimization (GWO), demonstrating that the developed p-WOA technique produces an optimal number of cluster heads (CH). The results achieved by calculating the Packet Delivery Ratio (PDR), average throughput, and latency demonstrate the superiority of the proposed method over other well-established methodologies (ALO and GWO). This study confirms statistically that VANETs employing ITS applications optimize their clusters by a factor of 75, which has the twin benefits of decreasing communication costs and routing overhead and extending the life of the cluster as a whole.

Suggested Citation

  • Ghassan Husnain & Shahzad Anwar & Gulbadan Sikander & Armughan Ali & Sangsoon Lim, 2023. "A Bio-Inspired Cluster Optimization Schema for Efficient Routing in Vehicular Ad Hoc Networks (VANETs)," Energies, MDPI, vol. 16(3), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1456-:d:1054341
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    References listed on IDEAS

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    1. Abubakar Bello Tambawal & Rafidah Md Noor & Rosli Salleh & Christopher Chembe & Michael Oche, 2019. "Enhanced weight-based clustering algorithm to provide reliable delivery for VANET safety applications," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-19, April.
    2. Kiran Afzal & Rehan Tariq & Farhan Aadil & Zeshan Iqbal & Nouman Ali & Muhammad Sajid, 2021. "An Optimized and Efficient Routing Protocol Application for IoV," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-32, May.
    3. Jin Wang & Youyuan Wang & Xiang Gu & Liang Chen & Jie Wan, 2018. "ClusterRep: A cluster-based reputation framework for balancing privacy and trust in vehicular participatory sensing," International Journal of Distributed Sensor Networks, , vol. 14(9), pages 15501477188, September.
    4. Farhan Aadil & Khalid Bashir Bajwa & Salabat Khan & Nadeem Majeed Chaudary & Adeel Akram, 2016. "CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-21, May.
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