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Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)

Author

Listed:
  • Sahar Ebadinezhad

    (Department of Computer Engineering, Cyprus International University, Lefkoşa 99258, Turkey
    Department of Computer Information System, Near East University, Nicosia TRNC 99138, Turkey)

  • Ziya Dereboylu

    (Department of Electronic and Electrical Engineering, Cyprus International University, Lefkoşa 99258, Turkey)

  • Enver Ever

    (Computer Engineering, Middle East Technical University, Northern Cyprus Campus, Güzelyurt 99738, Turkey)

Abstract

The Internet of Vehicles (IoV) has recently become an emerging promising field of research due to the increasing number of vehicles each day. IoV is vehicle communications, which is also a part of the Internet of Things (IoT). Continuous topological changes of vehicular communications are a significant issue in IoV that can affect the change in network scalability, and the shortest routing path. Therefore, organizing efficient and reliable intercommunication routes between vehicular nodes, based on conditions of traffic density is an increasingly challenging issue. For such issues, clustering is one of the solutions, among other routing protocols, such as geocast, topology, and position-based routing. This paper focuses mainly on the scalability and the stability of the topology of IoV. In this study, a novel intelligent system-based algorithm is proposed (CACOIOV), which stabilizes topology by using a metaheuristic clustering algorithm based on the enhancement of Ant Colony Optimization (ACO) in two distinct stages for packet route optimization. Another algorithm, called mobility Dynamic Aware Transmission Range on Local traffic Density (DA-TRLD), is employed together with CACOIOV for the adaptation of transmission range regarding of density in local traffic. The results presented through NS-2 simulations show that the new protocol is superior to both Ad hoc On-demand Distance Vector (AODV) routing and (ACO) protocols based on evaluating routing performance in terms of throughput, packet delivery, and drop ratio, cluster numbers, and average end-to-end delay.

Suggested Citation

  • Sahar Ebadinezhad & Ziya Dereboylu & Enver Ever, 2019. "Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)," Sustainability, MDPI, vol. 11(9), pages 1-22, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:9:p:2624-:d:228862
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    References listed on IDEAS

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    1. Min He & Zheng Guan & Liyong Bao & Zhaoxu Zhou & Marco Anisetti & Ernesto Damiani & Gwanggil Jeon, 2019. "Performance Analysis of a Polling-Based Access Control Combining with the Sleeping Schema in V2I VANETs for Smart Cities," Sustainability, MDPI, vol. 11(2), pages 1-22, January.
    2. Gravel, Marc & Price, Wilson L. & Gagne, Caroline, 2002. "Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic," European Journal of Operational Research, Elsevier, vol. 143(1), pages 218-229, November.
    3. 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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    Cited by:

    1. Abida Sharif & Jian Ping Li & Muhammad Asim Saleem & Gunasekaran Manogran & Seifedine Kadry & Abdul Basit & Muhammad Attique Khan, 2021. "A dynamic clustering technique based on deep reinforcement learning for Internet of vehicles," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 757-768, March.

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