IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i24p4720-d1001203.html
   My bibliography  Save this article

Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network

Author

Listed:
  • Hazem Noori Abdulrazzak

    (Institute of Power Engineering (IPE), Universiti Tenaga Nasional (UNITEN), Kajang 43000, Malaysia)

  • Goh Chin Hock

    (Institute of Power Engineering (IPE), Universiti Tenaga Nasional (UNITEN), Kajang 43000, Malaysia)

  • Nurul Asyikin Mohamed Radzi

    (Institute of Power Engineering (IPE), Universiti Tenaga Nasional (UNITEN), Kajang 43000, Malaysia)

  • Nadia M. L. Tan

    (Institute of Power Engineering (IPE), Universiti Tenaga Nasional (UNITEN), Kajang 43000, Malaysia
    Zhejiang Key Laboratory on the More Electric Aircraft Technologies, University of Nottingham Ningbo China, Ningbo 315100, China)

  • Chiew Foong Kwong

    (Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
    Next Generation Internet of Everything, University of Nottingham Ningbo China, Ningbo 315100, China)

Abstract

Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. The problems with the K-Means algorithm concern the selection of a suitable number of clusters, the creation of a highly reliable cluster, and achieving high similarity within a cluster. To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. Firstly, RK-Means creates multi-groups of vehicles using a covering rough set based on effective parameters. Secondly, the K-value-calculating algorithm computes the optimal number of clusters. Finally, the classical K-Means algorithm is applied to create the vehicle clusters for each covering rough set group. The datasets used in this work were imported from Simulation of Urban Mobility (SUMO), representing two highway scenarios, high-density and low-density. Four evaluation indexes, namely, the root mean square error (RMSE), silhouette coefficient (SC), Davies–Bouldin (DB) index, and Dunn index (DI), were used directly to test and evaluate the results of the clustering. The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. The result of the compression showed that RK-Means had high cluster similarity, greater reliability, and error reductions of 32.5% and 24.2% compared with OK-Means and K-Means++, respectively.

Suggested Citation

  • Hazem Noori Abdulrazzak & Goh Chin Hock & Nurul Asyikin Mohamed Radzi & Nadia M. L. Tan & Chiew Foong Kwong, 2022. "Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network," Mathematics, MDPI, vol. 10(24), pages 1-27, December.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4720-:d:1001203
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/24/4720/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/24/4720/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mingwei Feng & Haiqing Yao & Ioan Ungurean, 2022. "A Roadside Unit Deployment Optimization Algorithm for Vehicles Serving as Obstacles," Mathematics, MDPI, vol. 10(18), pages 1-24, September.
    2. Chunhui Yuan & Haitao Yang, 2019. "Research on K-Value Selection Method of K-Means Clustering Algorithm," J, MDPI, vol. 2(2), pages 1-10, June.
    Full references (including those not matched with items on IDEAS)

    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. Xie, Hailun & Eames, Matt & Mylona, Anastasia & Davies, Hywel & Challenor, Peter, 2024. "Creating granular climate zones for future-proof building design in the UK," Applied Energy, Elsevier, vol. 357(C).
    2. Yunhwan Kim, 2023. "Exploring Organizational Self-(re)presentations on Visual Social Media: Computational Analysis of Startups’ Instagram Photos Based on Unsupervised Learning," SAGE Open, , vol. 13(4), pages 21582440231, December.
    3. Heller, Yuval & Tubul, Itay, 2023. "Strategies in the repeated prisoner’s dilemma: A cluster analysis," MPRA Paper 117444, University Library of Munich, Germany.
    4. Chao Yan & Yao Yan & Zhiyu Wan & Ziqi Zhang & Larsson Omberg & Justin Guinney & Sean D. Mooney & Bradley A. Malin, 2022. "A Multifaceted benchmarking of synthetic electronic health record generation models," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    5. Cuomo, Maria Teresa & Tortora, Debora & Colosimo, Ivan & Ricciardi Celsi, Lorenzo & Genovino, Cinzia & Festa, Giuseppe & La Rocca, Michele, 2023. "Segmenting with big data analytics and Python: A quantitative exploratory analysis of household savings," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    6. Zhang, Yanquan & Chang, Ruidong & Zuo, Jian & Shabunko, Veronika & Zheng, Xian, 2023. "Regional disparity of residential solar panel diffusion in Australia: The roles of socio-economic factors," Renewable Energy, Elsevier, vol. 206(C), pages 808-819.
    7. Şebnem Koltan Yılmaz & Sibel Şener, 2022. "Analysis of The Countries According to The Prosperity Level with Data Mining," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 10(2), pages 85-104, December.
    8. Yao, S. & Peralta-Braz, P. & Alamdari, M.M. & Ruiz, R.O. & Atroshchenko, E., 2024. "Optimal design of piezoelectric energy harvesters for bridge infrastructure: Effects of location and traffic intensity on energy production," Applied Energy, Elsevier, vol. 355(C).
    9. Yen, Barbara T.H. & Li, Jun-Sheng, 2022. "Route-based performance evaluation for airlines – A metafrontier data envelopment analysis approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    10. Yunhwan Kim, 2022. "#Nomask on Instagram: Exploring Visual Representations of the Antisocial Norm on Social Media," IJERPH, MDPI, vol. 19(11), pages 1-14, June.
    11. Yunhwan Kim & Sunmi Lee, 2022. "#ShoutYourAbortion on Instagram: Exploring the Visual Representation of Hashtag Movement and the Public’s Responses," SAGE Open, , vol. 12(2), pages 21582440221, April.
    12. Massimo Arnone & Alberto Costantiello & Angelo Leogrande & Syed Kafait Hussain Naqvi & Cosimo Magazzino, 2024. "Financial Stability and Innovation: The Role of Non-Performing Loans," FinTech, MDPI, vol. 3(4), pages 1-41, October.
    13. Zhang, Ying & Robu, Valentin & Cremers, Sho & Norbu, Sonam & Couraud, Benoit & Andoni, Merlinda & Flynn, David & Poor, H. Vincent, 2024. "Modelling the formation of peer-to-peer trading coalitions and prosumer participation incentives in transactive energy communities," Applied Energy, Elsevier, vol. 355(C).
    14. Jujie Wang & Zhenzhen Zhuang, 2023. "A novel cluster based multi-index nonlinear ensemble framework for carbon price forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6225-6247, July.
    15. Ghaemi, Zahra & Tran, Thomas T.D. & Smith, Amanda D., 2022. "Comparing classical and metaheuristic methods to optimize multi-objective operation planning of district energy systems considering uncertainties," Applied Energy, Elsevier, vol. 321(C).
    16. Chen, Hao, 2022. "Cluster-based ensemble learning for wind power modeling from meteorological wind data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    17. Xinghua Wang & Xixian Liu & Fucheng Zhong & Zilv Li & Kaiguo Xuan & Zhuoli Zhao, 2023. "A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors," Sustainability, MDPI, vol. 15(20), pages 1-20, October.

    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:24:p:4720-:d:1001203. 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.