Enhanced clustering based routing protocol in vehicular ad‐hocnetworks

Journal article


Afia, N., Rizwan, M., Shtwai, A., Almadhor, A., Akhtaruzzaman, Md., Islam, S. and Rahman, H. 2023. Enhanced clustering based routing protocol in vehicular ad‐hocnetworks. IET Electrical Systems in Transportation. 13 (1), pp. 1-15. https://doi.org/10.1049/els2.12069
AuthorsAfia, N., Rizwan, M., Shtwai, A., Almadhor, A., Akhtaruzzaman, Md., Islam, S. and Rahman, H.
Abstract

A vehicular ad‐hoc network (VANET) is derived from a mobile ad‐hoc network that is a part of less infrastructure network design. Vehicular communication in VANET can be achieved using vehicle‐to‐infrastructure (V2I) and vehicle‐to‐vehicle (V2V) communication. A vehicle communicates with other vehicles through onboard units while communicating with roadside units in an infrastructure mode. Secure clustering
is required for the communication between nodes in the whole network. The fundamental problem with the VANET is the instability of the network that occurs due to vehicles' mobile nature, which decreases the network's efficiency. This research proposes an enhanced cluster‐based lifetime protocol ECBLTR that focuses on maximising the network's stability of routing and average throughput. The Sugeno model fuzzy inference system is used for assessing the cluster head (CH) that takes residual energy, local distance, node degree, concentration, and distance from the base station as input parameters. Our enhanced routing protocol shows that the proper channel model with an efficient routing protocol enhances the link throughput of the VANET for fixed network size. Our results show an efficient selection method of CH
through the fuzzy system and a 10% increase in network lifetime. Furthermore, performance evaluation also demonstrates the impact of network sizes and routing
protocols on packet delivery ratio and packet loss, average end‐to‐end delay, and
overhead transmission.

Keywordselectric drives; fuzzy control; transportation
Year2023
JournalIET Electrical Systems in Transportation
Journal citation13 (1), pp. 1-15
PublisherJohn Wiley & Sons Inc.
ISSN2042-9746
Digital Object Identifier (DOI)https://doi.org/10.1049/els2.12069
Web address (URL)https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/els2.12069
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online13 Jan 2023
Publication process dates
Accepted02 Jan 2023
Deposited27 Mar 2024
Permalink -

https://repository.derby.ac.uk/item/q562y/enhanced-clustering-based-routing-protocol-in-vehicular-ad-hocnetworks

Download files


Publisher's version
Enhanced_clustering_based_routing_protocol_in_vehi.pdf
License: CC BY-NC 4.0
File access level: Open

  • 1
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems
Abid, R., Rizwan, M., Alabdulatif, A., Alnajim, A., Alamro, M. and Azrour, M. 2024. Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems. CMC-Computers, Materials & Continua. pp. 1-17. https://doi.org/10.32604/cmc.2024.046880
Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions
Javed, A. R., Saadia, A., Mughal, H., Gadekallu, T.R., Rizwan, M., Maddikunta, P.K.R., Mahmud, M., Liyanage, M and Hussain, A. 2023. Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions. Cognitive Computation. pp. 1-46. https://doi.org/10.1007/s12559-023-10153-4
Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network
Alshahrani, H., Khan, A., Rizwan, M., Al Reshan, M. S., Sulaiman, A. and Shaikh, A. 2023. Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network. Sustainability. 15 (11), pp. 1-18. https://doi.org/10.3390/su15119001
Intelligent Transportation Systems in Smart City: A Systematic Survey
Hassan, M. A., Javed, R., Farhatullah, Granelli, F., Gen, X., Rizwan, M., Ali, S. H., Junaid, H. and Ullah, S. 2023. Intelligent Transportation Systems in Smart City: A Systematic Survey. 2023 International Conference on Robotics and Automation in Industry (ICRAI). IEEE Computer Society. https://doi.org/10.1109/ICRAI57502.2023.10089543
Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network
Rizwan, M., Aysha, S., Maryam, S., AR Javed, Baker, T. and Dhiya, O. 2022. Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network. IEEE Access. 10, pp. 29731 - 29740. https://doi.org/10.1109/ACCESS.2022.3153108
Exploratory Data Analysis, Classification, Comparative Analysis, Case Severity Detection, and Internet of Things in COVID 19 Telemonitoring for Smart Hospitals
Shabbir, A., Shabbir, M., Javed, A. R., Rizwan, M., Iwendi, C. and Chakraborty, C. 2022. Exploratory Data Analysis, Classification, Comparative Analysis, Case Severity Detection, and Internet of Things in COVID 19 Telemonitoring for Smart Hospitals. Journal of Experimental & Theoretical Artificial Intelligence. 35 (4), pp. 507-534. https://doi.org/10.1080/0952813X.2021.1960634