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Related Experiment Video

Updated: Nov 8, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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An intelligent cluster optimization algorithm based on Whale Optimization Algorithm for VANETs (WOACNET).

Ghassan Husnain1,2, Shahzad Anwar1

  • 1Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar, Pakistan.

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|April 21, 2021
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Summary
This summary is machine-generated.

This study introduces WOACNET, a novel clustering optimization for Vehicular Ad hoc Networks (VANETs). It enhances cluster head selection and network lifetime, outperforming existing methods.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Vehicular Ad hoc Networks (VANETs) face challenges in scalability and link stability due to high node mobility and sparse distribution.
  • Robust and reliable vehicle communication is crucial for safety and intelligent traffic management systems.
  • Existing clustering methods struggle with dynamic network topologies and dense traffic conditions.

Purpose of the Study:

  • To propose a novel optimization approach for clustering in VANETs.
  • To introduce the Whale Optimization Algorithm for Clustering in Vehicular Ad hoc Networks (WOACNET) for efficient cluster head selection.
  • To enhance the robustness, reliability, and scalability of vehicle communication.

Main Methods:

  • A novel optimization approach considering transmission range, node density, speed, direction, and grid size during clustering.
  • Implementation of the Whale Optimization Algorithm for Clustering in Vehicular Ad hoc Networks (WOACNET).
  • Comparative evaluation of WOACNET against Gray Wolf Optimization (GWO) and Ant Lion Optimization (ALO) using simulations and experiments.

Main Results:

  • WOACNET demonstrates superior performance in selecting cluster heads and optimizing network parameters like transmission range and grid size.
  • The developed method achieved a 46% enhancement in cluster optimization compared to other methods.
  • WOACNET resulted in an F-value of 31.64, significantly outperforming GWO (11.95) and ALO (22.50).

Conclusions:

  • WOACNET provides a significant improvement in cluster optimization and network lifetime for VANETs.
  • The algorithm effectively addresses challenges posed by high node mobility and dense traffic.
  • WOACNET offers a robust and scalable solution for vehicular communication networks.