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Enhancing network stability in VANETs using nature inspired algorithm for intelligent transportation system.

Sandeep Yerrathi1, Venugopal Pakala1

  • 1School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Plos One
|January 11, 2024
PubMed
Summary
This summary is machine-generated.

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An improved beluga whale optimizer-Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images.

PloS one·2023
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The African Vulture Optimization Based Clustering Algorithm (AVOCA) enhances vehicular ad-hoc networks (VANETs) by creating optimal clusters, significantly reducing cluster count and improving stability for intelligent transportation systems.

Area of Science:

  • Intelligent Transportation Systems (ITS)
  • Internet of Vehicles (IoV)
  • Vehicular Ad-hoc Networks (VANETs)

Background:

  • VANETs, crucial for ITS and IoV, face challenges like dynamic topology, node mobility, and bandwidth constraints.
  • These dynamics lead to frequent link failures, instability, and Quality of Service (QoS) concerns, exacerbated by NP-hard problem complexities.
  • Existing clustering techniques often create excessive clusters, increasing resource consumption, communication overhead, and latency.

Purpose of the Study:

  • To enhance stability in VANETs by increasing cluster lifetime through the generation of optimal clusters.
  • To address the limitations of current clustering methods that result in high resource usage and latency.
  • To introduce a novel nature-inspired algorithm for efficient cluster formation in vehicular networks.

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Main Methods:

  • Implementation of the African Vulture Optimization Based Clustering Algorithm (AVOCA), a nature-inspired meta-heuristic algorithm.
  • Focus on optimizing cluster generation to improve stability, load balancing, and resource utilization.
  • Exploration of a taxonomy for Cluster Head (CH) selection, coordination, and maintenance to reduce communication costs.

Main Results:

  • AVOCA significantly reduces the number of clusters generated compared to state-of-the-art algorithms.
  • Demonstrated reductions include 40% less than CAMONET, 45% less than SAMNET, 43% less than i-WOA, and 38% less than HHO.
  • The algorithm achieves load optimization, efficient resource utilization, and mitigates hidden node challenges.

Conclusions:

  • AVOCA effectively generates optimal clusters, leading to enhanced stability and extended cluster lifetime in VANETs.
  • The proposed algorithm outperforms existing methods in terms of cluster count reduction and efficiency.
  • AVOCA offers a promising solution for improving the performance and reliability of Intelligent Transportation Systems.