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Intelligent cities use Vehicular Adhoc Networks (VANETs) to ensure transport safety and prevent accidents in real-time. A stable network depends on the efficient cluster and Cluster Head (CH) election because of high variability and unpredictable drive behaviour. The present investigation introduces a CH selection strategy that improves transmission efficiency in congested metropolitan areas by utilising Adaptive Neighbouring Behaviour-Incentivization (ANB-I). Conventional graph-based clusters result in diminished connection dependability and interruptions. It culminates in decreased cost and latency. When it comes to CH selection, the ANB-I system takes transport behaviour into account, giving more weight to CHs that have more consistent trends and greater connection times. Its objective is to compare the different incentivised parameters to enhance VANET efficiency over a range of vehicle volumes; it has been evaluated through MATLAB and SUMO tools. The study analyses involve 100 to 800 vehicles for implementing the ANB-I. Also represents the comparison between two selected clusters based on the adaptive behaviour.

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Incentivized-Based Adaptive Neighbour Behaviour Approach for Vehicular Adhoc Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Ashish Kumari,
  • Shailender Kumar,
  • Ram Shringar Raw

摘要

Intelligent cities use Vehicular Adhoc Networks (VANETs) to ensure transport safety and prevent accidents in real-time. A stable network depends on the efficient cluster and Cluster Head (CH) election because of high variability and unpredictable drive behaviour. The present investigation introduces a CH selection strategy that improves transmission efficiency in congested metropolitan areas by utilising Adaptive Neighbouring Behaviour-Incentivization (ANB-I). Conventional graph-based clusters result in diminished connection dependability and interruptions. It culminates in decreased cost and latency. When it comes to CH selection, the ANB-I system takes transport behaviour into account, giving more weight to CHs that have more consistent trends and greater connection times. Its objective is to compare the different incentivised parameters to enhance VANET efficiency over a range of vehicle volumes; it has been evaluated through MATLAB and SUMO tools. The study analyses involve 100 to 800 vehicles for implementing the ANB-I. Also represents the comparison between two selected clusters based on the adaptive behaviour.