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A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning.

Deepak Gupta1, Rakesh Rathi1

  • 1Department of Computer Science and Engineering, Government Engineering College Ajmer, Ajmer 305001, India.

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Summary
This summary is machine-generated.

This study introduces a novel weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to improve vehicular ad hoc networks (VANETs). The w-SMNO significantly reduces communication delays and enhances data delivery for safer, more efficient autonomous driving.

Keywords:
NS2.35VANETleadermachine learningoptimizationrelayspider monkey

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Vehicular ad hoc networks (VANETs) are crucial for connected and autonomous vehicles, aiming to improve road safety and traffic efficiency.
  • Challenges in VANETs include communication delays, dynamic topology, and variable speeds, hindering trustworthy and high-quality services.
  • Effective data dissemination and relay node selection are vital for overcoming these VANET limitations.

Purpose of the Study:

  • To propose a novel nature-inspired optimization method for efficient relay node selection in VANETs.
  • To enhance system accuracy and minimize errors in machine learning models for VANET data dissemination.
  • To reduce communication delays and improve the reliability of data transmission in dynamic vehicular environments.

Main Methods:

  • Development of a weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) algorithm.
  • Implementation of a dynamic weight assignment and configuration model using neural networks with backpropagation and gradient descent.
  • Introduction of a distinct algorithm for effective relay selection within multiple spider monkey groups.

Main Results:

  • The w-SMNO method demonstrated a 35.7% increase in network coverage.
  • A significant 41.2% reduction in end-to-end communication delay was achieved.
  • Improvements include a 36.4% increase in message delivery rate and a 38.4% decrease in collision rate compared to existing methods.

Conclusions:

  • The proposed w-SMNO method offers a substantial improvement in VANET performance.
  • This approach effectively addresses challenges related to communication delays and enhances data dissemination reliability.
  • The findings suggest w-SMNO as a promising solution for optimizing VANETs in autonomous driving scenarios.