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December 5, 2025Al-Iraqia Journal of Scientific Engineering ResearchOpen Access

Adaptive Task Scheduling in Fog Computing Using Learning Automata and RBF Neural Networks for Optimized Performance and Energy Efficiency

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Authors

SRSobhan Roshani

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Overview

Adaptive task scheduling optimizes energy consumption and resource allocation in fog computing, suggesting enhanced performance with neural networks.

Key Points

  • Energy consumption is minimized with the proposed task scheduling approach using learning automata, leading to improved efficiency.
  • The method integrates a radial basis function model to predict relationships among makespan, fitness, and resource allocation.
  • Learning automata and neural networks enable efficient task scheduling, surpassing traditional methods in energy-aware computation.
  • This optimization may allow for reduced environmental impact through better resource use in fog computing environments.

Cite This Study

Sobhan Roshani (2025) studied this question.

synapsesocial.com/papers/694023c82d562116f28fca0ahttps://doi.org/10.58564/ijser.4.4.2025.352
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