AI-Driven Resource Allocation in Edge-Fog Computing: Leveraging Digital Twins for Efficient Healthcare Systems

The evolution of healthcare, driven by remote monitoring and connected devices, is transforming medical service de-livery. Digital twins, virtual replicas of patients, enable continuous monitoring and predictive analysis. However, the rapid growth of real-time health data presents major challenges in resource allocation and processing, especially in cardiac event prediction scenarios. This paper proposes an artificial intelligence-based approach to optimize resource allocation in a fog-edge computing environment, with a focus on Mauritania. The system integrates a deep learning model (CNN-BiLSTM), which achieves 98%accuracy in predicting cardiovascular risks from physiological signals, combined with a Deep Q-Network (DQN) to dynamically decide whether tasks should run at the edge or in the fog. Using IoT sensors, real-time health data is collected and processed intelligently, ensuring low latency and rapid response. Digital twins provide a synchronized virtual representation of the physical system for real-time supervision. This architecture improves resource utilization, reduces processing delays, and enhances responsiveness to critical medical conditions, supporting more accurate cardiac event prediction and timely intervention, especially in resource-constrained environments.

@article{Nouh2025,
  title     = {AI-Driven Resource Allocation in Edge-Fog Computing: Leveraging Digital Twins for Efficient Healthcare Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Brahim Ould Cheikh Mohamed Nouh and Rafika Brahmi and Sidi Cheikh and Ridha Ejbali and Mohamedade Farouk Nanne},
  doi       = {10.14569/IJACSA.2025.01604101},
  url       = {https://doi.org/10.14569/IJACSA.2025.01604101}
}