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A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization
Ahmed A H Abdellatif1,2, Aman Singh2,3, Abdulaziz Aldribi2,4
1Department of Pharmaceutics, College of Pharmacy, Qassim University Buraydah, Buraydah, Saudi Arabia.
Computational Intelligence and Neuroscience
|October 10, 2022
Summary
This study introduces an optimized fog-assisted smart healthcare system using advanced analytics and genetic algorithms. The novel framework significantly reduces latency and execution time for faster, more accurate patient care.
Area of Science:
- Internet of Things (IoT) and Fog Computing in Healthcare
Background:
- Fog networks in smart healthcare face challenges in managing dynamic, heterogeneous environments and increasing workloads.
- Efficient processing of data from sensor-based devices is crucial for speeding up smart healthcare system functionality.
Purpose of the Study:
- To present a novel framework for smart healthcare designed to minimize latency and execution time.
- To enhance the efficiency and stability of fog-assisted smart healthcare systems.
Main Methods:
- Utilized Principal Component Analysis (PCA) for effective feature reduction.
- Employed Support Vector Machines (SVM) with a Radial Basis Function (RBF) kernel for data classification.
- Implemented a genetic algorithm for optimizing workload distribution on fog nodes.
- Incorporated data preprocessing for cleaner data generation.
Main Results:
- The proposed framework demonstrated superior performance compared to existing systems.
- Significant improvements were observed in latency, execution time, system accuracy, and system stability.
Conclusions:
- The amalgamation of intelligent techniques and optimization algorithms effectively boosts the overall system efficiency.
- The novel framework offers a promising solution for advanced fog-assisted smart healthcare applications.
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