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Fog Computing Service in the Healthcare Monitoring System for Managing the Real-Time Notification.

Ahmed Elhadad1, Fulayjan Alanazi2, Ahmed I Taloba1

  • 1Department of Computer Science, College of Science and Arts Qurayyat, Jouf University, Sakaka 72388, Saudi Arabia.

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|March 28, 2022
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Summary
This summary is machine-generated.

Fog computing, an Internet of Things (IoT) advancement, enhances healthcare by reducing latency for faster patient monitoring and real-time alerts. This technology improves medical service delivery and supports better health outcomes.

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Area of Science:

  • Computer Science
  • Health Informatics
  • Internet of Things (IoT)

Background:

  • Fog computing is an emerging paradigm in computing systems.
  • Internet of Things (IoT) driven fog computing is being developed for the healthcare industry to improve services and save lives.
  • Reduced latency is crucial for efficient healthcare operations.

Purpose of the Study:

  • To propose a novel framework for healthcare monitoring using fog computing.
  • To manage real-time notifications for patient health status.
  • To leverage machine learning for timely medical alerts.

Main Methods:

  • Utilizing fog computing to reduce signal transmission latency compared to traditional cloud computing.
  • Employing wearable devices with embedded sensors to monitor patient's body temperature, heart rate, and blood pressure.
  • Implementing machine learning algorithms to detect anomalies in monitored vital signs and trigger real-time notifications.

Main Results:

  • The proposed framework enables faster transmission and communication of medical signals.
  • Real-time notifications are generated for healthcare providers upon detecting deviations from normal physiological thresholds.
  • Patients receive timely alerts for medication and diet adherence.

Conclusions:

  • Fog computing offers a viable solution for reducing latency in healthcare systems.
  • The proposed framework enhances patient monitoring and facilitates prompt medical interventions.
  • This approach supports efficient data management for future healthcare research and hospital references.