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Topic

An Energy-Saving Model for Integrating IoT with Fog and Cloud Computing in Telehealth Applications

Department of Computer Science

Date & location

• Wednesday, April 9, 2025
• 4:30 P.M.
• Virtual Defence

Examining Committee

Supervisory Committee

• Dr. Sudhakar Ganti, Department of Computer Science, University of Victoria (Supervisor)
• Dr. Kui Wu, Department of Computer Science, UVic (Member)
• Dr. Alex Kuo, School of Health Information Science, UVic (Outside Member)

External Examiner

• Dr. Hung-Wen Chiu, Graduate Institute of Biomedical Informatics, Taipei Medical University

Chair of Oral Examination

• Dr. Stephen Lindsay, Department of Psychology, UVic

Abstract

This dissertation presents an energy-efficient model for integrating Internet of Things (IoT) devices with fog and cloud computing platforms, specifically designed for telehealth applications. As the deployment of telehealth IoT devices continues to grow, the demand for efficient, real-time data processing and energy conservation becomes increasingly critical. This research addresses these challenges by proposing a hybrid architecture that combines the low-latency benefits of fog computing with the scalable resources of cloud computing. The model reduces energy consumption by processing data locally through fog nodes, minimizing the need for constant communication with cloud servers. This not only decreases latency but also optimizes the use of computational resources, making the system more adaptable to the dynamic demands of telehealth services. The model is further enhanced by an adaptive resource scaling algorithm, which dynamically adjusts processing capacity based on workload, ensuring both efficiency and reliability in critical healthcare applications. Simulations studies demonstrate the effectiveness of the model in reducing energy consumption and improving system performance for real-time telehealth monitoring. The results show significant improvements in data processing speed, energy efficiency, and resource utilization compared to traditional cloud-only architectures. This work contributes to the ongoing development of sustainable telehealth solutions by pro viding a robust framework for IoT-fog-cloud integration that meets the stringent demands of modern healthcare systems.