Guo Yunyong, Ganti Sudhakar, Wu Yi
Computer Science Department, University of Victoria, Victoria, BC, Canada.
JMIR Biomed Eng. 2024 Mar 6;9:e50175. doi: 10.2196/50175.
The increasing adoption of telehealth Internet of Things (IoT) devices in health care informatics has led to concerns about energy use and data processing efficiency.
This paper introduces an innovative model that integrates telehealth IoT devices with a fog and cloud computing-based platform, aiming to enhance energy efficiency in telehealth IoT systems.
The proposed model incorporates adaptive energy-saving strategies, localized fog nodes, and a hybrid cloud infrastructure. Simulation analyses were conducted to assess the model's effectiveness in reducing energy consumption and enhancing data processing efficiency.
Simulation results demonstrated significant energy savings, with a 2% reduction in energy consumption achieved through adaptive energy-saving strategies. The sample size for the simulation was 10-40, providing statistical robustness to the findings.
The proposed model successfully addresses energy and data processing challenges in telehealth IoT scenarios. By integrating fog computing for local processing and a hybrid cloud infrastructure, substantial energy savings are achieved. Ongoing research will focus on refining the energy conservation model and exploring additional functional enhancements for broader applicability in health care and industrial contexts.
远程医疗物联网（IoT）设备在医疗保健信息学中的日益普及引发了对能源使用和数据处理效率的担忧。
本文介绍了一种创新模型，该模型将远程医疗物联网设备与基于雾计算和云计算的平台集成，旨在提高远程医疗物联网系统的能源效率。
所提出的模型包含自适应节能策略、本地化雾节点和混合云基础设施。进行了模拟分析，以评估该模型在降低能耗和提高数据处理效率方面的有效性。
模拟结果表明实现了显著的节能，通过自适应节能策略能耗降低了2%。模拟的样本量为10 - 40，为研究结果提供了统计稳健性。
所提出的模型成功解决了远程医疗物联网场景中的能源和数据处理挑战。通过集成用于本地处理的雾计算和混合云基础设施，实现了大幅节能。正在进行的研究将专注于完善节能模型，并探索更多功能增强，以在医疗保健和工业环境中实现更广泛的应用。
