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一种用于物联网医疗生态系统的安全远程患者监测框架。

A secured remote patient monitoring framework for IoMT ecosystems.

作者信息

Bhattacharya Pronaya, Mukherjee Anwesha, Bhushan Bharat, Gupta Shashi Kant, Gadekallu Thippa Reddy, Zhu Zhu

机构信息

Department of Computer Science and Engineering, Amity School of Engineering and Technology, and Research and Innovation Cell, Amity University, Kolkata, West Bengal, India.

Department of Computer Science, Mahishadal Raj College, Mahishadal, Purba Medinipur, West Bengal, India.

出版信息

Sci Rep. 2025 Jul 2;15(1):22882. doi: 10.1038/s41598-025-04774-y.


DOI:10.1038/s41598-025-04774-y
PMID:40594005
Abstract

Recent advancement in the Internet of Medical Things (IoMT) allows patients to set up smart sensors and medical devices to connect to remote healthcare setups. However, existing remote patient monitoring solutions predominantly rely on persistent connectivity and centralized cloud processing, resulting in high latency and energy consumption, particularly in environments with intermittent network availability. There is a need for real-time IoMT computing closer to the dew, with secured and privacy-enabled access to healthcare data. To address this, we propose the DeW-IoMT framework, which includes a dew layer in the roof-fog-cloud systems. Notably, our approach introduces a novel roof computing layer that acts as an intermediary gateway between the dew and fog layers, enhancing data security and reducing communication latency. The proposed architecture provides critical services during disconnected operations and minimizes computational requirements for the fog-cloud system. We measure heart rate using the pulse sensor, where the dew layer sets up conditions for remote patient monitoring with low overheads. We experimentally analyze the proposed scheme's response time, energy dissipation, and bandwidth and present a simulation analysis of the fog layer through the iFogSim software. Our results at dew demonstrate a reduction in response time by 74.61%, a decrease in energy consumption by 38.78%, and a 33.56% reduction in task data compared to traditional cloud-centric models. Our findings validate the framework viability in scalable IoMT setups.

摘要

医疗物联网(IoMT)的最新进展使患者能够设置智能传感器和医疗设备,以连接到远程医疗保健设施。然而,现有的远程患者监测解决方案主要依赖于持续连接和集中式云处理,导致高延迟和高能耗,特别是在网络可用性间歇性的环境中。需要在更接近数据源的位置进行实时IoMT计算,并确保对医疗数据的安全和隐私保护访问。为了解决这个问题,我们提出了DeW-IoMT框架,该框架在屋顶-雾-云系统中包含一个数据源层。值得注意的是,我们的方法引入了一个新颖的屋顶计算层,它作为数据源层和雾层之间的中间网关,增强了数据安全性并减少了通信延迟。所提出的架构在断开连接的操作期间提供关键服务,并最大限度地减少雾-云系统的计算需求。我们使用脉搏传感器测量心率,其中数据源层为低开销的远程患者监测设置条件。我们通过实验分析了所提出方案的响应时间、能量消耗和带宽,并通过iFogSim软件对雾层进行了模拟分析。我们在数据源层的结果表明,与传统的以云为中心的模型相比,响应时间减少了74.61%,能量消耗降低了38.78%,任务数据减少了33.56%。我们的研究结果验证了该框架在可扩展的IoMT设置中的可行性。

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本文引用的文献

[1]
CONFRONT: Cloud-fog-dew based monitoring framework for COVID-19 management.

Internet Things (Amst). 2021-12

[2]
: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics.

IEEE Access. 2022-7-13

[3]
Diabetes-related avoidable hospitalisations and its relationship with primary healthcare resourcing in China: A cross-sectional study from Sichuan Province.

Health Soc Care Community. 2022-7

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