Islam Umar, Alatawi Mohammed Naif, Alqazzaz Ali, Alamro Sulaiman, Shah Babar, Moreira Fernando
Department of Computer Science, IQRA National University, Swat Campus, KPK, Peshawar, Pakistan.
Information Technology Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia.
Sci Rep. 2025 Jul 15;15(1):25655. doi: 10.1038/s41598-025-09696-3.
The advancement of the Internet of Medical Things (IoMT) has transformed healthcare delivery by enabling real-time health monitoring. However, it introduces critical challenges related to latency and, more importantly, the secure handling of sensitive patient data. Traditional cloud-based architectures often struggle with latency and data protection, making them inefficient for real-time healthcare scenarios. To address these challenges, we propose a Hybrid Fog-Edge Computing Architecture tailored for effective real-time health monitoring in IoMT systems. Fog computing enables processing of time-critical data closer to the data source, reducing response time and relieving cloud system overload. Simultaneously, edge computing nodes handle data preprocessing and transmit only valuable information-defined as abnormal or high-risk health signals such as irregular heart rate or oxygen levels-using rule-based filtering, statistical thresholds, and lightweight machine learning models like Decision Trees and One-Class SVMs. This selective transmission optimizes bandwidth without compromising response quality. The architecture integrates robust security measures, including end-to-end encryption and distributed authentication, to counter rising data breaches and unauthorized access in IoMT networks. Real-life case scenarios and simulations are used to validate the model, evaluating latency reduction, data consolidation, and scalability. Results demonstrate that the proposed architecture significantly outperforms cloud-only models, with a 70% latency reduction, 30% improvement in energy efficiency, and 60% bandwidth savings. Additionally, the time required for threat detection was halved, ensuring faster response to security incidents. This framework offers a flexible, secure, and efficient solution ideal for time-sensitive healthcare applications such as remote patient monitoring and emergency response systems.
医疗物联网（IoMT）的发展通过实现实时健康监测改变了医疗服务的提供方式。然而，它带来了与延迟相关的关键挑战，更重要的是，带来了敏感患者数据的安全处理问题。传统的基于云的架构在延迟和数据保护方面常常面临困难，使其在实时医疗场景中效率低下。为应对这些挑战，我们提出了一种混合雾边缘计算架构，专为IoMT系统中的有效实时健康监测量身定制。雾计算能够在更靠近数据源的位置处理对时间要求严格的数据，减少响应时间并减轻云系统的过载。同时，边缘计算节点处理数据预处理，并仅使用基于规则的过滤、统计阈值以及决策树和一类支持向量机等轻量级机器学习模型，传输定义为异常或高风险健康信号（如不规则心率或血氧水平）等有价值的信息。这种选择性传输在不影响响应质量的情况下优化了带宽。该架构集成了强大的安全措施，包括端到端加密和分布式认证，以应对IoMT网络中不断增加的数据泄露和未经授权的访问。使用实际案例场景和模拟来验证该模型，评估延迟降低、数据整合和可扩展性。结果表明，所提出的架构明显优于仅基于云的模型，延迟降低了70%，能源效率提高了30%，带宽节省了60%。此外，威胁检测所需的时间减半，确保对安全事件的响应更快。该框架为远程患者监测和应急响应系统等对时间敏感的医疗应用提供了一个灵活、安全且高效的解决方案。
