Akhbarifar Samira, Javadi Hamid Haj Seyyed, Rahmani Amir Masoud, Hosseinzadeh Mehdi
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Mathematics and Computer Science, Shahed University, Tehran, Iran.
Pers Ubiquitous Comput. 2023;27(3):697-713. doi: 10.1007/s00779-020-01475-3. Epub 2020 Nov 16.
Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients' health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients' health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients' health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients' sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.
物联网（IoT）和智能医疗设备通过实现随时随地对患者健康状况进行远程监测和筛查，改善了医疗保健系统。由于在冠状病毒（新型COVID-19）大流行期间患者数量意外大幅增加，在任何严重疾病或感染发生之前持续监测患者的健康状况变得至关重要。考虑到要传输大量不愿透露其私人医疗信息的患者所产生的敏感健康数据，处理物联网数据的安全问题作为一个主要关注点和具有挑战性的问题仍然存在。面对这一挑战，本文提出了一种远程健康监测模型，该模型在基于云的物联网环境中应用轻量级块加密方法来保障健康和医疗数据的安全。在该模型中，通过数据挖掘方法预测关键情况来确定患者的健康状况，以分析智能医疗物联网设备感知到的他们的生物数据，其中使用轻量级安全块加密技术来确保患者的敏感数据得到保护。由于物联网平台资源有限，轻量级块加密方法对这类系统具有至关重要的有效影响。实验结果表明，在随机森林（RF）、多层感知器（MLP）、支持向量机（SVM）和J48分类器中，K星分类方法取得了最佳结果，准确率为95%，精确率为94.5%，召回率为93.5%，F值为93.99%。因此，基于所取得的结果，所提出的模型成功地实现了一个在基于云的物联网平台中由安全的物联网数据辅助的有效的远程健康监测模型。
