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）和智能医疗设备可以随时随地远程监控和筛查患者的健康状况，从而改善了医疗保健系统。由于冠状病毒（新型冠状病毒肺炎）大流行期间患者数量意外大幅增加，因此在发生任何严重疾病或感染之前持续监测患者的健康状况是非常必要的。由于患者产生的大量敏感健康数据不希望自己的私人医疗信息被泄露，因此处理物联网数据的安全问题仍然是一个主要关注点和挑战性问题。面对这一挑战，本文提出了一种远程健康监测模型，该模型应用轻量级块加密方法来为基于云的物联网环境中的健康和医疗数据提供安全性。在该模型中，通过数据挖掘方法分析智能医疗物联网设备感测到的生物数据来预测危急情况，从而确定患者的健康状况，其中使用轻量级安全块加密技术来确保患者的敏感数据受到保护。由于物联网平台资源有限，轻量级块加密方法对此类系统具有至关重要的有效影响。实验结果表明，K-star分类方法在RF、MLP、SVM和J48分类器中取得了最好的结果，准确率为95%，精确率为94.5%，召回率为93.5%，f-score为93.99%。因此，就所取得的成果而言，所建议的模型成功地在基于云的物联网平台中的安全物联网数据的辅助下实现了有效的远程健康监测模型。