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Fog and Cloud Computing Assisted IoT Model Based Personal Emergency Monitoring and Diseases Prediction Services

机译：FOG和云计算辅助IOT模型的个人应急监测和疾病预测服务

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摘要

Along with the rapid development of modern high-tech and the change of people's awareness of healthy life, the demand for personal healthcare services is gradually increasing. The rapid progress of information and communication technology and medical and bio technology not only improves personal healthcare services, but also brings the fact that the human being has entered the era of longevity. At present, there are many researches focused on various wearable sensing devices and implant devices and Internet of Things in order to capture personal daily life health information more conveniently and effectively, and significant results have been obtained, such as fog computing. To provide personal healthcare services, the fog and cloud computing is an effective solution for sharing health information. The health big data analysis model can provide personal health situation reports on a daily basis, and the gene sequencing can provide hereditary disease prediction. However, the injury mortality and emergency diseases since long ago caused death and great pain for the family. And there are no effective rescue methods to save precious lives and no methods to predict the disease morbidity likelihood. The purpose of this research is to capture personal daily health information based on sensors and monitoring emergency situations with the help of fog computing and mobile applications, and disease prediction based on cloud computing and big data analysis. Through the comparison of test results it was proved that the proposed emergency monitoring based on fog and cloud computing and the diseases prediction model based on big data analysis not only gain more of the rescue time than the traditional emergency treatment method, but they also accumulate lots of different personal healthcare related experience. The Taian 960 hospital of PLA and the Yanbian Hospital as IM testbed were joined to provide emergency monitoring tests, and to ensure the CVD and CVA morbidity likelihood medical big data analysis, the people around Taian city participated in personal health tests. Through the project, the five network layers architecture and integrated MAPE-K Model based EMDPS platform not only made the cooperation between hospitals feasible to deal with emergency situations, but also the Internet medicine for the disease prediction was built.
机译：随着现代高科技的快速发展和人们对健康生活的认识的变化，对个人医疗服务的需求逐渐增加。信息和通信技术的快速进步和医疗和生物技术不仅可以提高个人医疗服务，而且还带来了人类进入长寿时代的事实。目前，有许多研究专注于各种可穿戴传感装置和植入装置和物联网，以便更方便，有效地捕获个人日常生活健康信息，并且获得了显着的结果，例如雾计算。提供个人医疗服务，雾和云计算是共享健康信息的有效解决方案。健康大数据分析模型可以每天提供个人健康状况报告，并且基因测序可以提供遗传性疾病预测。然而，很久以前引起了伤病死亡率和急诊疾病导致了家庭的死亡和巨大痛苦。并且没有有效的救援方法可以节省珍贵的生命，没有方法可以预测疾病的发病率可能性。本研究的目的是根据传感器捕获个人日常健康信息，并在雾计算和移动应用中监测紧急情况，以及基于云计算和大数据分析的疾病预测。通过比较测试结果，证明了基于雾和云计算的拟议应急监测以及基于大数据分析的疾病预测模型不仅可以获得比传统的紧急治疗方法更多的救援时间，但它们也积累了批次不同的个人医疗保健相关经验。泰安960医院的解放军和延边医院随着IM试验台加入，提供应急监测测试，并确保CVD和CVA发病率似的医学大数据分析，泰安市的人们参加了个人健康测试。通过该项目，五个网络层架构和集成的Mape-K模型的EMDPS平台不仅使医院之间的合作可行处理紧急情况，而且还建造了疾病预测的互联网医学。

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