|本期目录/Table of Contents|

[1]胡建强.一种基于云雾辅助的移动健康监护系统设计[J].厦门大学学报(自然科学版),2019,58(04):608-613.[doi:10.6043/j.issn.0438-0479.201810016]
 HU Jianqiang.Design on a cloud & fog-assisted mobile healthcare monitoring system[J].Journal of Xiamen University(Natural Science),2019,58(04):608-613.[doi:10.6043/j.issn.0438-0479.201810016]
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一种基于云雾辅助的移动健康监护系统设计(PDF)
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
58卷
期数:
2019年04期
页码:
608-613
栏目:
研究论文
出版日期:
2019-07-28

文章信息/Info

Title:
Design on a cloud & fog-assisted mobile healthcare monitoring system
文章编号:
0438-0479(2019)04-0608-06
作者:
胡建强
厦门理工学院计算机与信息工程学院,福建省物联网应用高校重点实验室,福建 厦门 361024
Author(s):
HU Jianqiang
Key Laboratory of Internet-of-Things Applications of Fujian Province,School of Computer and Information Engineering,Xiamen University of Technology,Xiamen 361024,China
关键词:
IPv6 云雾资源 统一调度算法 健康监护 系统响应
Keywords:
IPv6 cloud & fog resources unified scheduling algorithm healthcare monitoring response delay
分类号:
TP 391
DOI:
10.6043/j.issn.0438-0479.201810016
文献标志码:
A
摘要:
针对新一代健康监护系统在综合运用传感器网络、云计算和大数据等技术时存在的移动性、网络延迟、慢性疾病预测的准确率局限性问题,设计了一种基于云雾辅助的移动健康监护系统.通过分析基于云雾辅助的移动健康监护系统的层次化结构,解决了该系统的3项关键技术,包括:采用基于IPv6的网络体系结构以增加移动性,采用基于时间阈值的云雾资源统一调度算法以降低响应延迟,采用基于级联特征降维和特征选择的慢性病变风险分级模型以提高预测的智能性和准确率.对比实验表明,该系统具有更低的响应延迟和更高的慢性病变风险分级准确率,从而有利于心血管病类慢性疾病的早期预警.
Abstract:
The new generation healthcare monitoring system integrates sensor networks,cloud computing and bigdata technologies,and there are still limitations in mobility,network delay,and the accuracy of chronic disease prediction.In response to the above situation,a cloud & fog-assisted mobile health monitoring system was designed.First of all,the hierarchy structure of cloud & fog-assisted mobile health monitoring system is analyzed.Then,some key technologies of the system are solved as follows:designing IPv6-based network architecture to enhance mobility; adopting unified scheduling algorithm of cloud & fog resources based on time threshold to reduce response delay; relying on risk classification model of chronic diseases based on a cascade feature dimensionality reduction and feature selection to improve the intelligence and accuracy of chronic lesions prediction.Finally,the comparative experiments demonstrate that the system has lower response delay and higher accuracy of chronic disease risk classification,which is conducive to early warning of chronic cardiovascular disease.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-10-18录用日期:2019-01-20
基金项目:国家自然科学基金(61872436); 福建省自然科学基金(2019J01856); 赛尔网络下一代互联网创新项目(NGI20160708)
Email:jqhucn@xmut.edu.cn
更新日期/Last Update: 1900-01-01