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A Framework for Fog-assisted Healthcare Monitoring

Jianqiang Hu^1,2, Wei Liang², Zhiyong Zeng³, Yong Xie 1,2^1,2 and Jianxun Eileen Yang⁴

1. School of Computer and Information Engineering, Xiamen University of Technology
   361024 Xiamen, P.R. China
   hujianqiang@tsinghua.org.cn
2. Key Laboratory of Internet-of-Things Applications of Fujian Province, Xiamen University of Technology
   361024 Xiamen, P.R. China
   {wliang,yongxie}@xmut.edu.cn
3. College of Mathematics and Informatics, Fujian Normal University
   350007 Fuzhou, P.R. China
   zzyong@fjnu.edu.cn
4. Shenzhen Research Institute of Sun Yat-Sen University
   Shenzhen 518057, P.R. China
   eileenyjx@163.com

Abstract

In order to tackle some challenges in ubiquitous healthcare monitoring such as mobility, scalability, and network latency, a framework for Fog-assisted healthcare monitoring is proposed in this paper. This framework is composite of body-sensing layer, Fog layer (Fog-assisted gateway), Cloud layer (health Cloud). And then, this paper makes an intensive study in some key technologies of the proposed framework such as an IPv6-based network architecture, intelligent warning model based on subband energy feature, security framework of HL7 RIM-based data exchange, health risk assessment based on fusion of grey model and Markov model. Finally, results of experiment depict that the proposed intelligent warning model can make immediate distinction abnormal signals. Moreover, the proposed health assessment model confirms its effectiveness with respect to 245 patients in Xiamen District of Jimei.

Key words

Healthcare monitoring, Fog computing, IPv6, HL7 RIM, Health risk assessment

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS180930025H

Publication information

Volume 16, Issue 3 (October 2019)
Recent Advances in Information Processing and Security
Year of Publication: 2019
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Hu, J., Liang, W., Zeng, Z., 1,2, Y. X., Yang, J. E.: A Framework for Fog-assisted Healthcare Monitoring. Computer Science and Information Systems, Vol. 16, No. 3, 753–772. (2019), https://doi.org/10.2298/CSIS180930025H