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Emergency-level-based healthcare information offloading over fog network

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Abstract

Recently, the healthcare technologies continue to develop rapidly, especially various wearable Internet of Things (IoT) devices for body network have been invented one after another. The relevant products can already be easily purchased in the market such as the smart bracelet, smart blood pressure monitor and so on. These healthcare devices not only make users able to understand their own body information more in more detail but also provide a communication way to the hospital. It means that patients can obtain the professional medical prescription advice without going to the hospital in person because the health information can transmit to the medical cloud through any network interfaces. Additionally, both medical records of patients and prescription advice from doctors are stored in the cloud. In order to provide the better service quality, the use of fog in the network edge can quickly response the requests from the patients. The computing power of the fog node is less than the cloud. Therefore, balancing the trade-off between cloud and fog is very important. In this paper, we formulate an optimization problem about offloading then use the metaheuristic to find out the best policy. Moreover, we also design an emergency supporting measure. Simulation results show that the proposed methods can provide a more efficient healthcare service.

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Acknowledgements

This research was partly funded by the National Science Council of the R.O.C. under grants MOST 107-2221-E-197-005-MY3.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China

    Cong Zhang

  2. Department of Computer Science and Information Engineering, National Ilan University, Yilan, Taiwan

    Hsin-Hung Cho & Chi-Yuan Chen

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  1. Cong Zhang
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Correspondence to Hsin-Hung Cho.

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Zhang, C., Cho, HH. & Chen, CY. Emergency-level-based healthcare information offloading over fog network. Peer-to-Peer Netw. Appl. 13, 16–26 (2020). https://doi.org/10.1007/s12083-018-0715-4

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