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QoE-based optimal resource allocation in wireless healthcare networks: opportunities and challenges

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Abstract

Ubiquitous health monitoring is a mobile health service with the aim of monitoring patients’ conditions anytime and anywhere by collecting and transferring biosignal data from patients to health-service providers (e.g., healthcare centers). As a critical issue in ubiquitous health monitoring, wireless resource allocation can influence the performance of health monitoring, and the majority of work in wireless resource allocation for health monitoring has focused on quality-of-service oriented allocation schemes with primary challenges at the physical and MAC layers. Recently, quality-of-experience (QoE) oriented resource allocation schemes in wireless health monitoring have attracted attentions as a promising design to a better service of healthcare monitoring. In this paper, we overview the metrics of assessing the quality of medical images, and discuss the performance of these metrics in QoE-oriented resource allocation for health monitoring. We start with addressing the state-of-the-art QoE metrics by providing a taxonomy of the different metrics employed in assessing medical images. We then present the design of resource allocation schemes for health monitoring. After that, we present a case study to compare the performance of different classes of metrics in designing resource allocation schemes. We end the paper with a few open issues in the design of novel QoE metrics for resource allocation in health monitoring.

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Notes

  1. Equations (7l) and (7m) hold when the wireless network is with a MIMO-OFDM architecture, and we assume that both WLAN and WAN networks for health service employ this architecture.

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Acknowledgments

This work was partially supported by National Natural Science Foundation of China (No. 61370202) and partially supported by a grant from the National High Technology Research and Development Program of China (863 Program, No. 2012AA02A614).

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Authors and Affiliations

  1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Di Lin

  2. Department of Electrical and Computer Engineering, McGill University, Montreal, Canada

    Fabrice Labeau

  3. Department of Computer and Telecommunications Engineering, University of Western Macedonia, Kozani, Greece

    Athanasios V. Vasilakos

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  1. Di Lin
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  3. Athanasios V. Vasilakos
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Correspondence to Di Lin.

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Lin, D., Labeau, F. & Vasilakos, A.V. QoE-based optimal resource allocation in wireless healthcare networks: opportunities and challenges. Wireless Netw 21, 2483–2500 (2015). https://doi.org/10.1007/s11276-015-0927-y

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