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Cited 59 time in webofscience Cited 88 time in scopus
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UAV-enabled healthcare architecture: Issues and challenges

Authors
Ullah, SanaKim, Ki-IlKim, Kyong HoonImran, MuhammadKhan, PervezTovar, EduardoAli, Farman
Issue Date
Aug-2019
Publisher
ELSEVIER
Keywords
Unmanned Aerial Vehicles; Body Area Network; IoV; Smart healthcare; Industry
Citation
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, v.97, pp 425 - 432
Pages
8
Indexed
SCIE
SCOPUS
Journal Title
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Volume
97
Start Page
425
End Page
432
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/73030
DOI
10.1016/j.future.2019.01.028
ISSN
0167-739X
1872-7115
Abstract
Unmanned Aerial Vehicles (UAVs) have great potential to revolutionize the future of automotive, energy, and healthcare sectors by working as wireless relays to improve connectivity with ground networks. They are able to collect and process real-time information by connecting existing network infrastructures including Internet of Medical Things (e.g., Body Area Networks (BANs)) and Internet of Vehicles with clouds or remote servers. In this article, we advocate and promote the notion of employing UAVs as data collectors. To demonstrate practicality of the idea, we propose a UAV-based architecture to communicate with BANs in a reliable and power-efficient manner. The proposed architecture adopts the concept of wakeup-radio based communication between a UAV and multiple BANs. We analyze the performance of the proposed protocol in terms of throughput and delay by allocating different priorities to the hubs or gateways. The proposed architecture may be useful in remote or disaster areas, where BANs have poor or no access to conventional wireless communication infrastructure, and may even assist vehicular networks by monitoring driver's physiological conditions through BANs. We further highlight open research issues and challenges that are important for developing efficient protocols for UAV-based data collection in smart healthcare systems. (C) 2019 Elsevier B.V. All rights reserved.
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