Next-Gen Smart Healthcare Using UAV Assisted Cooperative Communication: A Deep Learning Approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F25%3A43910241" target="_blank" >RIV/60076658:12310/25:43910241 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758788&utm_source=clarivate&getft_integrator=clarivate&tag=1" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758788&utm_source=clarivate&getft_integrator=clarivate&tag=1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TCE.2024.3502913" target="_blank" >10.1109/TCE.2024.3502913</a>
Alternative languages
Result language
angličtina
Original language name
Next-Gen Smart Healthcare Using UAV Assisted Cooperative Communication: A Deep Learning Approach
Original language description
Unmanned aerial vehicles (UAVs) are evolving rapidly and are revolutionizing the operation of several applications. An important application that can be significantly improved with UAV-assisted communication is healthcare. Wearable devices can be worn on the body and are equipped with sensors that can detect and monitor various physiological parameters. The data collected by these devices can be used to track changes in health status over time, identify potential health risks, and inform clinical decision-making. UAV-assisted non-terrestrial wireless communication networks can play a significant role in the reliable transfer of this data. The novelty of the work is that it considers multi-antenna UAVs for communicating patient data collected from various wearable devices. It also proposes a hybrid relaying scheme incorporating deep learning (DL) for improved performance. The UAVs have pre-trained deep neural networks (DNNs) to process patient data and make accurate decisions without forwarding it to the healthcare facility. A two-layer deep neural network has been used at the UAV with a training accuracy of 99.6%, validation accuracy of 96.6%, and test accuracy of 96.62%. The UAV relays the data to the healthcare facility for expert opinion if it cannot make decisions with the desired accuracy. In this scenario, the optimum UAV and transmit antenna are selected, and a hybrid relaying scheme is used to minimize the network outage and maximize the throughput. The communication model is built, and extensive simulations are performed using patient data to demonstrate the approach. The work will significantly impact the use of UAVs for smart healthcare.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
ISSN
0098-3063
e-ISSN
1558-4127
Volume of the periodical
71
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
8
Pages from-to
419-426
UT code for WoS article
001511069500042
EID of the result in the Scopus database
2-s2.0-85210076122