Next-Gen Smart Healthcare Using UAV Assisted Cooperative Communication: A Deep Learning Approach

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Next-Gen Smart Healthcare Using UAV Assisted Cooperative Communication: A Deep Learning Approach

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Next-Gen Smart Healthcare Using UAV Assisted Cooperative Communication: A Deep Learning Approach

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS

ISSN

0098-3063

e-ISSN

1558-4127

Svazek periodika

71

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

419-426

Kód UT WoS článku

001511069500042

EID výsledku v databázi Scopus

2-s2.0-85210076122