A <italic>Q</italic>-Learning Approach for Real-Time NOMA Scheduling of Medical Data in UAV-Aided WBANs

A <italic>Q</italic>-Learning Approach for Real-Time NOMA Scheduling of Medical Data in UAV-Aided WBANs

Unmanned Aerial Vehicles (UAVs) have emerged as a flexible and cost-effective solution for remote monitoring of the vital signs of patients in large-scale Internet of Medical Things (IoMT) Wireless Body Area Networks (WBANs). This paper deals with the problem of using UAVs for real-time scheduling o...

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Bibliographic Details
Main Authors: Zeinab Askari, Jamshid Abouei, Muhammad Jaseemuddin, Alagan Anpalagan, Konstantinos N. Plataniotis
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
IoMT WBAN
UAV
latency
trajectory
NOMA
Q-learning
Online Access:https://ieeexplore.ieee.org/document/9933749/

Internet

https://ieeexplore.ieee.org/document/9933749/

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