Multiagent federated reinforcement learning for resource allocation in UAV-enabled internet of medical things networks

AM Seid, A Erbad, HN Abishu… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
In the 5G/B5G network paradigms, intelligent medical devices known as the Internet of
Medical Things (IoMT) have been used in the healthcare industry to monitor remote users' …

An exploratory bibliometric analysis of the literature on the age of information-aware unmanned aerial vehicles aided communication

UA Bukar, MS Sayeed, SFA Razak, S Yogarayan… - Informatica, 2023 - informatica.si
Real-time status updates require more frequent updates with fresh information. This study
investigates the applications and research potential of unmanned aerial vehicles (UAV) for …

Joint dual-UAV trajectory and RIS design for ARIS-assisted aerial computing in IoT

B Duo, M He, Q Wu, Z Zhang - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
Reconfigurable intelligent surface (RIS), as an emerging technology, has recently been
applied to expand the range of mobile-edge computing (MEC) networks and improve …

Integration of 6G signal processing, communication, and computing based on information timeliness-aware digital twin

H Liao, J Lu, Y Shu, Z Zhou, M Tariq… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
6G has emerged as a feasible solution to enable intelligent electric vehicle (EV) energy
management. It can be further combined with digital twin (DT) to optimize resource …

Timeliness of Information in 5G Non-Terrestrial Networks: A Survey

QT Ngo, Z Tang, B Jayawickrama, Y He… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
This paper explores the significance of the timeliness of information in the context of fifth
generation (5G) non-terrestrial networks (NTN). As 5G technology continues to evolve, its …

Asynchronous Federated Learning for Resource Allocation in Software Defined Internet of UAVs

KI Qureshi, L Wang, X **ong… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
The use of unmanned aerial vehicles (UAVs) as flying base stations to support various tasks,
such as data collection, machine learning (ML) model training, and wireless communication …

Federated Deep Reinforcement Learning for Joint AeBSs Deployment and Computation Offloading in Aerial Edge Computing Network

L Liu, Y Zhao, F Qi, F Zhou, W **e, H He, H Zheng - Electronics, 2022 - mdpi.com
In the 6G aerial network, all aerial communication nodes have computing and storage
functions and can perform real-time wireless signal processing and resource management …

Multi-agent federated reinforcement learning for resource allocation in UAV-enabled internet of medical things networks

M Abegaz, A Erbad, H Nahom, A Albaseer… - Authorea …, 2023 - techrxiv.org
In the 5G/B5G network paradigms, intelligent medical devices known as the Internet of
Medical Things (IoMT) have been used in the healthcare industry to monitor remote users' …

Enhancing Data Freshness in Air-Ground Collaborative Heterogeneous Networks through Contract Theory and Generative Diffusion-Based Mobile Edge Computing

Z Sun, G Chen - Sensors, 2023 - mdpi.com
Mobile edge computing is critical for improving the user experience of latency-sensitive and
freshness-based applications. This paper provides insights into the potential of non …

Age–energy‐aware trajectory planning for UAV‐assisted data collection in Internet of Things

H Chen, Z Jia, N Ma, Y Liu, Y Yao, X Qin - IET Communications, 2023 - Wiley Online Library
Unmanned aerial vehicles (UAVs) are employed as mobile relay nodes to enable timely
remote monitoring by collecting information from monitoring devices and transferring the …