FDRL Approach for Association and Resource Allocation in Multi-UAV Air-To-Ground IoMT Network

Authors

Abegaz Mohammed, Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
Aiman Erbad, Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
Hayla Nahom, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
Abdullatif Albaseer, Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarFollow
Mohammed Abdallah, Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarFollow
Mohsen Guizani, Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

TechRxiv

Abstract

In 6G networks, unmanned aerial vehicles (UAVs) can serve as aerial flying base stations (AFBS) with aerial mobile edge computing (AMEC) server capabilities. AFBS is an increasingly popular solution for delivering time-sensitive applications, extending network coverage, and assisting ground base stations in the healthcare systems for remote areas with limited infrastructure. Furthermore, the UAVs are deployed in the healthcare system to support the Internet of medical things (IoMT) devices in data collection, medical equipment distribution, and providing smart services. However, ensuring the privacy and security of patients’ data with the limited UAV resources is a major challenge. In this paper, we present a federated deep reinforcement learning framework for resource allocation in UAV-enabled healthcare systems, where IoMT devices send their trained model parameters without transmitting sensitive raw data to the AMEC server. In the proposed framework, the IoMT device is associated with AFBS based on the quality of the data and its demand in order to maximize learning efficiency and accuracy. This work aims to minimize the computation costs of the IoMT devices while considering UAV resources and the fairness of UAV coverage. Simulation results prove that our proposed algorithm outperforms other baseline algorithms in learning accuracy and computational cost. © 2022, CC BY.

DOI

10.36227/techrxiv.20523120

Publication Date

8-21-2022

Keywords

Computation cost, Federated learning, IoMT, Privacy preservation, Resource allocation, Antennas, Base stations, Deep learning, Health care, Mobile edge computing, Reinforcement learning, Unmanned aerial vehicles (UAV)

Comments

Preprint: Techrxiv

Archived with thanks to Techrxiv

Preprint License: CC by 4.0

Uploaded October 17, 2022

Recommended Citation

A. Mohammed, A. Erbad, H. Nahom, A. Albaseer, M. Abdallah, and M. Guizani, "FDRL Approach for Association and Resource Allocation in Multi-UAV Air-To-Ground IoMT Network", 2022, doi: 10.36227/techrxiv.20523120