Open Access iconOpen Access

ARTICLE

crossmark

Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks

Ibrahim Abunadi1, Maha M. Althobaiti2, Fahd N. Al-Wesabi3,4, Anwer Mustafa Hilal5, Mohammad Medani6, Manar Ahmed Hamza5,*, Mohammed Rizwanullah5, Abu Serwar Zamani5

1 Department of Information Systems, Prince Sultan University, Rafha Street, Riyadh, 11586, Saudi Arabia
2 Department of Computer Science, College of Computing and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Muhayel Aseer, 62529, Saudi Arabia
4 Faculty of Computer and IT, Sana'a University, Sana'a, 61101, Yemen
5 Department of Computer and Self Development, Prince Sattam bin Abdulaziz University, Al-Kharj, 16278, Saudi Arabia
6 Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Muhayel Aseer, 62529, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email: email

Computers, Materials & Continua 2022, 72(1), 1195-1212. https://doi.org/10.32604/cmc.2022.025473

Abstract

The evolving “Industry 4.0” domain encompasses a collection of future industrial developments with cyber-physical systems (CPS), Internet of things (IoT), big data, cloud computing, etc. Besides, the industrial Internet of things (IIoT) directs data from systems for monitoring and controlling the physical world to the data processing system. A major novelty of the IIoT is the unmanned aerial vehicles (UAVs), which are treated as an efficient remote sensing technique to gather data from large regions. UAVs are commonly employed in the industrial sector to solve several issues and help decision making. But the strict regulations leading to data privacy possibly hinder data sharing across autonomous UAVs. Federated learning (FL) becomes a recent advancement of machine learning (ML) which aims to protect user data. In this aspect, this study designs federated learning with blockchain assisted image classification model for clustered UAV networks (FLBIC-CUAV) on IIoT environment. The proposed FLBIC-CUAV technique involves three major processes namely clustering, blockchain enabled secure communication and FL based image classification. For UAV cluster construction process, beetle swarm optimization (BSO) algorithm with three input parameters is designed to cluster the UAVs for effective communication. In addition, blockchain enabled secure data transmission process take place to transmit the data from UAVs to cloud servers. Finally, the cloud server uses an FL with Residual Network model to carry out the image classification process. A wide range of simulation analyses takes place for ensuring the betterment of the FLBIC-CUAV approach. The experimental outcomes portrayed the betterment of the FLBIC-CUAV approach over the recent state of art methods.

Keywords


Cite This Article

APA Style
Abunadi, I., Althobaiti, M.M., Al-Wesabi, F.N., Hilal, A.M., Medani, M. et al. (2022). Federated learning with blockchain assisted image classification for clustered UAV networks. Computers, Materials & Continua, 72(1), 1195-1212. https://doi.org/10.32604/cmc.2022.025473
Vancouver Style
Abunadi I, Althobaiti MM, Al-Wesabi FN, Hilal AM, Medani M, Hamza MA, et al. Federated learning with blockchain assisted image classification for clustered UAV networks. Comput Mater Contin. 2022;72(1):1195-1212 https://doi.org/10.32604/cmc.2022.025473
IEEE Style
I. Abunadi et al., "Federated Learning with Blockchain Assisted Image Classification for Clustered UAV Networks," Comput. Mater. Contin., vol. 72, no. 1, pp. 1195-1212. 2022. https://doi.org/10.32604/cmc.2022.025473



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1865

    View

  • 1102

    Download

  • 0

    Like

Share Link