Deep Learning Empowered Service Selection in FANETs: A 5G-Enabled UAV-Cloud Architecture
Flying Ad-Hoc Networks (FANETs), commonly referred to as drones or Unmanned Aerial Vehicles (UAVs), are increasingly transforming various industries by supporting numerous applications. In this context, a UAV can function as a cloud service provider, offering services such as memory, computing, and...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10945331/ |
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| author | Mohamed Ben Bezziane Messaoud Abbas Hessa Alfraihi Bouziane Brik Yacine Khaldi Oussama Aiadi Hasan Siham |
| author_facet | Mohamed Ben Bezziane Messaoud Abbas Hessa Alfraihi Bouziane Brik Yacine Khaldi Oussama Aiadi Hasan Siham |
| author_sort | Mohamed Ben Bezziane |
| collection | DOAJ |
| description | Flying Ad-Hoc Networks (FANETs), commonly referred to as drones or Unmanned Aerial Vehicles (UAVs), are increasingly transforming various industries by supporting numerous applications. In this context, a UAV can function as a cloud service provider, offering services such as memory, computing, and network connectivity to consumer UAVs. This work introduces an innovative 5G-enabled UAV-Cloud Architecture empowered by deep learning for service selection. The architecture integrates two main modules: (i) A game theory-based module, which establishes an interactive game between provider and consumer UAVs, generating datasets to guide consumer UAVs in selecting suitable providers; and (ii) A deep learning-based module, leveraging the generated datasets to predict optimal service providers. Numerical simulations validate the proposed architecture, highlighting its superior prediction accuracy, reduced time and message complexity, and minimized service discovery and consumption delays compared to existing methods. These advancements are attributed to the synergy of game theory, deep learning, and 5G communication capabilities. |
| format | Article |
| id | doaj-art-4da6ab5ed0ab45dd81ef244258df3dd2 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4da6ab5ed0ab45dd81ef244258df3dd22025-08-20T03:18:26ZengIEEEIEEE Access2169-35362025-01-0113638776389410.1109/ACCESS.2025.355587110945331Deep Learning Empowered Service Selection in FANETs: A 5G-Enabled UAV-Cloud ArchitectureMohamed Ben Bezziane0https://orcid.org/0009-0007-6632-6045Messaoud Abbas1https://orcid.org/0000-0002-7998-9020Hessa Alfraihi2https://orcid.org/0000-0001-8169-3766Bouziane Brik3https://orcid.org/0000-0002-3267-5702Yacine Khaldi4https://orcid.org/0000-0002-8004-7698Oussama Aiadi5https://orcid.org/0000-0002-4102-1735Hasan Siham6Artificial Intelligence and Information Technologies Laboratory (LINATI), Université Kasdi Merbah Ouargla, Ouargla, AlgeriaLIAP Laboratory, University of El Oued, El Oued, AlgeriaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaComputer Science Department, College of Computing and Informatics, Sharjah University, Sharjah, United Arab EmiratesMathematics Department, Ouargla Higher Normal School, Ouargla, AlgeriaArtificial Intelligence and Information Technologies Laboratory (LINATI), Université Kasdi Merbah Ouargla, Ouargla, AlgeriaFaculty of Technology, De Montfort University, Gateway House, Leicester, U.K.Flying Ad-Hoc Networks (FANETs), commonly referred to as drones or Unmanned Aerial Vehicles (UAVs), are increasingly transforming various industries by supporting numerous applications. In this context, a UAV can function as a cloud service provider, offering services such as memory, computing, and network connectivity to consumer UAVs. This work introduces an innovative 5G-enabled UAV-Cloud Architecture empowered by deep learning for service selection. The architecture integrates two main modules: (i) A game theory-based module, which establishes an interactive game between provider and consumer UAVs, generating datasets to guide consumer UAVs in selecting suitable providers; and (ii) A deep learning-based module, leveraging the generated datasets to predict optimal service providers. Numerical simulations validate the proposed architecture, highlighting its superior prediction accuracy, reduced time and message complexity, and minimized service discovery and consumption delays compared to existing methods. These advancements are attributed to the synergy of game theory, deep learning, and 5G communication capabilities.https://ieeexplore.ieee.org/document/10945331/UAV-cloud architectureservice selectiondeep learninggame theory and 5G |
| spellingShingle | Mohamed Ben Bezziane Messaoud Abbas Hessa Alfraihi Bouziane Brik Yacine Khaldi Oussama Aiadi Hasan Siham Deep Learning Empowered Service Selection in FANETs: A 5G-Enabled UAV-Cloud Architecture IEEE Access UAV-cloud architecture service selection deep learning game theory and 5G |
| title | Deep Learning Empowered Service Selection in FANETs: A 5G-Enabled UAV-Cloud Architecture |
| title_full | Deep Learning Empowered Service Selection in FANETs: A 5G-Enabled UAV-Cloud Architecture |
| title_fullStr | Deep Learning Empowered Service Selection in FANETs: A 5G-Enabled UAV-Cloud Architecture |
| title_full_unstemmed | Deep Learning Empowered Service Selection in FANETs: A 5G-Enabled UAV-Cloud Architecture |
| title_short | Deep Learning Empowered Service Selection in FANETs: A 5G-Enabled UAV-Cloud Architecture |
| title_sort | deep learning empowered service selection in fanets a 5g enabled uav cloud architecture |
| topic | UAV-cloud architecture service selection deep learning game theory and 5G |
| url | https://ieeexplore.ieee.org/document/10945331/ |
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