Rebollo, M.;                     Rincon, J.A.;                     Hernández, L.;                     Enguix, F.;                     Carrascosa, C.    
        Extending the Framework for Developing Intelligent Virtual Environments (FIVE) with Artifacts for Modeling Internet of Things Devices and a New Decentralized Federated Learning Based on Consensus for Dynamic Networks. Sensors 2024, 24, 1342.
    https://doi.org/10.3390/s24041342
    AMA Style
    
                                Rebollo M,                                 Rincon JA,                                 Hernández L,                                 Enguix F,                                 Carrascosa C.        
                Extending the Framework for Developing Intelligent Virtual Environments (FIVE) with Artifacts for Modeling Internet of Things Devices and a New Decentralized Federated Learning Based on Consensus for Dynamic Networks. Sensors. 2024; 24(4):1342.
        https://doi.org/10.3390/s24041342
    
    Chicago/Turabian Style
    
                                Rebollo, Miguel,                                 Jaime Andrés Rincon,                                 LuÃs Hernández,                                 Francisco Enguix,                                 and Carlos Carrascosa.        
                2024. "Extending the Framework for Developing Intelligent Virtual Environments (FIVE) with Artifacts for Modeling Internet of Things Devices and a New Decentralized Federated Learning Based on Consensus for Dynamic Networks" Sensors 24, no. 4: 1342.
        https://doi.org/10.3390/s24041342
    
    APA Style
    
                                Rebollo, M.,                                 Rincon, J. A.,                                 Hernández, L.,                                 Enguix, F.,                                 & Carrascosa, C.        
        
        (2024). Extending the Framework for Developing Intelligent Virtual Environments (FIVE) with Artifacts for Modeling Internet of Things Devices and a New Decentralized Federated Learning Based on Consensus for Dynamic Networks. Sensors, 24(4), 1342.
        https://doi.org/10.3390/s24041342