From cloud and fog computing to federated-fog computing: A comparative analysis of computational resources in real-time IoT applications based on semantic interoperability

Huaranga-Junco, Edgar; González-Gerpe, Salvador; Castillo-Cara, Manuel; Cimmino, Andrea; García-Castro, Raúl

doi:10.1016/j.future.2024.05.001

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2024

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Cited by 5 publications

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ALBLA: an adaptive load balancing approach in edge-cloud networks utilizing learning automata

Ghorbani,

Khaledian,

Moazzami

2024

Computing

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ALBLA: an adaptive load balancing approach in edge-cloud networks utilizing learning automata

Ghorbani,

Khaledian,

Moazzami

2024

Computing

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Fog computing-enabled adaptive prognosis of cutting tool remaining life through multi-source data

Liang,

Wang,

et al. 2024

Journal of Computational Design and Engineering

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Cutting tool remaining life during machining processes is essential for achieving sustainable production. Traditional prognosis methods are often crippled by the inability to adapt to diverse working conditions across the machining process lifecycle. This paper introduces a fog computing-enabled adaptive prognosis framework utilising multi-source data to address these challenges effectively. The key innovations include: (1) The proposed system integrates power and vibration data collected from LGMazak VTC-16A and IRON MAN QM200 machines. A standardised data fusion method combines multi-source data to enhance robustness and accuracy. (2) The transformer model is employed to improve prognosis accuracy of cutting tool remaining life, best accuracy of 98.24% and an average accuracy of 97.63% are achieved. (3) Finite Element Analysis (FEA) is incorporated to validate the model's predictions to validate reliability of deep learning model. (4) The fog computing optimisation mechanism based on the bees algorithm, which shows fitness value of 0.92 and convergence within 15 iterations. The proposed method reduces total data volume in cloud by 54.12%, prediction time by 33.64% and time complexity in the cloud layer by 4.62%. The effectiveness of fog computing in enhancing the operational efficiency and reliability of manufacturing systems is validated through advanced data integration and deep learning techniques.

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Dynamic Load Balancing for Enhanced Network Performance in IoT-Enabled Smart Healthcare With Fog Computing

Ala’anzy,

Zhanuzak,

Akhmedov

et al. 2024

IEEE Access

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From cloud and fog computing to federated-fog computing: A comparative analysis of computational resources in real-time IoT applications based on semantic interoperability

Cited by 5 publications

References 24 publications

ALBLA: an adaptive load balancing approach in edge-cloud networks utilizing learning automata

ALBLA: an adaptive load balancing approach in edge-cloud networks utilizing learning automata

Fog computing-enabled adaptive prognosis of cutting tool remaining life through multi-source data

Dynamic Load Balancing for Enhanced Network Performance in IoT-Enabled Smart Healthcare With Fog Computing

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