Avaliação de uma Arquitetura Híbrida Borda-Nuvem para Monitoramento de Motores Elétricos em Tempo Real
Resumo
Propomos uma arquitetura híbrida Borda–Nuvem para detecção em tempo real de falhas em motores elétricos industriais. A análise local fornece alertas com latência mediana de 16 ms, enquanto agregações periódicas são enviadas à nuvem. Os resultados mostram que a borda supera amplamente a nuvem em estabilidade e latência, com uso eficiente de CPU e memória mesmo sob múltiplos sensores.Referências
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Duc, T. L., Nguyen, C., and Östberg, P.-O. (2025). Workload prediction for proactive resource allocation in large-scale cloud-edge applications. Electronics, 14(16):3333.
Gettys, J. and Nichols, K. (2012). Bufferbloat: dark buffers in the internet. Communications of the ACM, 55(1):57–65.
Kreps, J. (2014). Questioning the lambda architecture. Online article, July, 205:18–34.
Lee, H. and Cha, J. H. (2016). New stochastic models for preventive maintenance and maintenance optimization. European Journal of Operational Research, 255(1):80–90.
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE signal processing magazine, 37(3):50–60.
Pandey, R., Uziel, S., Hutschenreuther, T., and Krug, S. (2023). Towards deploying dnn models on edge for predictive maintenance applications. Electronics, 12(3):639.
Pillai, P., Kaushik, A., Bhavikatti, S., Roy, A., and Kumar, V. (2016). A hybrid approach for fusing physics and data for failure prediction. International Journal of Prognostics and Health Management, 7(4).
Saini, N., Yadav, A. L., and Rahman, A. (2024). Cloud based predictive maintenance system. In ICRITO, pages 1–5. IEEE.
Sanchez-Iborra, R., Zoubir, A., Hamdouchi, A., Idri, A., and Skarmeta, A. (2023). Intelligent and efficient iot through the cooperation of tinyml and edge computing. Informatica, 34(1):147–168.
Sepe, M., Graziano, A., Badora, M., Di Stazio, A., Bellani, L., Compare, M., Zio, E., et al. (2021). A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets. J. of the Global Power and Propulsion Society, 2021.
Shi, W., Cao, J., Zhang, Q., Li, Y., and Xu, L. (2016). Edge computing: Vision and challenges. IEEE internet of things journal, 3(5):637–646.
Warren, J. and Marz, N. (2015). Big Data: Principles and best practices of scalable realtime data systems. Simon and Schuster.
Andriulo, F. C., Fiore, M., Mongiello, M., Traversa, E., and Zizzo, V. (2024). Edge computing and cloud computing for internet of things: A review. In Informatics.
Duc, T. L., Nguyen, C., and Östberg, P.-O. (2025). Workload prediction for proactive resource allocation in large-scale cloud-edge applications. Electronics, 14(16):3333.
Gettys, J. and Nichols, K. (2012). Bufferbloat: dark buffers in the internet. Communications of the ACM, 55(1):57–65.
Kreps, J. (2014). Questioning the lambda architecture. Online article, July, 205:18–34.
Lee, H. and Cha, J. H. (2016). New stochastic models for preventive maintenance and maintenance optimization. European Journal of Operational Research, 255(1):80–90.
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE signal processing magazine, 37(3):50–60.
Pandey, R., Uziel, S., Hutschenreuther, T., and Krug, S. (2023). Towards deploying dnn models on edge for predictive maintenance applications. Electronics, 12(3):639.
Pillai, P., Kaushik, A., Bhavikatti, S., Roy, A., and Kumar, V. (2016). A hybrid approach for fusing physics and data for failure prediction. International Journal of Prognostics and Health Management, 7(4).
Saini, N., Yadav, A. L., and Rahman, A. (2024). Cloud based predictive maintenance system. In ICRITO, pages 1–5. IEEE.
Sanchez-Iborra, R., Zoubir, A., Hamdouchi, A., Idri, A., and Skarmeta, A. (2023). Intelligent and efficient iot through the cooperation of tinyml and edge computing. Informatica, 34(1):147–168.
Sepe, M., Graziano, A., Badora, M., Di Stazio, A., Bellani, L., Compare, M., Zio, E., et al. (2021). A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets. J. of the Global Power and Propulsion Society, 2021.
Shi, W., Cao, J., Zhang, Q., Li, Y., and Xu, L. (2016). Edge computing: Vision and challenges. IEEE internet of things journal, 3(5):637–646.
Warren, J. and Marz, N. (2015). Big Data: Principles and best practices of scalable realtime data systems. Simon and Schuster.
Publicado
08/12/2025
Como Citar
SIQUEIRA, Guilherme; KREUTZ, Diego.
Avaliação de uma Arquitetura Híbrida Borda-Nuvem para Monitoramento de Motores Elétricos em Tempo Real. In: ESCOLA REGIONAL DE REDES DE COMPUTADORES (ERRC), 22. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 14-20.
DOI: https://doi.org/10.5753/errc.2025.17834.
