Middleware de Inferência de Contexto baseado em Aprendizado Federado para IoT no Computing Continuum
Resumo
O crescimento da Internet das Coisas (IoT) amplia a coleta de dados sobre os ambientes, impulsionando sistemas cientes de contexto que dependem de inferência para extrair informações relevantes. Embora o aprendizado de máquina seja promissor nessa tarefa, há desafios relacionados ao uso de recursos e privacidade. Paradigmas como aprendizado federado e computação em névoa permitem descentralizar processamento e dados, mas sua integração é complexa, motivando o uso de middlewares. Então, este trabalho propõe o Micelio, um middleware IoT ciente de contexto com foco em inferência via aprendizado federado, com suporte à computação em nuvem, névoa e borda. É apresentado um estudo de caso em classificação de lixo para cidades inteligentes, avaliado num ambiente simulação, e demonstrando o uso eficiente de recursos pelo middleware, e acurácia superior à linha de base.Referências
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Mahieu, C., Ongenae, F., De Backere, F., Bonte, P., De Turck, F., and Simoens, P. (2019). Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things. Journal of Systems and Software, 149:138 – 157.
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Michalakis, K., Christodoulou, Y., Caridakis, G., Voutos, Y., and Mylonas, P. (2021). A context-aware middleware for context modeling and reasoning: A case-study in smart cultural spaces. Applied Sciences, 11(13).
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Saha, R., Misra, S., and Deb, P. K. (2020). Fogfl: Fog-assisted federated learning for resource-constrained iot devices. IEEE Internet of Things Journal, 8(10):8456–8463.
Shelby, Z., Hartke, K., and Bormann, C. (2014). Rfc 7252: The constrained application protocol (coap).
Symeonaki, E., Arvanitis, K., and Piromalis, D. (2020). A context-aware middleware cloud approach for integrating precision farming facilities into the iot toward agriculture 4.0. Applied Sciences (Switzerland), 10(3).
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Zhang, H., Huang, T., Liu, Y., Zhu, S., Gui, G., and Chi, Y. (2017). Senz: A context awareness middleware system used in mobile devices. In 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), pages 1–7. IEEE.
Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A., Hamdi, M., and Guizani, M. (2022). Pervasive ai for iot applications: A survey on resource-efficient distributed artificial intelligence. IEEE Communications Surveys & Tutorials, 24(4):2366–2418.
Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Fernandez-Marques, J., Gao, Y., Sani, L., Kwing, H. L., Parcollet, T., Gusmão, P. P. d., and Lane, N. D. (2020). Flower: A friendly federated learning research framework. arXiv preprint arXiv:2007.14390.
Bormann, C. and Hoffman, P. (2020). Rfc 8949 concise binary object representation (cbor). Internet Engineering Task Force (IETF).
Elkhodr, M., Khan, S., and Gide, E. (2024). A novel semantic iot middleware for secure data management: Blockchain and ai-driven context awareness. Future Internet, 16(1).
Fu, L., Zhang, H., Gao, G., Zhang, M., and Liu, X. (2023). Client selection in federated learning: Principles, challenges, and opportunities. IEEE Internet of Things Journal.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
Hou, W., Wen, H., Zhang, N., Lei, W., Lin, H., Han, Z., and Liu, Q. (2024). Adaptive training and aggregation for federated learning in multi-tier computing networks. IEEE Transactions on Mobile Computing, 23(5):4376 – 4388.
Mahieu, C., Ongenae, F., De Backere, F., Bonte, P., De Turck, F., and Simoens, P. (2019). Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things. Journal of Systems and Software, 149:138 – 157.
Medina, A., Lakhina, A., Matta, I., and Byers, J. (2001). Brite: An approach to universal topology generation. In MASCOTS 2001, Proceedings Ninth International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, pages 346–353. IEEE.
Michalakis, K., Christodoulou, Y., Caridakis, G., Voutos, Y., and Mylonas, P. (2021). A context-aware middleware for context modeling and reasoning: A case-study in smart cultural spaces. Applied Sciences, 11(13).
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32.
Perera, C., Zaslavsky, A., Christen, P., and Georgakopoulos, D. (2013). Context aware computing for the internet of things: A survey. IEEE communications surveys & tutorials, 16(1):414–454.
Razzaque, M. A., Milojevic-Jevric, M., Palade, A., and Clarke, S. (2015). Middleware for internet of things: a survey. IEEE Internet of things journal, 3(1):70–95.
Saha, R., Misra, S., and Deb, P. K. (2020). Fogfl: Fog-assisted federated learning for resource-constrained iot devices. IEEE Internet of Things Journal, 8(10):8456–8463.
Shelby, Z., Hartke, K., and Bormann, C. (2014). Rfc 7252: The constrained application protocol (coap).
Symeonaki, E., Arvanitis, K., and Piromalis, D. (2020). A context-aware middleware cloud approach for integrating precision farming facilities into the iot toward agriculture 4.0. Applied Sciences (Switzerland), 10(3).
Tripathy, S. S., Bebortta, S., Chowdhary, C. L., Mukherjee, T., Kim, S., Shafi, J., and Ijaz, M. F. (2024). Fedhealthfog: A federated learning-enabled approach towards healthcare analytics over fog computing platform. Heliyon, 10(5).
Yang, M. and Thung, G. (2016). Classification of trash for recyclability status. CS229 project report, 2016(1):3.
Yokochi, M. and Thalhath, N. (2023). Evaluating oxigraph server as a triple store for small and medium-sized datasets. BioHackrXiv DOI: 10.37044/osf.io/yru4b, 29.
Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., and Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, 98:289–330.
Zhang, H., Huang, T., Liu, Y., Zhu, S., Gui, G., and Chi, Y. (2017). Senz: A context awareness middleware system used in mobile devices. In 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), pages 1–7. IEEE.
Publicado
25/05/2026
Como Citar
SANTANA, Henrique de S.; SILVA, Thais R. M. B.; SILVA, Fabrício A.; AYLON, Linnyer B. R..
Middleware de Inferência de Contexto baseado em Aprendizado Federado para IoT no Computing Continuum. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 10. , 2026, Praia do Forte/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 141-154.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2026.24130.
