FLEX-FHIR: A Modular Fog Computing Architecture for Community Health Monitoring and Decision Support Using AWS
Resumo
Centralized cloud architectures face inherent limitations when addressing region-specific healthcare challenges in smart cities. This paper presents FLEX-FHIR (Fog Layer EXtensible Framework for Health using Interoperable Resources), a modular and scalable fog computing architecture for community-centered health monitoring and population-level decision support. The system is structured as a recursive hierarchical fog tree in which each node runs on Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances hosting containerized HAPI FHIR servers, PostgreSQL databases, and artificial intelligence/statistical modules. Regional health insights are generated locally through health clinical scoring, K-Means clustering, trimodal correlation analysis (Pearson, Spearman, Kendall), and spatial autocorrelation (Moran’s I), all operating exclusively over FHIR resources. Experimental evaluation across workloads of up to 20,000 patients showed zero FHIR compliance failures, inter-tier forwarding latency of 83–134 ms per patient, independent of dataset size, and analytically correct behavior in all tested scenarios. The architecture is directly compatible with Brazil’s Rede Nacional de Dados em Saúde (RNDS) and the International Patient Summary (IPS) initiative.
Referências
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Garcés-Jiménez, A., Polo-Luque, M.-L., Gómez-Pulido, J. A., Rodríguez-Puyol, D., and Gómez-Pulido, J. M. (2024). Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis. Computers in Biology and Medicine, 174:108469.
Islam, U., Alatawi, M. N., Alqazzaz, A., et al. (2025). A hybrid fog-edge computing architecture for real-time health monitoring in iomt systems with optimized latency and threat resilience. Scientific Reports, 15(1):25655.
Isnan, S., Abdullah, A. F., Shariff, A. R. M., Ishak, I., Ismail, S. N. S., and Appanan, M. R. (2025). Moran’s I and Geary’s C: investigation of the effects of spatial weight matrices for assessing the distribution of infectious diseases. Geospatial Health, 20(1):1277.
Janjua, J. I., Ghazal, T. M., Abushiba, W., and Abbas, S. (2024). Optimizing patient outcomes with ai and predictive analytics in healthcare. In 2024 IEEE 65th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), pages 1–6. IEEE.
Jeyaraman, N. et al. (2024). Applications of fog computing in healthcare. Cureus, 16(7):e64263.
Joint Initiative Council (2021). International Patient Summary Standard – ISO 27269:2021. Technical report, International Organization for Standardization. Implemented via HL7 FHIR; see [link].
Lekadir, K., Quaglio, G., Gallas, S., et al. (2025). Future-ai: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. The BMJ, 388:e080214.
Malibari, A. A. (2023). An efficient iot-artificial intelligence-based disease prediction using lightweight cnn in healthcare system. Measurement: Sensors, 26:100695.
Ministério da Saúde do Brasil (2025a). Rede Nacional de Dados em Saúde (RNDS). [link]. Accessed: 2025.
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Prerna, Singh, P., and Singh, D. P. (2023). A delay sensitive framework for effective healthcare using machine learning. In 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), pages 541–545.
Ramirez Lopez, L. J., Jaimes Salazar, N. E., and Barbosa Posada, J. E. (2026). Pire: Interoperable platform for electronic records. Computers, 15(3):162.
Royal College of Physicians (2017). National Early Warning Score (NEWS) 2.
Seth, M., Jalo, H., Högstedt, Å., Medin, O., Sjöqvist, B. A., and Candefjord, S. (2025). Technologies for interoperable internet of medical things platforms to manage medical emergencies in home and prehospital care: Scoping review. Journal of Medical Internet Research, 27:e54470.
Wang, R., Tu, J., and Chu, C. (2023). Research on the application of cross-regional sharing of blockchain-based electronic health records. In 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), pages 1–6.
Yang, A. C., Ma, W.-M., Chiang, D.-H., Liao, Y.-Z., Lai, H.-Y., Lin, S.-C., Liu, M.-C., Wen, K.-T., Lin, T.-H., Tsai, W.-X., et al. (2025). Early prediction of sepsis using an xgboost model with single time-point non-invasive vital signs and its correlation with c-reactive protein and procalcitonin: A multi-center study. Intelligence-Based Medicine, 11:100242.
Ahmed, W., Iqbal, W., Hassan, A., Ahmad, A., Ullah, F., and Srivastava, G. (2025). Elevating e-health excellence with iota distributed ledger technology: Sustaining data integrity in next-gen fog-driven systems. Future Generation Computer Systems, 168:107755.
André Setti Cassel, G., da Rosa Righi, R., André da Costa, C., Rosecler Bez, M., and Pasin, M. (2024). Towards providing a priority-based vital sign offloading in healthcare with serverless computing and a fog-cloud architecture. Future Generation Computer Systems, 157:51–66.
Ayub, N. (2023). Human vital sign dataset. [link]. Accessed: 2026-05-19.
Azizah, N., Faisal, M. R., Abadi, F., Budiman, I., Mazdadi, M. I., Herteno, R., and Nugrahadi, D. T. (2023). A vital sign monitoring system exploiting bt/ble on low-cost commercial smartwatch for home care patients. In 2023 International Seminar on Intelligent Technology and Its Applications (ISITIA), pages 405–410.
Chebaane, A., Arshad, M. K., Burger, F., and Khelil, A. (2024). A layered strategy for reducing offloading latency in fog computing. In 2024 9th International Conference on Fog and Mobile Edge Computing (FMEC), pages 176–182.
Chen, W.-H., Chang, Y.-H., Hsiao, C.-T., Hsueh, P.-R., and Shih, H.-M. (2025). Utilizing artificial intelligence and cellular population data for timely identification of bacteremia in hospitalized patients. International Journal of Medical Informatics, 195:105788.
da Rosa Righi, R., Rodrigues, V. F., da Costa, C. A., and Eskofier, B. (2025). Breaking down the data path in digital health: From edge to fog and beyond. IEEE Pervasive Computing.
Garcés-Jiménez, A., Polo-Luque, M.-L., Gómez-Pulido, J. A., Rodríguez-Puyol, D., and Gómez-Pulido, J. M. (2024). Predictive health monitoring: Leveraging artificial intelligence for early detection of infectious diseases in nursing home residents through discontinuous vital signs analysis. Computers in Biology and Medicine, 174:108469.
Islam, U., Alatawi, M. N., Alqazzaz, A., et al. (2025). A hybrid fog-edge computing architecture for real-time health monitoring in iomt systems with optimized latency and threat resilience. Scientific Reports, 15(1):25655.
Isnan, S., Abdullah, A. F., Shariff, A. R. M., Ishak, I., Ismail, S. N. S., and Appanan, M. R. (2025). Moran’s I and Geary’s C: investigation of the effects of spatial weight matrices for assessing the distribution of infectious diseases. Geospatial Health, 20(1):1277.
Janjua, J. I., Ghazal, T. M., Abushiba, W., and Abbas, S. (2024). Optimizing patient outcomes with ai and predictive analytics in healthcare. In 2024 IEEE 65th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), pages 1–6. IEEE.
Jeyaraman, N. et al. (2024). Applications of fog computing in healthcare. Cureus, 16(7):e64263.
Joint Initiative Council (2021). International Patient Summary Standard – ISO 27269:2021. Technical report, International Organization for Standardization. Implemented via HL7 FHIR; see [link].
Lekadir, K., Quaglio, G., Gallas, S., et al. (2025). Future-ai: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. The BMJ, 388:e080214.
Malibari, A. A. (2023). An efficient iot-artificial intelligence-based disease prediction using lightweight cnn in healthcare system. Measurement: Sensors, 26:100695.
Ministério da Saúde do Brasil (2025a). Rede Nacional de Dados em Saúde (RNDS). [link]. Accessed: 2025.
Ministério da Saúde do Brasil (2025b). Sala de Apoio à Gestão Estratégica (SAGE). [link]. Accessed: 2025.
Prerna, Singh, P., and Singh, D. P. (2023). A delay sensitive framework for effective healthcare using machine learning. In 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), pages 541–545.
Ramirez Lopez, L. J., Jaimes Salazar, N. E., and Barbosa Posada, J. E. (2026). Pire: Interoperable platform for electronic records. Computers, 15(3):162.
Royal College of Physicians (2017). National Early Warning Score (NEWS) 2.
Seth, M., Jalo, H., Högstedt, Å., Medin, O., Sjöqvist, B. A., and Candefjord, S. (2025). Technologies for interoperable internet of medical things platforms to manage medical emergencies in home and prehospital care: Scoping review. Journal of Medical Internet Research, 27:e54470.
Wang, R., Tu, J., and Chu, C. (2023). Research on the application of cross-regional sharing of blockchain-based electronic health records. In 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), pages 1–6.
Yang, A. C., Ma, W.-M., Chiang, D.-H., Liao, Y.-Z., Lai, H.-Y., Lin, S.-C., Liu, M.-C., Wen, K.-T., Lin, T.-H., Tsai, W.-X., et al. (2025). Early prediction of sepsis using an xgboost model with single time-point non-invasive vital signs and its correlation with c-reactive protein and procalcitonin: A multi-center study. Intelligence-Based Medicine, 11:100242.
Publicado
19/07/2026
Como Citar
SILVA, Fernanda Schäfer Tesch da; RIGHI, Rodrigo da Rosa.
FLEX-FHIR: A Modular Fog Computing Architecture for Community Health Monitoring and Decision Support Using AWS. In: SIMPÓSIO DE INFRAESTRUTURA DIGITAL/NUVEM PARA PESQUISA (PESQUISA@NUVEM), 1. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 48-58.
DOI: https://doi.org/10.5753/pesquisanuvem.2026.23300.
