Leveraging Fog and Cloud Computing for Continuous Health Monitoring and Data Processing: An Architecture for Outdoor Environments and Variable Connectivity

Juan Felipe Souza Oliveira; Paulo Cesar Salgado Vidal; Ronaldo Moreira Salles; Marcelo Quesado Filgueiras

doi:10.5753/jbcs.2026.5471

Authors

Juan Felipe Souza Oliveira Instituto Militar de Engenharia https://orcid.org/0000-0002-8380-4017
Paulo Cesar Salgado Vidal Instituto Militar de Engenharia https://orcid.org/0000-0002-9582-2263
Ronaldo Moreira Salles Instituto Politécnico do Porto https://orcid.org/0000-0002-3916-5086
Marcelo Quesado Filgueiras Universidade Federal de Juiz de Fora https://orcid.org/0000-0002-3862-7197

DOI:

https://doi.org/10.5753/jbcs.2026.5471

Keywords:

Fog Computing, Cloud Computing, E-Health

Abstract

A multilayer architecture was developed for real-time health data collection and processing, designed for outdoor environments with high population density and significant network interference. By integrating fog and cloud computing, the system addresses the growing demand for continuous health monitoring driven by the proliferation of Internet of Things (IoT) devices and Wireless Body Area Networks (WBANs) using smartbands. Traditional cloud-centric solutions often face challenges such as high latency and data integrity issues in unstable network conditions. The proposed architecture overcomes these limitations by employing fog computing for edge data preprocessing, reducing reliance on cloud connectivity and enhancing system responsiveness. The architecture was originally evaluated under diverse network conditions (3G, 4G, 5G) and in real-world scenarios such as football stadiums, metro systems, and urban beaches, demonstrating over 96% packet delivery success and significant latency reductions compared to cloud-only approaches. In this extended version, additional real-world scenarios are analyzed, including domestic flights, large-scale events in stadiums with over 60,000 attendees, and new evaluations along urban beachfronts. Furthermore, this version provides a more detailed explanation of key mechanisms, such as the use of the Transactional Outbox pattern to ensure data consistency in unstable networks and the integration of distributed processing techniques for real-time alert generation. These contributions offer deeper insights into the architecture’s scalability and reliability, confirming its effectiveness in maintaining data integrity and achieving low latency in connectivity-challenged environments, providing a solution for health monitoring.

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