IoT and MQTT-based cardiovascular parameter monitoring system for medical alerts

Abstract

This paper presents the development of a computing platform for the real-time monitoring of cardiovascular parameters derived from bioelectrical signals. A comprehensive analysis of primary users was conducted, leading to the identification of both technical and functional requirements. The interface design was guided by Sommerville’s methodology. The system architecture is based on a microservices model, incorporating a relational database and enabling integration with data transmitted from Internet of Things (IoT) devices. The platform was evaluated through incremental stress testing, starting with zero users and increasing in steps of 100 up to 5,000. A total of 22,132 requests were processed at a peak rate of 440.4 requests per second, with an average response time of 930 ms and 95% of responses occurring within 2,300 ms. The system demonstrated error-free performance with up to 1,700 concurrent users. At 5,000 users and 26,393 total requests, a minimal error rate of 0.16% was recorded, confirming the platform’s stability under high workloads. These findings validate the feasibility of the proposed solution for remote biomedical monitoring, offering an efficient, scalable, and robust tool for real-time health supervision.