Performance Analysis and QoS Modeling of an IoT-Based Real-Time Patient Monitoring System Using Heart Rate and GPS Data

This paper presents the design, implementation, and experimental performance evaluation of an IoT-based real-time patient monitoring system using heart rate and GPS data. The proposed system integrates a wearable pulse sensor and GPS module with a Wi-Fi-enabled microcontroller to continuously transmit physiological and location data to a cloud-based monitoring platform. Real-world experiments were conducted under varying network traffic conditions to evaluate key Quality of Service (QoS) parameters, including throughput, end-to-end delay, and packet loss. The experimental results show that the system performs reliably under low to moderate traffic loads, achieving stable throughput with average delay below acceptable real-time thresholds and negligible packet loss. However, as network traffic increases, delay rises significantly and packet loss becomes more pronounced, particularly when buffer capacity is limited. Comparative testing with different buffer configurations demonstrates that larger buffers improve data reliability by reducing packet loss, but at the cost of increased latency. Furthermore, the system successfully delivers real-time heart rate and location data with high accuracy, demonstrating its applicability for remote healthcare monitoring. The results validate that maintaining operation within a controlled traffic region is essential to ensure optimal QoS. This study provides practical insights into the deployment of IoT healthcare systems, emphasizing the importance of balancing latency, reliability, and network resource constraints in real-world environments.