Integration of Internet of Things for Haemodialysis Water Quality Monitoring Systems

Abstract

ABSTRACT Patient safety in haemodialysis depends on maintaining good water quality, but conventional monitoring techniques are still manual and reactive, which raises the possibility of contamination. This study presents an Internet of Things (IoT) based real-time water quality monitoring system that integrates Total Dissolved Solids (TDS), temperature, pressure, flow, and vibration sensors with microcontrollers, microprocessors, and cloud analytics. The system enables continuous monitoring, early anomaly detection, and predictive maintenance, significantly enhancing water treatment reliability. Experimental deployment in haemodialysis units demonstrated notable improvements in monitoring efficiency. The system utilizes Total Dissolved Solids (TDS), temperature, pressure, flow, and vibration sensors, coupled with cloud-based data processing and machine learning techniques. Results indicate that the IoT system enhances anomaly detection by 35% to 55% efficiency and predictive maintenance accuracy by 45% to 65% compared to the manual method. By combining machine learning-based predictive analytics with real-time IoT sensors, this study offers a scalable, automated, and economical approach to managing water quality during haemodialysis. According to the results, IoT-driven automation improves operational effectiveness, patient safety, and regulatory compliance. This opens the door for broader implementation in vital healthcare water monitoring systems