A Real-Time Drinking Water Quality Monitoring Model based on IoT Cloud Platform
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Water quality safety is increasingly threatened by industrial pollution and resource scarcity, and traditional monitoring approaches are often inefficient and slow to respond, leading to delayed contamination alerts. In this paper, we propose a real-time drinking water quality monitoring system based on an IoT cloud platform. The proposed technology adopts a multi-layer architecture integrating low-power communication, edge computing, and Docker containerization while employing machine learning algorithms such as Random Forest and Gradient Boosting for feature extraction, data cleaning, and predictive modeling. Experimental results indicate that the RF model achieves an accuracy of 96.0% with only 1.7 seconds of latency, effectively reducing undetected contamination risks while providing broad coverage at reduced costs. Future work will explore unknown contaminant detection, blockchain-based data security, and solar-powered operation for sustainable deployment.
Abstract
Water quality safety is increasingly threatened by industrial pollution and resource scarcity, and traditional monitoring approaches are often inefficient and slow to respond, leading to delayed contamination alerts. In this paper, we propose a real-time drinking water quality monitoring system based on an IoT cloud platform. The proposed technology adopts a multi-layer architecture integrating low-power communication, edge computing, and Docker containerization while employing machine learning algorithms such as Random Forest and Gradient Boosting for feature extraction, data cleaning, and predictive modeling. Experimental results indicate that the RF model achieves an accuracy of 96.0% with only 1.7 seconds of latency, effectively reducing undetected contamination risks while providing broad coverage at reduced costs. Future work will explore unknown contaminant detection, blockchain-based data security, and solar-powered operation for sustainable deployment.