A Structured Review of IoT-Based Embedded Systems and Machine Learning for Water Quality Monitoring

This paper presents the results of a structured scoping review (SSR) that explores the integration of the Internet of Things (IoT) and embedded systems in creating a sustainable and interconnected technological ecosystem. The study focuses on water quality monitoring, an area where these technologies have demonstrated significant potential. The SSR follows a meticulous methodology, covering planning, execution, and documentation stages to ensure a comprehensive and unbiased review of the existing literature. Key research questions guide the review, focusing on extracting and analyzing water sample characteristics, using machine learning algorithms for classification, and the technologies utilized in these systems. The search process involved multiple databases, yielding 343 articles, of which 8 met the stringent inclusion and exclusion criteria. The review highlights the widespread use of IoT for real-time data collection and artificial intelligence (AI) for analyzing complex patterns in water quality data. Our findings underscore the significance of temperature, pH, turbidity, and conductivity, commonly utilized in water classification. In addition, prevalent machine learning techniques for analyzing water quality data include K-Nearest Neighbors (KNN) and artificial neural networks (ANN). Despite the advances, challenges such as implementation costs, connectivity in remote areas, and the interpretability of AI models remain. This review underscores the transformative potential of IoT and AI in water quality monitoring, with implications for ensuring safe drinking water and sustainable water resource management.