Smart IoT-Driven Domestic Water Quality Monitoring Systems: Architecture, Machine Learning Integration, and Implementation Challenges
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Access to safe drinking water affects 2.2 billion people globally, yet conventional laboratory-based water quality testing is slow and often fails to detect swift contamination. Internet of Things (IoT) technology, which is a network of smart sensors, cloud platforms, and advanced analytics, offers real-time monitoring at household and community scales. This systematic review examines IoT water quality monitoring technologies, architectures, and practical deployment challenges, with emphasis on domestic applications in resource-constrained environments. Three significant trends emerge: machine learning for predictive contamination detection, edge computing for low-connectivity resilience, and affordable sensor assemblies for community implementation. However, there are critical barriers like sensor drift, cybersecurity vulnerabilities, infrastructure gaps, and affordability constraints, to its general adoption. Using Nigeria (where 68-70% of water is contaminated) as a case study, this review synthesizes evidence on IoT effectiveness and provides evidence-based recommendations for designing systems that improve water safety and public health in developing regions.