Authors: Nawale Atharva Bharat, Professor Bangar Abhishek Popat, Professor Bhosale. Sachin Bajirao, Professor Karad Vandana
Abstract: Waterborne diseases remain a public-health concern where drinking-water sources may be contaminated by pathogenic microorganisms, sewage, agricultural runoff, or inadequate sanitation. Conventional laboratory testing provides important reference evidence, but sampling, transport, and analysis can delay the identification of unsafe water. Internet of Things (IoT) devices can support more frequent observation of water-quality indicators, while machine learning (ML), cloud or edge computing, and community health reporting can help convert observations into timely alerts. This paper presents a narrative review of IoT-enabled water-quality monitoring and waterborne-disease early-warning approaches. It examines common sensing parameters—including pH, temperature, turbidity, electrical conductivity and total dissolved solids—along with data transmission, anomaly detection, predictive analytics, and public-health notification. The review compares representative approaches, identifies gaps in field validation, sensor calibration, pathogen-specific testing, data quality, and alert governance, and proposes a conceptual architecture that combines sensor stations, laboratory confirmation, risk analytics, and community health surveillance. The proposed architecture is a review-derived design, not an experimentally validated system.
