IoT based Water Quality Monitoring System

Water quality monitoring is a global challenge due to rapid urbanization and industrialization. Conventional laboratory testing is time-intensive and geographically restricted. This paper presents an IoT-enabled autonomous RC platform for real-time water quality monitoring. Equipped with pH, turbidity, and temperature sensors, the system performs dynamic sampling across diverse aquatic environments. Unlike previous works, this study introduces an error-aware calibration framework considering least count, absolute, and relative error, thereby enhancing measurement credibility. A dataset was collected from five distinct sites—college pond, local river, urban drainage outlet, agricultural canal, and industrial discharge. Results show significant differences across sites, with acidic contamination and high turbidity detected in urban and industrial regions. A Water Quality Index (WQI) was computed for each location, supported by error bars in graphical analysis. Finally, a predictive analytics framework for future deployment is proposed. The proposed system demonstrates a scalable, low-cost, and accurate solution for environmental monitoring.