IoT-enabled data visualization and analysis for small-scale wastewater treatment

Abstract

This thesis proposes an IoT-enabled data visualization and analysis system for enhancing the educational experience in water quality monitoring. The system utilizes modern IoT technologies to collect, store, visualize, and analyze environmental data, with a focus on air quality metrics such as temperature and humidity. Through integration with platforms like ThingsBoard and Grafana, real-time monitoring, predictive analytics, and comprehensive data analysis are made possible. The thesis presents detailed descriptions of the system architecture, hardware components, software components, data collection and storage processes, data visualization and analysis results, and a comparison between existing and pro posed solutions. Future work is outlined, suggesting for further enhancement and adaptation of the system for water quality monitoring. Overall, the thesis utilizes IoT technologies in environmental monitoring, with the potential to significantly improve data accuracy, real-time monitoring capabilities, and prepare students for industry practices.