A Smart IoT-Based System for Real-Time Water Quality Monitoring Using Edge Computing and Machine Learning

Abstract

The monitoring of water quality is essential in the provision of safe and sustainable water resources, especially in a scenario when the pollution of the environment and climate changes are developing. The conventional water monitoring systems where there is periodic sampling and centralized processing are not always effective to provide real-time and effective use of data in the decision-making process. The proposed research is the smart, Internet of Things (IoT)-based system, which continuously monitors the quality of water, where edge computing and machine learning are designed to enhance real-time analysis. The system includes IoT sensors that are installed on bodies of water to measure the parameters of pH, turbidity, temperature, and conductivity. The edge computing approach takes advantage of local data processing, which entails a considerable decrease in the latency and data transmission. Machine learning algorithm compares the processed data to predict the quality of water and identify the possible instances of contamination. The prototype of the system was deployed and tested in the urban and agricultural water bodies. It can be concluded that the system is successful at identifying the cases of contamination in real-time, and machine learning models demonstrate a high level of classification effectiveness. The edge computing integration has increased the efficiency and reliability of the system and hence it can be deployed to different environmental conditions. In this paper, the system design, data processing, machine learning integration, and experimental outcomes were analyzed in detail, and they prove the potential of the IoT, edge computing, and machine learning in managing the water quality.