Connected Intelligence for Smart Water Quality Monitoring System in IIoT

Abstract

Unclean water for drinking is unavailable to many people. By enabling automatic water quality measurement, this problem can be mitigated. In recent years, edge computing has gained popularity for remote monitoring. In order to track the water quality in rivers and household tanks in response to this trend, we developed an edge computing support system. For the application, the wireless module and sensor module are both required. The sensor connects wirelessly to a wireless module in order to collect and send data there. Data is transferred to the cloud using the wireless protocol once it reaches the wireless node. The conductivity, turbidity, oxygen, pH, and temperature are among the sensors included in the sensor module. Databases are used to store the data that the sensors have collected. A mobile AI-powered interactive app is developed to evaluate the water quality instantaneously based on sensor measurements. Retrieving sensor data from a distance and monitoring sensor performance are made simpler by the app's architecture. The simulation demonstrates how a straightforward AI model set up in an edge device helps with accurate water quality prediction.