Design of IOT Based Self Powered Smart WQMS using Filter Optimization with Efficient Sensor Node

Growing population, industries, and global warming are degrading water quality, producing various diseases and health issues. The existing wireless sensor network (WSN) and internet of things (IOT) based systems to monitor water quality are not extensively explored and are deficient in filter optimization (FO) and efficient sensor node. In this research, a self-powered system (SPS) is proposed to monitor contamination of water in real-time. The proposed method comprises smart sensors array measuring potential of hydrogen (pH), electrical conductivity (EC), pressure, flow rate, water recovery ratio (WRR), and Total Dissolved Solid (TDS) before and after filtration through the membrane. The measured physical parameters are also collected and displayed through Arduino Pro Mini microcontroller at both ends. The data accumulated at both ends are transmitted to a master node and then to an open source cloud server named ADDA Fruit IO through ESP32 WSN modules for remote monitoring. Twelve highly saline random water samples are collected periodically to analyze the proposed system performance. The above physical parameters are analyzed using statistical methods such as multiple regression, analysis of variance (ANOVA), and correlation.