Overview / Usage

Water quality maintenance is crucial in strategizing control and pitching in measures to reduce water pollution and degradation of aquatic ecosystems. Periodic monitoring of water quality is an essential exercise that can help to keep track of hydrological information of water bodies. This project presents a new system that can make water quality monitoring more efficient with the objective of preventing on-going pollution control trends has been proposed. Here, we propose a well-structured technique to collect the quality parameters, especially chemical indicators and generate characteristic maps for each of the parameters pertaining to that particular water body under survey. The data collected is transferred to the cloud in real-time to keep track of the quality of the water body under survey, and giving access to real-time quality data of different chemical and biological indicators like pH, dissolved oxygen, total dissolved solvents, turbidity, etc. The proposed system solves major limitations of existing methods, thereby making the water quality monitoring efficient and minimizing water pollution.

Methodology / Approach

Advancements in the fields like data analysis, USVs (Unmanned Surface Vessel) and IoT (Internet of Things) in the bygone era can be made use for monitoring the quality of water without relying on existing monitoring methods like manual water sampling and sensor node methodology using WSN. Here, we propose an engineering technique to track and monitor the various quality parameters of water (pH, dissolved oxygen, temperature, conductivity, etc.). For a, given water body, our proposed system provides a detailed report of the quality status of diverse geographical coordinates of the water body under surveillance.

Our system involves both hardware and software technology stack operating unison forming a typical embedded system to accomplish this objective. The technology comprising the embedded system is an unmanned surface vehicle (boat). In software, the back-end technology stack contains the data analytics and the front-end deals with a portal that presents all the quality information and results out of the back-end part to the user or pollution control board.

Technologies Used

IoT, Machine Learning, Data Analytics

Other links

• Version - 1 USV