IoT Based Real-Time Water Quality Monitoring and Visualization System Using an Autonomous Surface Vehicle

Autonomous Surface Vessels (ASVs) are useful tools for monitoring and management of waterbodies to increase data capture rates and quantities within shorter time frames and at lower costs than manned methods. SeaTrac Systems Inc.’s SP- 4S ASV is an autonomous boat designed to provide a platform to collect water quality data on a long term (i.e., months) basis. Solar panels provide continuous power supply to the vessel and the instruments within. Autonomous steering and path tracking capability of the ASV allows users to predetermine a data collection path using Geographic Positioning System (GPS) waypoints. SP-48 is designed to collect water quality parameters that include Chlorophyll a (Chl-a), Phycocyanin (PC), Phycoerythrin (PE), Colored Dissolved Organic Matter (CDOM), Dissolved Oxygen, Temperature, Turbidity, Salinity, pH, Partial Pressure of Carbon Dioxide (pCO
2
), and Backscattering. To provide collection of these parameters, six water quality sensors (SeaBird Scientific Inc.’s ECO-Triplet-FL3-B [Chl-a, PC, PE], ECO-Triplet-BB2FL [CDOM, Turbidity], ECO-Triplet-BB3 [Backscattering], SBE 63 [Dissolved Oxygen], ProOceanus Inc.’s CO
2
ProCV [pCO
2
], and AML Oceanographic Inc.’s CT Xchange [pH, Salinity, Temperature]) were integrated into the ASV. Additionally, the ASV has integrated instruments that capture GPS, wind, and meteorological data. The GPS is captured by an Airmar GH2183 Network GPS Compass and the wind and meteorological data is captured by an Airmar 200WX WeatherStation. To receive the data from the water quality sensors, two options were considered. One possibility was to use sensor specific software to capture and store the data on the ASV onboard computer. This would require all software to run every time the ASV is deployed. In addition, the output files would not be accessible for real-time processing, preventing real-time data visualization and monitoring. The second option was to create a single interface to obtain data from all the sensors and send it to a server on a real-time basis. Ultimately, a connection tool (named Sensors Bridge) was developed to transmit water quality, GPS, and meteorology data by capturing information from communication (COM) ports and a LAN port onboard the ASV. The captured data was sent to a server located at Mississippi State University via a cellular network for storage and visualization. A Node.js server was created to provide the gateway to the database and the publicly available web application. The Node.js server processes the raw data, converts it to its final form, and saves it to the database. The data was stored in a PostgreSQL/ PostGIS relational spatial database. The web application (web app) named Water Quality Monitor was developed to visualize data in real-time as well as query historical data. The app contains four major components (Dashboard, Charts, Maps, and Add Location) that are accessible through a tabs interface in the app. The Dashboard component displays the last 30 records captured for each water quality parameter as a line graph depicting parameter magnitude as a function of time. Additionally, it displays the current location of the vessel and recently recorded points. There is also a bar graph showing all the parameters with the number of data points stored in the database, which can help monitor the quantity of records stored in the database for each parameter. The Chart tab assists in querying and visualization of historical data as a line or bar graph. Data can also be downloaded in image and spreadsheet formats. The Map tab provides the option of visualizing the water quality data spatially as a raster, vector, and heatmap. Users can download the data as a spatial file format. The Add Location tab enables system administrators to add a new study area. Once the boundary, name, and code of a study area are specified, database tables are automatically created. When the ASV starts capturing data from a study location, the data is saved to its respective locational database tables. Complete implementation of real-time data capture and visualization streamlines water quality monitoring. Additionally, captured data can be used for time series analysis. The current implementation lays a foundation for a decision support system.