Design and development of in situ FPGA-based water quality monitoring kit

Abstract

In 2017, about 144 million people collected water from untreated water bodies, such as lakes, streams, and rivers. One of the major causes of death is consuming contaminated or polluted water. Measuring and monitoring water quality are usually done using two methods. The conventional method occurs by taking samples of water and then transferring them to the laboratory. The second method is real-time water quality by integrating the Internet of Things (IoT). This method is preferable as it only requires smart sensors and processors to monitor the water quality. Among the widely used processors are the Arduino and Raspberry Pi. However, these two processors have a limitation, including a limited number of hard-coded input/output pins, unlike the Field Programmable Gate Array (FPGA) processor, which has many input/output pins not hard-coded to allow different interfacing of multiple sensors. Based on the literature, an FPGA platform provides more flexibility and reconfigurability features when compared with the Arduino and Raspberry Pi. This research mainly focuses on designing a reconfigurable multi-core Smart Water Quality System (SWQS) measuring the pH, Total Dissolved Solids (TDS), and turbidity parameters. The hardware design was developed based on the system-on-chip (SoC) design methodology on an FPGA to parallelize the SWQS functionality. A Liquid-Crystal Display (LCD) display has been incorporated into the Raspberry Pi to show real-time data. The Platform Designer on Quartus II has been used to instantiate four cores to integrate all functions into one processor. The Eclipse tool on Quartus II, on the other hand, was used to program the sensors using embedded C language. The proposed design has been implemented on DE10 Nano FPGA-SoC consuming 9% of logic resources and 57% of internal memory. To verify the proposed system functionality, the sensors were tested on different liquids. To test the pH level, the pH sensor was tested on pure water, lemon juice, and milk to show the acidity and alkalinity. The pH sensor showed 7, less nearly 2, and less than 8 for pure water, lemon juice, and milk, respectively. The TDS sensor successfully detected the salt added to the water, and the TDS values increased to approximately 1800 ppm. Finally, the turbidity sensor revealed the dust inserted in the solution. The more dust in the liquid, the more TDS value there was recorded. Additionally, results showed that the processing time of all the sensors using FPGA is approximately 300 ms for ten readings; on the other hand, the processing time of using other processors, such as Arduino, took 2 s for ten readings. This is because FPGA is functioning at 100 MHz, while Arduino’s frequency is not more than 24 MHz. All real-time sensor readings were shown on a Linux Terminal. In conclusion, the proposed FPGA-based system can be utilized as a heterogeneous multi-core system for many applications, including the SWQS.