AI-Driven WSN for Precise Aquatic Pollution Detection Using an Intelligent Monitoring Approach

The proliferation of digital devices, sensors, and interconnected systems has led to an explosion of data. Simple sensors like pH, conductivity, and Turbidity sensors can be used for the classification of gasoline and diesel in water. These sensors are easy to set up and deploy, so they can be installed in vast numbers and can get real-time data from the site. FPGAs are very fast in processing complex data and have low latency time compared to other traditional microcontrollers. But FPGA accepts coding in Hardware Description Languages like Verilog or VHDL, which can be very complex to code complex models that are trained in other high-level languages like Python and C++. Simple classification models in machine learning are implemented using High-Level Synthesis tools, which accept codes written in languages like C, C++, or SystemC, and translate them into hardware-implementable RTL (Register- Transfer Level) code. The data from two major components of oil, gasoline, and petrol are used to train various classification models with widely used libraries in Python. The trained parameters are extracted from the trained model. The parameters are then assembled and then coded in C++ as currently most of the tools support C++. Some modifications need to be made to the original code to make it compatible with the synthesis tool.