An Intelligent Wireless Electronic Nose Node for Monitoring Gas Mixtures Using Neuro-Fuzzy Networks Implemented on a Microcontroller

This paper presents an intelligent wireless electronic nose node (WENN) that has been designed to classify and quantify binary gas mixtures, NH₃ and H₂S, the main malodors in various environments. The proposed WENN is based on embedded PC technology and neuro-fuzzy network algorithms. The hardware part of the designed system consists of a microcontroller for processing the measured data set obtained from a micro-gas sensor array, and a Zigbee-ready RF transceiver for transmitting the processed data to a base node. The main program embedded on the designed hardware performs real-lime classification and concentration estimation of the binary gas mixtures using the fuzzy ART and ARTMAP neural networks. To verify performance of the designed intelligent WENN, the measured data from the experiments for the binary gas mixtures have been executed using the WENN. The results show the reproducibility of the measured data and the verification of real-time classification and concentration estimation for the target gas.