Smart and Selective Gas Sensor System Empowered With Machine Learning Over IoT Platform

Simple, accurate, portable, and selective gas sensors with autonomous, remote, and real-time access have become a requisite in various fields of applications. In this paper, we report the development of a stand-alone and selective gas sensor system incorporating a single resistive sensor with wireless monitoring and internet connectivity. The sensor is fabricated in-house with platinum decorated tin-oxide hollow-spheres as the sensing material, which exhibits a prominent response towards the tested volatile organic compounds (VOCs) at different concentrations. The intelligence in terms of accurate identification of VOCs and their concentration is attained by employing a machine learning tool based on deep neural network. The applied model displays an average accuracy of 96.43% with a fast prediction speed of 310ls, allowing a real-time recognition capability. The wireless connectivity is established utilizing a low-power microcontroller board and a Bluetooth module. The real-time data is made available for the users over an Android-based mobile application and a webpage while utilizing cloud services through the internet. The implemented system is successfully experimented with and validated under different test conditions that verify the whole platform. Further, the sensor system can be potentially applied to a remote application without needing any manual involvement. The demonstrated work with an internet-of-things (IoT) paradigm strengthens the next-generation gas sensing technology for developing smart, selective, and real-time gas sensor systems.