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

Simple, accurate, portable, and selective gas sen-sors with autonomous, remote, and real-time access have become a requisite in various fields of applications. In this article, we report the development of a stand-alone and selective gas sensorsystem 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 toward 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 learningtool based on a deep neural network. The applied model displaysan average accuracy of 96.43% with a fast prediction speed of310 mu s, allowing a real-time recognition capability. The wirelessconnectivity is established utilizing a low-power microcontrollerboard and a Bluetooth module. The real-time data is made avail-able for the users over an Android-based mobile application anda webpage while utilizing cloud services through the Internet.The implemented system is successfully experimented with andvalidated under different test conditions that verify the wholeplatform. Further, the sensor system can be potentially appliedto a remote application without needing any manual involve-ment. The demonstrated work with an Internet of Things (IoT)paradigm strengthens the next-generation gas sensing technologyfor developing smart, selective, and real-time gas sensor systems.