Thermal Profiling by WiFi Sensing in IoT Networks

Extensive literature has shown the possibility of using WiFi to sense large scale environmental features such as people, movement, and human gestures. To our best knowledge, there has been no investigation on identifying the microscopic changes in a channel due to atmospheric temperature variations. We identify this as a real world use case, since there are scenarios such as Data Centres where WiFi traffic is omnipresent and temperature monitoring is important. We develop a framework for sensing temperature using WiFi Channel State Information (CSI), proposing that the increased kinetic energy of ambient gas particles will affect the wireless link. To validate this, our paper uses low wavelength 5GHz WiFi CSI from commodity hardware to measure how the channel changes as the ambient temperature is raised. Empirically, we demonstrate that the CSI amplitude value drops at a rate of 13 per degree Celsius rise in the ambient temperature based on the testing platform, and developed regressions models with ± 1°C accuracy in the majority of cases. Moreover, we have shown that WiFi subcarriers exhibit a frequency-selective behaviour in their varying responses to the rise in ambient temperature.