Phone-based ambient temperature sensing using opportunistic crowdsensing and machine learning

Abstract Due to the ubiquitous nature of smartphones, opportunistic phone-based crowdsensing has emerged as an important sensing modality. Since fine-grain ambient temperature measurements are a pre-requisite for energy-efficient operation of heating and cooling (HVAC) systems in buildings, in this paper, we use mobile phone sensing in conjunction with a web-based crowdsensing system to obtain detailed ambient temperature estimates inside buildings. We present a machine learning approach based on a random forest ensemble learning model that uses the phone battery temperature sensor to infer the ambient air temperature. We also present a few-shot transfer learning method to quickly learn and deploy our model onto new phones with modest training overheads. Our crowdsensing web service enables predictions made by multiple phones to be aggregated in an opportunistic fashion, extending our approach from an individual level to a community level. We evaluate our ML-based model for a range of devices, operating scenarios, and ambient temperatures, and see mean errors of less than ± 0.5 ° F for our temperature predictions. More generally, our results show the feasibility of using an on-device ML model for ambient temperature predictions in mobile phones. This allows buildings - new and old, with and without sensing systems - to benefit from a new class of ubiquitous temperature sensors, enabling more sustainable operation.