Phone-based Ambient Temperature Measurement with a New Confidence-based Truth Inference Model

Abstract

Monitoring the indoor temperature can help saving of energy and improve the comfort level. Smartphone, as a ubiquitous device, can be an additional data source to provide the ambient temperature estimation. However, the estimation results sometimes can be unreliable due to the different phone using states. How to integrate multiple estimation results in one area to get a more accurate prediction result is still a challenge. In this work, we proposed one phone-based ambient temperature measurement system which contains two models. The first temperature prediction model takes easily accessible phone state features as inputs and outputs ambient temperature prediction with a confidence value. The second truth inference model takes multiple prediction results with confidences as inputs and outputs a referred final prediction result. Our temperature prediction model reaches 0.253°C with MAE in our testing set. We also proved by transfer learning our model can be used in other new type of phones. We evaluate the truth inference model in our testing dataset and it reaches 0.128°C, which outperforms the state-of-the-art truth inference algorithms. We believe this work can contribute to energy conservation and provide new ideas for crowdsourcing.