A Phone-based Distributed Ambient Temperature Measurement System with An
  Efficient Label-free Automated Training Strategy
Dayin Chen Xiaodan Shi Haoran Zhang Xuan Song Dongxiao Zhang Yuntian Chen Jinyue Yan
Enhancing the energy efficiency of buildings significantly relies on
monitoring indoor ambient temperature. The potential limitations of
conventional temperature measurement techniques, together with the omnipresence
of smartphones, have redirected researchers' attention towards the exploration
of phone-based ambient temperature estimation technology. Nevertheless,
numerous obstacles remain to be addressed in order to achieve a practical
implementation of this technology. This study proposes a distributed
phone-based ambient temperature estimation system which enables collaboration
between multiple phones to accurately measure the ambient temperature in each
small area of an indoor space. Besides, it offers a secure, efficient, and
cost-effective training strategy to train a new estimation model for each newly
added phone, eliminating the need for manual collection of labeled data. This
innovative training strategy can yield a high-performing estimation model for a
new phone with just 5 data points, requiring only a few iterations. Meanwhile,
by crowdsourcing, our system automatically provides accurate inferred labels
for all newly collected data. We also highlight the potential of integrating
federated learning into our system to ensure privacy protection at the end of
this study. We believe this study has the potential to advance the practical
application of phone-based ambient temperature measurement, facilitating
energy-saving efforts in buildings.