A Phone-Based Distributed Ambient Temperature Measurement System With an Efficient Label-Free Automated Training Strategy

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on mobile computing 2024-12, Vol.23 (12), p.11781-11793
Main Authors: Chen, Dayin, Shi, Xiaodan, Zhang, Haoran, Song, Xuan, Zhang, Dongxiao, Chen, Yuntian, Yan, Jinyue
Format: Magazinearticle
Language:English
Subjects:
Crowdsourcing
Data models
Estimation
label-free
MAML
phone
Task analysis
Temperature distribution
Temperature measurement
temperature measuring
Uncertainty
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 methods. However, existing phone-based methods face challenges such as insufficient privacy protection, difficulty in adapting models to various phones, and hurdles in obtaining enough labeled training data. In this study, we propose a distributed phone-based ambient temperature estimation system which enables collaboration among multiple phones to accurately measure the ambient temperature in different areas of an indoor space. This system also provides an efficient, cost-effective approach with a few-shot meta-learning module and an automated label generation module. It shows that with just 5 new training data points, the temperature estimation model can adapt to a new phone and reach a good performance. Moreover, the system uses crowdsourcing to generate accurate labels for all newly collected training data, significantly reducing costs. Additionally, we highlight the potential of incorporating federated learning into our system to enhance privacy protection. We believe this study can advance the practical application of phone-based ambient temperature measurement, facilitating energy-saving efforts in buildings.
ISSN:1536-1233
1558-0660
1558-0660
DOI:10.1109/TMC.2024.3399843