A Distributed Smartphone-based System for Accurate Indoor Temperature Monitoring with Automated Data Labeling and Few-shot Learning

Alapfogalmak

A distributed smartphone-based system that enables accurate indoor temperature monitoring through collaborative crowdsourcing, automated data labeling, and few-shot learning techniques.

Kivonat

The proposed system consists of four key modules:

Ambient Temperature Estimation Model: Each smartphone can estimate the ambient temperature using a machine learning model that takes in various phone state features as input and outputs the temperature estimate along with an associated uncertainty value.

Crowdsourcing-based Truth Inference: The system leverages crowdsourcing to collect multiple temperature estimates from different smartphones within a local area. It then employs a Confidence-based Tree-structure (CBTS) model to aggregate these estimates and derive a more accurate final temperature reading.

Automated Data Labeling: To address the challenge of limited training data for new smartphones, the system utilizes the CBTS model to automatically generate labels for the newly collected data, eliminating the need for manual labeling.

Few-shot Learning with Meta-learning: The system adopts a meta-learning-based few-shot learning strategy, specifically the Model-Agnostic Meta-Learning (MAML) framework, to enable rapid fine-tuning of the temperature estimation model for new smartphones using only a small amount of data.

Additionally, the study explores the integration of federated learning to ensure the privacy protection of smartphone users during the collaborative training process.
The experimental results demonstrate the effectiveness of the proposed system. The CBTS-based crowdsourcing approach achieves a mean absolute error (MAE) of 0.136°C, outperforming various baseline methods. The few-shot learning strategy with MAML can reduce the estimation error to less than 1°C using only 5 data samples, significantly improving upon direct training and pre-training approaches.
The system has the potential to enable efficient and personalized thermal management in buildings, facilitating energy-saving efforts.

Statisztikák

The data set consists of 21,014 sample data points collected from 8 distinct smartphones.
The average mean absolute error (MAE) of the ambient temperature estimation model across all 6 contributor phones is 0.276°C.

Idézetek

"The application of crowdsourcing techniques reduces the error by approximately 50% compared to ambient temperature measurement by a single phone."
"Even with only 5 new data instances, by training with MAML, we can effectively reduce the estimation error to less than 1°C."

Főbb Kivonatok

A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy

by Dayin Chen,X... : arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10401.pdf

Mélyebb kérdések

How can the proposed system be extended to incorporate additional contextual information, such as occupancy patterns or building layout, to further improve the accuracy and granularity of temperature monitoring

To enhance the accuracy and granularity of temperature monitoring, the proposed system can be extended to incorporate additional contextual information such as occupancy patterns and building layout. By integrating occupancy data, the system can adjust temperature settings based on the number of people in a specific area, ensuring optimal comfort and energy efficiency. Building layout information can help in identifying temperature variations in different zones or rooms, allowing for targeted cooling or heating strategies. By combining these contextual factors with temperature data, the system can create more personalized and adaptive temperature control solutions tailored to specific areas within a building.

What are the potential challenges and privacy concerns associated with the large-scale deployment of such a distributed temperature monitoring system, and how can they be addressed

The large-scale deployment of a distributed temperature monitoring system may pose challenges and privacy concerns related to data security, data integrity, and user privacy. One challenge is ensuring the secure transmission of data between devices and the central server to prevent unauthorized access or data breaches. Privacy concerns arise from the collection of personal data from smartphones and the potential risk of data exposure. To address these challenges, robust encryption protocols should be implemented to protect data in transit and at rest. Additionally, strict access controls and authentication mechanisms should be in place to prevent unauthorized access to sensitive information. Transparent data handling policies and user consent mechanisms can help build trust and mitigate privacy concerns among users.

Given the advancements in edge computing and the increasing computational capabilities of smartphones, how can the system be further optimized to perform more complex data processing and model training directly on the devices, reducing the reliance on a central server

With the advancements in edge computing and the increasing computational capabilities of smartphones, the system can be optimized to perform more complex data processing and model training directly on the devices, reducing the reliance on a central server. By leveraging edge computing capabilities, smartphones can execute machine learning algorithms locally, enabling real-time data analysis and decision-making without the need for constant communication with a central server. This approach can reduce latency, enhance data privacy, and improve system scalability. Implementing on-device model training and inference can also lead to energy efficiency by minimizing data transmission and server processing requirements. Additionally, utilizing federated learning techniques can further enhance privacy and security by training models collaboratively across multiple devices while keeping data decentralized and secure.