MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost Air Monitoring Sensors

TL;DR

This paper introduces MTNet, a multi-task neural network that calibrates multiple low-cost air quality sensors simultaneously, leveraging task interactions to improve calibration accuracy over traditional single-task methods.

Contribution

The paper presents a novel multi-task calibration network with shared and task-specific modules, effectively modeling interactions among different sensor calibration tasks.

Findings

MTNet outperforms existing calibration methods on real-world datasets.

It effectively models interactions among multiple sensor calibration tasks.

Achieves state-of-the-art calibration accuracy across various sensors.

Abstract

The advances of sensor technology enable people to monitor air quality through widely distributed low-cost sensors. However, measurements from these sensors usually encounter high biases and require a calibration step to reach an acceptable performance in down-streaming analytical tasks. Most existing calibration methods calibrate one type of sensor at a time, which we call single-task calibration. Despite the popularity of this single-task schema, it may neglect interactions among calibration tasks of different sensors, which encompass underlying information to promote calibration performance. In this paper, we propose a multi-task calibration network (MTNet) to calibrate multiple sensors (e.g., carbon monoxide and nitrogen oxide sensors) simultaneously, modeling the interactions among tasks. MTNet consists of a single shared module, and several task-specific modules. Specifically, in the shared module, we extend the multi-gate mixture-of-experts structure to harmonize the task conflicts and correlations among different tasks; in each task-specific module, we introduce a feature selection strategy to customize the input for the specific task. These improvements allow MTNet to learn interaction information shared across different tasks, and task-specific information for each calibration task as well. We evaluate MTNet on three real-world datasets and compare it with several established baselines. The experimental results demonstrate that MTNet achieves the state-of-the-art performance.