Publication Type

Journal Article

Version

publishedVersion

Publication Date

6-2017

Abstract

Urban air pollution monitoring with mobile, portable, low-cost sensors has attracted increasing research interest for their wide spatial coverage and affordable expenses to the general public. However, low-cost air quality sensors not only drift over time but also suffer from cross-sensitivities and dependency on meteorological effects. Therefore calibration of measurements from low-cost sensors is indispensable to guarantee data accuracy and consistency to be fit for quantitative studies on air pollution. In this work we propose sensor array network calibration (SCAN), a multi-hop calibration technique for dependent low-cost sensors. SCAN is applicable to sets of co-located, heterogeneous sensors, known as sensor arrays, to compensate for cross-sensitivities and dependencies on meteorological influences. SCAN minimizes error accumulation over multiple hops of sensor arrays, which is unattainable with existing multi-hop calibration techniques. We formulate SCAN as a novel constrained least-squares regression and provide a closed-form expression of its regression parameters. We theoretically prove that SCAN is free from regression dilution even in presence of measurement noise. In-depth simulations demonstrate that SCAN outperforms various calibration techniques. Evaluations on two real-world low-cost air pollution sensor datasets comprising 66 million samples collected over three years show that SCAN yields 16% to 60% lower error than state-of-the-art calibration techniques.

Keywords

Sensor Array, Calibration, Urban Sensing

Discipline

Hardware Systems | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Volume

1

Issue

2

First Page

19:1

Last Page

19:21

ISSN

2474-9567

Identifier

10.1145/3090084

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1145/3090084

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