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.
%0 Journal Article
%1 maag2017multihop
%A Maag, Balz
%A Zhou, Zimu
%A Saukh, Olga
%A Thiele, Lothar
%C New York, NY, USA
%D 2017
%I ACM
%J Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.
%K array box calibration eva everyaware hop multi no2 p2map sensor
%N 2
%P 19:1--19:21
%R 10.1145/3090084
%T SCAN: Multi-Hop Calibration for Mobile Sensor Arrays
%U http://doi.acm.org/10.1145/3090084
%V 1
%X 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.
@article{maag2017multihop,
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.},
acmid = {3090084},
added-at = {2018-11-09T09:01:27.000+0100},
address = {New York, NY, USA},
articleno = {19},
author = {Maag, Balz and Zhou, Zimu and Saukh, Olga and Thiele, Lothar},
biburl = {https://www.bibsonomy.org/bibtex/289c7cfd6f452a8507199cdb4ee6956e0/becker},
description = {SCAN},
doi = {10.1145/3090084},
interhash = {4d5cb6a006cc5a66367420c07cffafa1},
intrahash = {89c7cfd6f452a8507199cdb4ee6956e0},
issn = {2474-9567},
issue_date = {June 2017},
journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
keywords = {array box calibration eva everyaware hop multi no2 p2map sensor},
month = jun,
number = 2,
numpages = {21},
pages = {19:1--19:21},
publisher = {ACM},
timestamp = {2018-11-09T09:01:27.000+0100},
title = {SCAN: Multi-Hop Calibration for Mobile Sensor Arrays},
url = {http://doi.acm.org/10.1145/3090084},
volume = 1,
year = 2017
}