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Date
2017-06Type
- Conference Paper
ETH Bibliography
yes
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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 Show more
Publication status
publishedExternal links
Journal / series
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous TechnologiesVolume
Pages / Article No.
Publisher
Association for Computing MachineryEvent
Subject
Sensor Array; Calibration; Urban SensingOrganisational unit
03429 - Thiele, Lothar (emeritus) / Thiele, Lothar (emeritus)
Related publications and datasets
Is supplemented by: http://hdl.handle.net/20.500.11850/352334
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