Multisensor Data Fusion Calibration in IoT Air Pollution Platforms

被引：66

作者：

Ferrer-Cid, Pau ^{[1
]}

Barcelo-Ordinas, Jose M. ^{[1
]}

Garcia-Vidal, Jorge ^{[1
]}

Ripoll, Anna ^{[2
]}

Viana, Mar ^{[3
]}

机构：

[1] Univ Politecn Cataluna, Dept Comp Architecture, Barcelona 08034, Spain

[2] 4Sfera Innova, Girona 17003, Spain

[3] Spanish Natl Res Council, Inst Environm Assessment & Water Res, Barcelona 08034, Spain

来源：

IEEE INTERNET OF THINGS JOURNAL | 2020年 / 7卷 / 04期

基金：

欧盟地平线“2020”;

关键词：

Correlated errors; Internet of Things (IoT) platform; low-cost sensors; machine learning models; multisensor data fusion; sensor calibration; uncontrolled environments; WIRELESS SENSOR NETWORKS; QUALITY MONITORING. PART; LOW-COST SENSORS; FIELD CALIBRATION; AVAILABLE SENSORS; PERFORMANCE; PROTOCOLS; CLUSTER; OZONE; MODEL;

D O I：

10.1109/JIOT.2020.2965283

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article investigates the calibration of low-cost sensors for air pollution. The sensors were deployed on three Internet of Things (IoT) platforms in Spain, Austria, and Italy during the summers of 2017, 2018, and 2019. One of the biggest challenges in the operation of an IoT platform, which has a great impact on the quality of the reported pollution values, is the calibration of the sensors in an uncontrolled environment. This calibration is performed using arrays of sensors that measure cross sensitivities and therefore compensate for both interfering contaminants and environmental conditions. This article investigates how the fusion of data taken by sensor arrays can improve the calibration process. In particular, calibration with sensor arrays, multisensor data fusion calibration with weighted averages, and multisensor data fusion calibration with machine learning models are compared. Calibration is evaluated by combining data from various sensors with linear and nonlinear regression models.

引用

页码：3124 / 3132

页数：9

共 30 条

[1]

[Anonymous], [No title captured]

[2]

[Anonymous], 2021, EFFICIENT ORIENTATIO

[3]

Barcelo-Ordinas JM, 2013, PRECISION AGRICULTURE '13, P801

[4] Distributed Multi-Scale Calibration of Low-Cost Ozone Sensors in Wireless Sensor Networks [J].

Barcelo-Ordinas, Jose M. ;

Ferrer-Cid, Pau ;

Garcia-Vidal, Jorge ;

Ripoll, Anna ;

Viana, Mar .

SENSORS, 2019, 19 (11)

[5] Self-calibration methods for uncontrolled environments in sensor networks: A reference survey [J].

Barcelo-Ordinas, Jose M. ;

Doudou, Messaoud ;

Garcia-Vidal, Jorge ;

Badache, Nadjib .

AD HOC NETWORKS, 2019, 88 :142-159

[6]

Barcelo-Ordinas JM, 2018, IEEE WCNC

[7] Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application [J].

Bigi, Alessandro ;

Mueller, Michael ;

Grange, Stuart K. ;

Ghermandi, Grazia ;

Hueglin, Christoph .

ATMOSPHERIC MEASUREMENT TECHNIQUES, 2018, 11 (06) :3717-3735

[8] A Review of Data Fusion Techniques [J].

Castanedo, Federico .

SCIENTIFIC WORLD JOURNAL, 2013,

[9] Architectures and algorithms for track association and fusion [J].

Chong, CY ;

Mori, S ;

Barker, WH ;

Chang, KC .

IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE, 2000, 15 (01) :5-13

[10] Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative machine learning approaches [J].

De Vito, S. ;

Esposito, E. ;

Salvato, M. ;

Popoola, O. ;

Formisano, F. ;

Jones, R. ;

Di Francia, G. .

SENSORS AND ACTUATORS B-CHEMICAL, 2018, 255 :1191-1210

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