Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study

ABSTRACT: With the development of new technologies, particularly Internet of Things (IoT), there has been an increase in the deployment of low-cost air quality monitoring systems. Compared to traditional robust monitoring stations, these systems provide real-time information with higher spatio-tempo...

Full description

Autores:
Buelvas Pérez, Julio Hernán
Tobón Vallejo, Diana Patricia
Múnera Ramírez, Danny Alexandro
Aguirre Morales, Johnny Alexander
Gaviria Gómez, Natalia
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/35006
Acceso en línea:
https://hdl.handle.net/10495/35006
Palabra clave:
Contaminación del Aire
Air Pollution
Exactitud de los Datos
Data Accuracy
Internet de las Cosas
Internet of Things
Rights
openAccess
License
http://creativecommons.org/licenses/by/2.5/co/
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dc.title.spa.fl_str_mv Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study
title Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study
spellingShingle Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study
Contaminación del Aire
Air Pollution
Exactitud de los Datos
Data Accuracy
Internet de las Cosas
Internet of Things
title_short Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study
title_full Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study
title_fullStr Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study
title_full_unstemmed Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study
title_sort Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study
dc.creator.fl_str_mv Buelvas Pérez, Julio Hernán
Tobón Vallejo, Diana Patricia
Múnera Ramírez, Danny Alexandro
Aguirre Morales, Johnny Alexander
Gaviria Gómez, Natalia
dc.contributor.author.none.fl_str_mv Buelvas Pérez, Julio Hernán
Tobón Vallejo, Diana Patricia
Múnera Ramírez, Danny Alexandro
Aguirre Morales, Johnny Alexander
Gaviria Gómez, Natalia
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Investigación en Telecomunicaciones Aplicadas (GITA)
Intelligent Information Systems Lab.
dc.subject.decs.none.fl_str_mv Contaminación del Aire
Air Pollution
Exactitud de los Datos
Data Accuracy
Internet de las Cosas
Internet of Things
topic Contaminación del Aire
Air Pollution
Exactitud de los Datos
Data Accuracy
Internet de las Cosas
Internet of Things
description ABSTRACT: With the development of new technologies, particularly Internet of Things (IoT), there has been an increase in the deployment of low-cost air quality monitoring systems. Compared to traditional robust monitoring stations, these systems provide real-time information with higher spatio-temporal resolution. These systems use inexpensive and low-cost sensors, with lower accuracy as compared to robust systems. This fact has raised some concern regarding the quality of the data gathered by the IoT systems, which may compromise the performance of the environmental models. Considering the relevance of the data quality in this scenario, this paper presents a study of the data quality associated with IoT-based air quality monitor- ing systems. Following a systematic mapping method, and based on existing guidelines to assess data qual- ity in these systems, we have identified the main Data Quality (DQ) dimensions and the corresponding DQ enhancement techniques. After analyzing more than 70 papers, we found that the most common DQ dimensions targeted by the different works are accuracy and precision, which are enhanced by the use of different calibration techniques. Based on our findings, we present a discussion on the challenges that must be addressed in order to improve data quality in IoT-based air quality monitoring systems.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-05-12T22:08:41Z
dc.date.available.none.fl_str_mv 2023-05-12T22:08:41Z
dc.date.issued.none.fl_str_mv 2023
dc.type.spa.fl_str_mv Artículo de investigación
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dc.identifier.issn.none.fl_str_mv 0049-6979
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10495/35006
dc.identifier.doi.none.fl_str_mv 10.1007/s11270-023-06127-9
dc.identifier.eissn.none.fl_str_mv 1573-2932
identifier_str_mv 0049-6979
10.1007/s11270-023-06127-9
1573-2932
url https://hdl.handle.net/10495/35006
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.ispartofjournalabbrev.spa.fl_str_mv Water. Air. Soil. Pollut.
dc.relation.citationendpage.spa.fl_str_mv 248
dc.relation.citationstartpage.spa.fl_str_mv 234
dc.relation.citationvolume.spa.fl_str_mv 234
dc.relation.ispartofjournal.spa.fl_str_mv Water, Air, and Soil Pollution
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spelling Buelvas Pérez, Julio HernánTobón Vallejo, Diana PatriciaMúnera Ramírez, Danny AlexandroAguirre Morales, Johnny AlexanderGaviria Gómez, NataliaGrupo de Investigación en Telecomunicaciones Aplicadas (GITA)Intelligent Information Systems Lab.2023-05-12T22:08:41Z2023-05-12T22:08:41Z20230049-6979https://hdl.handle.net/10495/3500610.1007/s11270-023-06127-91573-2932ABSTRACT: With the development of new technologies, particularly Internet of Things (IoT), there has been an increase in the deployment of low-cost air quality monitoring systems. Compared to traditional robust monitoring stations, these systems provide real-time information with higher spatio-temporal resolution. These systems use inexpensive and low-cost sensors, with lower accuracy as compared to robust systems. This fact has raised some concern regarding the quality of the data gathered by the IoT systems, which may compromise the performance of the environmental models. Considering the relevance of the data quality in this scenario, this paper presents a study of the data quality associated with IoT-based air quality monitor- ing systems. Following a systematic mapping method, and based on existing guidelines to assess data qual- ity in these systems, we have identified the main Data Quality (DQ) dimensions and the corresponding DQ enhancement techniques. After analyzing more than 70 papers, we found that the most common DQ dimensions targeted by the different works are accuracy and precision, which are enhanced by the use of different calibration techniques. Based on our findings, we present a discussion on the challenges that must be addressed in order to improve data quality in IoT-based air quality monitoring systems.COL0025934COL004444823application/pdfengSpringerPaíses Bajoshttp://creativecommons.org/licenses/by/2.5/co/https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping StudyArtículo de investigaciónhttp://purl.org/coar/resource_type/c_2df8fbb1https://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionContaminación del AireAir PollutionExactitud de los DatosData AccuracyInternet de las CosasInternet of ThingsWater. Air. Soil. 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