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

dc.contributor.authorBuelvas J
dc.contributor.authorMúnera D
dc.contributor.authorTobón V D.P
dc.contributor.authorAguirre J
dc.contributor.authorGaviria N.
dc.date.accessioned2023-10-24T19:24:00Z
dc.date.available2023-10-24T19:24:00Z
dc.date.created2023
dc.identifier.issn496979
dc.identifier.urihttp://hdl.handle.net/11407/7901
dc.description.abstractWith 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 monitoring systems. Following a systematic mapping method, and based on existing guidelines to assess data quality 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. © 2023, The Author(s).eng
dc.language.isoeng
dc.publisherInstitute for Ionics
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85152553772&doi=10.1007%2fs11270-023-06127-9&partnerID=40&md5=2745669834adc2ebf004d1b590024d8f
dc.sourceWater Air Soil Pollut.
dc.sourceWater, Air, and Soil Pollutioneng
dc.subjectAir qualityeng
dc.subjectData qualityeng
dc.subjectData quality enhancing techniqueseng
dc.subjectData quality indicatorseng
dc.subjectInternet of Thingseng
dc.subjectLow-cost sensorseng
dc.titleData Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Studyeng
dc.typeReview
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Telecomunicacionesspa
dc.type.spaRevisión
dc.identifier.doi10.1007/s11270-023-06127-9
dc.relation.citationvolume234
dc.relation.citationissue4
dc.publisher.facultyFacultad de Ingenieríasspa
dc.affiliationBuelvas, J., Faculty of Engineering, Universidad de Antioquia, Cl. 67 #53-108, Medellín, Colombia
dc.affiliationMúnera, D., Faculty of Engineering, Universidad de Antioquia, Cl. 67 #53-108, Medellín, Colombia
dc.affiliationTobón V, D.P., Faculty of Engineering, Universidad de Medellín, Cra. 87 #30-65, Medellín, Colombia
dc.affiliationAguirre, J., Faculty of Engineering, Universidad de Medellín, Cra. 87 #30-65, Medellín, Colombia
dc.affiliationGaviria, N., Faculty of Engineering, Universidad de Antioquia, Cl. 67 #53-108, Medellín, Colombia
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dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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