Missing Data Estimation in a Low-Cost Sensor Network for Measuring Air Quality: a Case Study in Aburrá Valley
According to the World Health Organization (WHO), air pollution is currently one leading cause of death around the world. As a result, some projects have emerged to monitor air quality through the implementation of low-cost Wireless Sensor Networks (WSNs). However, the type of technology and the sen...
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| Published in | Water, air, and soil pollution Vol. 232; no. 10 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Springer International Publishing
01.10.2021
Springer Springer Nature B.V |
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| Abstract | According to the World Health Organization (WHO), air pollution is currently one leading cause of death around the world. As a result, some projects have emerged to monitor air quality through the implementation of low-cost Wireless Sensor Networks (WSNs). However, the type of technology and the sensors’ location have an impact on data quality, resulting in a considerable amount of missing data. This hinders the proper implementation of methodologies for sensor calibration and data leverage for dispersion analysis of pollutants and prediction of pollution episodes. This paper presents a methodology based on matrix factorization (MF) to recover missing data from a low-cost WSN for particulate matter PM2.5 measurement. Using the proposed methodology with the study case in Aburrá Valley, Colombia, it is shown that is possible to recover 40
%
missing data with less than 12
%
errors, obtaining better results than those presented by other methods found in the literature. |
|---|---|
| AbstractList | According to the World Health Organization (WHO), air pollution is currently one leading cause of death around the world. As a result, some projects have emerged to monitor air quality through the implementation of low-cost Wireless Sensor Networks (WSNs). However, the type of technology and the sensors’ location have an impact on data quality, resulting in a considerable amount of missing data. This hinders the proper implementation of methodologies for sensor calibration and data leverage for dispersion analysis of pollutants and prediction of pollution episodes. This paper presents a methodology based on matrix factorization (MF) to recover missing data from a low-cost WSN for particulate matter PM2.5 measurement. Using the proposed methodology with the study case in Aburrá Valley, Colombia, it is shown that is possible to recover 40
%
missing data with less than 12
%
errors, obtaining better results than those presented by other methods found in the literature. According to the World Health Organization (WHO), air pollution is currently one leading cause of death around the world. As a result, some projects have emerged to monitor air quality through the implementation of low-cost Wireless Sensor Networks (WSNs). However, the type of technology and the sensors’ location have an impact on data quality, resulting in a considerable amount of missing data. This hinders the proper implementation of methodologies for sensor calibration and data leverage for dispersion analysis of pollutants and prediction of pollution episodes. This paper presents a methodology based on matrix factorization (MF) to recover missing data from a low-cost WSN for particulate matter PM2.5 measurement. Using the proposed methodology with the study case in Aburrá Valley, Colombia, it is shown that is possible to recover 40% missing data with less than 12% errors, obtaining better results than those presented by other methods found in the literature. |
| ArticleNumber | 436 |
| Audience | Academic |
| Author | Rivera-Muñoz, León M. Gallego-Villada, Juan D. Giraldo-Forero, Andrés F. Martinez-Vargas, Juan D. |
| Author_xml | – sequence: 1 givenname: León M. orcidid: 0000-0002-2559-236X surname: Rivera-Muñoz fullname: Rivera-Muñoz, León M. email: leonrivera281329@correo.itm.edu.co organization: Instituto Tecnológico Metropolitano (ITM) – sequence: 2 givenname: Juan D. surname: Gallego-Villada fullname: Gallego-Villada, Juan D. organization: Instituto Tecnológico Metropolitano (ITM) – sequence: 3 givenname: Andrés F. surname: Giraldo-Forero fullname: Giraldo-Forero, Andrés F. organization: Instituto Tecnológico Metropolitano (ITM) – sequence: 4 givenname: Juan D. surname: Martinez-Vargas fullname: Martinez-Vargas, Juan D. organization: Instituto Tecnológico Metropolitano (ITM) |
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| CitedBy_id | crossref_primary_10_1016_j_scitotenv_2024_171522 crossref_primary_10_1016_j_ecoinf_2022_101775 crossref_primary_10_1007_s11270_023_06127_9 crossref_primary_10_1002_met_2161 crossref_primary_10_1007_s11270_022_05679_6 crossref_primary_10_5194_amt_16_5415_2023 |
| Cites_doi | 10.1109/TMC.2016.2595569 10.1109/MASCOTS.2016.45 10.1016/j.knosys.2017.12.034 10.1109/JIOT.2018.2853660 10.1016/j.envint.2016.12.007 10.1016/j.eswa.2020.113685 10.1145/1454008.1454049 10.1109/WoWMoM.2017.7974338 10.1016/j.envint.2018.08.033 10.1016/j.neucom.2020.09.085 10.1016/j.neucom.2020.06.049 10.1016/j.measurement.2018.07.039 10.1109/SAM.2016.7569735 10.1007/978-3-642-35289-8_25 10.1007/978-3-662-48331-2 10.1109/MC.2009.263 10.1021/acs.est.8b01826 10.1016/j.biopsych.2014.07.012 10.1007/978-3-319-22482-4 10.1109/isi.2007.379474 10.1016/j.envint.2017.05.005 10.1016/j.adhoc.2019.01.008 10.1007/978-94-011-5014-9_23 10.1109/JSEN.2013.2297714 10.3390/s151229859 10.1016/j.neucom.2019.11.109 10.1109/TKDE.2012.51 10.1080/03610928008827904 |
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| Keywords | Matrix factorization Low-cost sensors network Missing data estimation Machine learning |
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| References | Vincent, E., Yeredor, A., Koldovský, Z., & Tichavský, P. (2015). Blind calibration of mobile sensors using informed nonnegative matrix factorization. Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9237(March 2018). https://doi.org/10.1007/978-3-319-22482-4. WHO. (2018). Ambient (outdoor) air pollution. https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health. WHO. (2018). The top 10 causes of death. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death. SchneiderPCastellNVogtMDaugeFRLahozWABartonovaAMapping urban air quality in near real-time using observations from low-cost sensors and model informationEnvironment International2017106December 20162342471:CAS:528:DC%2BC2sXhtFSqs7fP10.1016/j.envint.2017.05.005https://doi.org/10.1016/j.envint.2017.05.005 SIATA. (2021). Geoportal. https://siata.gov.co. ShahJMishraBIot-enabled low power environment monitoring system for prediction of pm2. 5Pervasive and Mobile Computing202067101175 XieKNingXWangXXieDCaoJXieGWenJRecover corrupted data in sensor networks: a matrix completion solutionIEEE Transactions on Mobile Computing20171651434144810.1109/TMC.2016.2595569https://doi.org/10.1109/TMC.2016.2595569 HaglerGSWWilliamsRPapapostolouVPolidoriAAir quality sensors and data adjustment algorithms: When is it no longer a measurement?Environmental Science & Technology 20185210553055311:CAS:528:DC%2BC1cXotFeksrs%3D10.1021/acs.est.8b01826https://doi.org/10.1021/acs.est.8b01826. PMID: 29688714 ImanRLDavenportJMApproximations of the critical region of the fbietkan statisticCommunications in Statistics-Theory and Methods19809657159510.1080/03610928008827904 Wang, J., & Zhang, J. (2007). Addressing accuracy issues in privacy preserving data mining through matrix factorization. ISI 2007: 2007 IEEE Intelligence and Security Informatics, pp. 217–220. https://doi.org/10.1109/isi.2007.379474. HeYchang PiDImproving knn method based on reduced relational grade for microarray missing values imputationIAENG International Journal of Computer Science2016433356362 Rasmussen, C. E. (2003). LNAI 3176 - Gaussian Processes In Machine Learning. Tech rep. Hautecoeur, C., & Glineur, F. (2020). Nonnegative matrix factorization over continuous signals using parametrizable functions. Neurocomputing. CárdenasAMRiveraLMGómezBLValenciaGMAcostaHACorreaJDShort communication: Pollution-and-greenhouse gases measurement systemMeasurement201812956556810.1016/j.measurement.2018.07.039https://doi.org/10.1016/j.measurement.2018.07.039 Álvarez, M., Luengo, D., Titsias, M., & Lawrence, N. D. (2010). Efficient multioutput gaussian processes through variational inducing kernels. In Y. W. Teh M. Titterington (Eds.) Proceedings of the thirteenth international conference on artificial intelligence and statistics, proceedings of machine learning research. PMLR, Chia Laguna Resort, Sardinia, Italy, (Vol. 9 pp. 25–32). MaagBZhouZThieleLA survey on sensor calibration in air pollution monitoring deploymentsIEEE Internet of Things Journal2018564857487010.1109/JIOT.2018.2853660https://doi.org/10.1109/JIOT.2018.2853660 PedregosaFVaroquauxGGramfortAMichelVThirionBGriselOBlondelMPrettenhoferPWeissRDubourgVVanderplasJPassosACournapeauDBrucherMPerrotMDuchesnayEScikit-learn: Machine learning in PythonJournal of Machine Learning Research20111228252830 Liu, X., Xi, T., & Ngai, E. (2016). Data modelling with gaussian process in sensor networks for urban environmental monitoring. In 2016 IEEE 24th international symposium on modeling, analysis and simulation of computer and telecommunication systems (MASCOTS) (pp. 457–462). Williams, C. (1999). Prediction with Gaussian processes. Learning in graphical models, pp 599–621. Mnih, A., & Salakhutdinov, R. R. (2008). Probabilistic matrix factorization. In Advances in neural information processing systems (pp. 1257–1264). MazaheriMCliffordSYeganehBVianaMRizzaVFlamentRBuonannoGMorawskaLInvestigations into factors affecting personal exposure to particles in urban microenvironments using low-cost sensorsEnvironment International2018120January4965041:CAS:528:DC%2BC1cXhsF2mu7fL10.1016/j.envint.2018.08.033https://doi.org/10.1016/j.envint.2018.08.033 LiuHCaiJOngYSRemarks on multi-output gaussian process regressionKnowledge-Based Systems201814410212110.1016/j.knosys.2017.12.034 Barcelo-Ordinas, J. M., Doudou, M., Garcia-Vidal, J., & Badache, N. (2019). Self-calibration methods for uncontrolled environments in sensor networks: A reference survey Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2019.01.008. Huang, Q., Yin, X., Chen, S., Wang, Y., & Chen, B. (2020). Robust nonnegative matrix factorization with structure regularization. Neurocomputing. https://doi.org/10.1016/j.neucom.2020.06.049. minambiente. (2008). K2 Ingeniería: Protocolo para el monitoreo y seguimiento de la calidad del aire. Tech. rep., Bogotá D.C. www.minambiente.gov.co. Koki MiuraMTOkadaYA recommender system based on an improved simultaneous selection method of query items and neighborsIAENG International Journal of Computer Science2016434406410 YiWYLoKMMakTLeungKSLeungYMengMLA survey of wireless sensor network based air pollution monitoring systemsSensors2015151231,39231,42710.3390/s151229859 Jakomin, M., Bosnić, Z., & Curk, T. (2020). Simultaneous incremental matrix factorization for streaming recommender systems. Expert Systems with Applications, p 113685. https://doi.org/10.1016/j.eswa.2020.113685. Ross, B. (2008). Non-Negative Matrix factorization techniques and optimizations springer. https://doi.org/10.1007/978-3-662-48331-2. Wang, W., De, S., Zhou, Y., Huang, X., & Moessner, K. (2017). Distributed sensor data computing in smart city applications. In 2017 IEEE 18th international symposium on a world of wireless, mobile and multimedia networks (woWMom) (pp. 1–5). IEEE. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix recommender techniques for factorization systems, pp 42–49. https://doi.org/10.1109/MC.2009.263. Lung, S. C., Jones, R., Zellweger, C., Karppinen, A., Penza, M., Dye, T., Hu̇glin, C., Ning, Z., Lewis, A. C., von Schneidemesser, E., Peltier, R. E., Leigh, R., Hagan, D., Laurent, O., & Carmichael, G. (2018). Low-cost sensors for the measurement of atmospheric composition: overview of topic and future applications. May. https://doi.org/10.1016/j.biopsych.2014.07.012. de Wolff, T., Cuevas, A., & Tobar, F. (2020). MOGPTK: The Multi-Output Gaussian Process Toolkit. arXiv:2002.03471. https://github.com/GAMES-UChile/mogptk. Takács, G., Pilaszy, I., Nemeth, B., & Tikk, D. (2008). Matrix factorization and neighbor based algorithms for the netflix prize problem categories and subject descriptors. In Proceedings of the 2008 ACM conference on Recommender systems - RecSys ’08 (pp. 267–274). Dorffer, C., Puigt, M., Delmaire, G., & Roussel, G. (2016). Nonlinear mobile sensor calibration using informed semi-nonnegative matrix factorization with a Vandermonde factor Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop 2016-Septe. https://doi.org/10.1109/SAM.2016.7569735. CastellNDaugeFRSchneiderPVogtMLernerUFishbainBBrodayDBartonovaACan commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?Environment International2017992933021:CAS:528:DC%2BC2sXjt1yh10.1016/j.envint.2016.12.007https://doi.org/10.1016/j.envint.2016.12.007 DemšarJStatistical comparisons of classifiers over multiple data setsThe Journal of Machine Learning Research20067130 WangYXZhangYJNonnegative matrix factorization: a comprehensive reviewIEEE Transactions on Knowledge and Data Engineering20132561336135310.1109/TKDE.2012.51https://doi.org/10.1109/TKDE.2012.51 LeeBtSonScKangKA blind calibration scheme exploiting mutual calibration relationships for a dense mobile sensor networkIEEE Sensors Journal20141451518152610.1109/JSEN.2013.2297714https://doi.org/10.1109/JSEN.2013.2297714 Bottou, L. (2012). Stochastic gradient descent tricks. In Neural networks: Tricks of the trade (pp. 421–436). Springer. 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| References_xml | – volume: 16 start-page: 1434 issue: 5 year: 2017 ident: 5363_CR39 publication-title: IEEE Transactions on Mobile Computing doi: 10.1109/TMC.2016.2595569 contributor: fullname: K Xie – ident: 5363_CR18 doi: 10.1109/MASCOTS.2016.45 – volume: 144 start-page: 102 year: 2018 ident: 5363_CR17 publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2017.12.034 contributor: fullname: H Liu – volume: 5 start-page: 4857 issue: 6 year: 2018 ident: 5363_CR21 publication-title: IEEE Internet of Things Journal doi: 10.1109/JIOT.2018.2853660 contributor: fullname: B Maag – volume: 43 start-page: 406 issue: 4 year: 2016 ident: 5363_CR14 publication-title: IAENG International Journal of Computer Science contributor: fullname: MT Koki Miura – volume: 99 start-page: 293 year: 2017 ident: 5363_CR4 publication-title: Environment International doi: 10.1016/j.envint.2016.12.007 contributor: fullname: N Castell – volume: 67 start-page: 175 issue: 101 year: 2020 ident: 5363_CR29 publication-title: Pervasive and Mobile Computing contributor: fullname: J Shah – ident: 5363_CR13 doi: 10.1016/j.eswa.2020.113685 – ident: 5363_CR24 – ident: 5363_CR20 – ident: 5363_CR31 doi: 10.1145/1454008.1454049 – ident: 5363_CR34 doi: 10.1109/WoWMoM.2017.7974338 – volume: 120 start-page: 496 issue: January year: 2018 ident: 5363_CR22 publication-title: Environment International doi: 10.1016/j.envint.2018.08.033 contributor: fullname: M Mazaheri – ident: 5363_CR5 doi: 10.1016/j.neucom.2020.09.085 – ident: 5363_CR11 doi: 10.1016/j.neucom.2020.06.049 – volume: 12 start-page: 2825 year: 2011 ident: 5363_CR25 publication-title: Journal of Machine Learning Research contributor: fullname: F Pedregosa – ident: 5363_CR30 – volume: 129 start-page: 565 year: 2018 ident: 5363_CR3 publication-title: Measurement doi: 10.1016/j.measurement.2018.07.039 contributor: fullname: AM Cárdenas – ident: 5363_CR36 – volume: 7 start-page: 1 year: 2006 ident: 5363_CR6 publication-title: The Journal of Machine Learning Research contributor: fullname: J Demšar – ident: 5363_CR7 doi: 10.1109/SAM.2016.7569735 – ident: 5363_CR2 doi: 10.1007/978-3-642-35289-8_25 – ident: 5363_CR27 doi: 10.1007/978-3-662-48331-2 – ident: 5363_CR26 – ident: 5363_CR15 doi: 10.1109/MC.2009.263 – volume: 52 start-page: 5530 issue: 10 year: 2018 ident: 5363_CR8 publication-title: Environmental Science & Technology doi: 10.1021/acs.est.8b01826 contributor: fullname: GSW Hagler – ident: 5363_CR19 doi: 10.1016/j.biopsych.2014.07.012 – ident: 5363_CR32 doi: 10.1007/978-3-319-22482-4 – ident: 5363_CR33 doi: 10.1109/isi.2007.379474 – volume: 106 start-page: 234 issue: December 2016 year: 2017 ident: 5363_CR28 publication-title: Environment International doi: 10.1016/j.envint.2017.05.005 contributor: fullname: P Schneider – ident: 5363_CR23 – volume: 43 start-page: 356 issue: 3 year: 2016 ident: 5363_CR10 publication-title: IAENG International Journal of Computer Science contributor: fullname: Y He – ident: 5363_CR1 doi: 10.1016/j.adhoc.2019.01.008 – ident: 5363_CR38 doi: 10.1007/978-94-011-5014-9_23 – volume: 14 start-page: 1518 issue: 5 year: 2014 ident: 5363_CR16 publication-title: IEEE Sensors Journal doi: 10.1109/JSEN.2013.2297714 contributor: fullname: Bt Lee – volume: 15 start-page: 31,392 issue: 12 year: 2015 ident: 5363_CR40 publication-title: Sensors doi: 10.3390/s151229859 contributor: fullname: WY Yi – ident: 5363_CR9 doi: 10.1016/j.neucom.2019.11.109 – ident: 5363_CR37 – volume: 25 start-page: 1336 issue: 6 year: 2013 ident: 5363_CR35 publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2012.51 contributor: fullname: YX Wang – volume: 9 start-page: 571 issue: 6 year: 1980 ident: 5363_CR12 publication-title: Communications in Statistics-Theory and Methods doi: 10.1080/03610928008827904 contributor: fullname: RL Iman |
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| Title | Missing Data Estimation in a Low-Cost Sensor Network for Measuring Air Quality: a Case Study in Aburrá Valley |
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