Publication
A neural network approach in WSN real-time monitoring system to measure indoor air quality
| dc.contributor.author | Brito, Thadeu | |
| dc.contributor.author | Lima, José | |
| dc.contributor.author | Biondo, Elias Junior | |
| dc.contributor.author | Nakano, Alberto Yoshiro | |
| dc.contributor.author | Pereira, Ana I. | |
| dc.date.accessioned | 2024-01-05T10:06:32Z | |
| dc.date.available | 2024-01-05T10:06:32Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Indoor Air Quality (IAQ) pertains to the air quality within a specific space and is directly linked to the well-being and comfort of its occupants. In line with this objective, this research presents a real-time system dedicated to monitoring and predicting IAQ, encompassing both thermal comfort and gas concentration. The system initiates with a data acquisition, wherein a set of sensors captures environmental parameters and transmits this data for storage in a database. The measured parameters are analyzed by a neural network algorithm that predicts anomalies based on historical data. The neural network model generated predictions from 75.9% to 98.1% (depending on the parameter) of precision during regular situations. After that, a test with smoke in the same place was done to validate the model, and the results showed it could detect anomalies. Finally, prediction data are stored in a new database and displayed on a dashboard for monitoring in real-time measured and prediction data. | pt_PT |
| dc.description.sponsorship | The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCTIMCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LAlPI0007/2021). Thadeu Brito is supported by FCT PhD Grant Reference SFRHIBD/08598/2020. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Brito, Thadeu; Lima, José; Biondo, Elias; Nakano, Alberto; Pereira, Ana I. (2023). A neural network approach in WSN real-time monitoring system to measure indoor air quality. In 3rd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). 27-28 September 2023, Cairo, Egypt. ISBN 979-8-3503-0623-1. p. 233-238 | pt_PT |
| dc.identifier.doi | 10.1109/MIUCC58832.2023.10278362 | pt_PT |
| dc.identifier.isbn | 979-8-3503-0623-1 | |
| dc.identifier.uri | http://hdl.handle.net/10198/29102 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | IEEE | pt_PT |
| dc.relation | LA/P/0007/2021 | pt_PT |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Internet of things | pt_PT |
| dc.subject | Wireless sensor network | pt_PT |
| dc.subject | Indoor air quality | pt_PT |
| dc.subject | Artificial neural network | pt_PT |
| dc.title | A neural network approach in WSN real-time monitoring system to measure indoor air quality | pt_PT |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT | |
| oaire.citation.endPage | 238 | pt_PT |
| oaire.citation.startPage | 233 | pt_PT |
| oaire.citation.title | 3rd International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Brito | |
| person.familyName | Lima | |
| person.familyName | Pereira | |
| person.givenName | Thadeu | |
| person.givenName | José | |
| person.givenName | Ana I. | |
| person.identifier | BjSISEAAAAAJ | |
| person.identifier | R-000-8GD | |
| person.identifier.ciencia-id | C911-A95D-712F | |
| person.identifier.ciencia-id | 6016-C902-86A9 | |
| person.identifier.ciencia-id | 0716-B7C2-93E4 | |
| person.identifier.orcid | 0000-0002-5962-0517 | |
| person.identifier.orcid | 0000-0001-7902-1207 | |
| person.identifier.orcid | 0000-0003-3803-2043 | |
| person.identifier.rid | L-3370-2014 | |
| person.identifier.rid | F-3168-2010 | |
| person.identifier.scopus-author-id | 57200694948 | |
| person.identifier.scopus-author-id | 55851941311 | |
| person.identifier.scopus-author-id | 15071961600 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | restrictedAccess | pt_PT |
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