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In-cabin and outdoor environmental monitoring in vehicular scenarios with distributed computing

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In-cabin and outdoor environmental monitoring in vehicular scenarios with distributed computing

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dc.contributor.author Ramos-Sorroche, Emilio es_ES
dc.contributor.author Rubio-Aparicio, Jesus es_ES
dc.contributor.author Santa, Jose es_ES
dc.contributor.author Guardiola, Carlos es_ES
dc.contributor.author Egea-Lopez, Esteban es_ES
dc.date.accessioned 2024-12-17T19:02:29Z
dc.date.available 2024-12-17T19:02:29Z
dc.date.issued 2024-04 es_ES
dc.identifier.uri http://hdl.handle.net/10251/213009
dc.description.abstract [EN] Accurate environmental monitoring is becoming the basis for assuring sustainable development in administrations at different levels, including cities and industry as key actors. However, current techniques rely on static stations that may not be representative of larger areas, for the case of outdoor scenarios, or even not considering indoor spaces where people can remain for long periods. This is the case of vehicles. The COVID-19 pandemic has remarked the importance of measuring air quality indoors, for instance. With the aim of solving this twofold issue, this work proposes an in-cabin and outdoor air pollution monitoring system to assure healthy conditions when travelling, driving and operating vehicles, and to analyse the evolution of environmental parameters in cities. This effort is carried out exploiting distributed computing with micro-services, betting for an on-board hardware solution provided with sensors for measuring particulate matter, CO, CO2, NO2, O3, temperature and humidity. While basic data pre-processing is carried out in this acquisition unit, edge processing is performed on a single board computer aboard and intermediary communication nodes in the network path from the vehicle to the cloud. Vehicle connectivity is provided by 4G cellular and Low-Power Wide Area (LPWAN) networks. Global environmental perception is acquired by cloud-based software powered by machine learning and time series analysis. The whole solution has been validated and tested in the city of Cartagena (Spain), with good performance in terms of data collection, communication links and service offered. es_ES
dc.description.sponsorship This work was supported by the grants PID2020-112675RBC41 (ONOFRE-3) , funded by MCIN/AEI/10.13039/501100011033; RYC-2017-23823, funded by MCIN/AEI/10.13039/501100011033 and by "ESF Investing in your future"; CNS2022-136150 (WILLIOT) , funded by MCIN/AEI/10.13039/501100011033 and by "European Union NextGenerationEU/PRTR"; and H2020 957258 (ASSIST-IoT) , funded by the European Commission. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Internet of Things es_ES
dc.rights Reconocimiento - No comercial (by-nc) es_ES
dc.subject Environmental monitoring es_ES
dc.subject IoT es_ES
dc.subject Smart cities es_ES
dc.subject Intelligent transportation systems es_ES
dc.subject Crowdsensing es_ES
dc.subject Edge computing es_ES
dc.subject.classification MAQUINAS Y MOTORES TERMICOS es_ES
dc.title In-cabin and outdoor environmental monitoring in vehicular scenarios with distributed computing es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.iot.2023.101009 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-112675RB-C41/ES/ADAPTACION DE RECURSOS DE COMPUTO Y RED DESDE LA NUBE AL EXTREMO: PLANIFICACION Y ACCESO COORDINADO OPTIMO (ONOFRE-3-UPCT)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/957258/EU/Architecture for Scalable, Self-*, human-centric, Intelligent, Secure, and Tactile next generation IoT/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//RYC-2017-23823/ES/Vehicular Telematics for Next-Generation Moving Smart Spaces es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//CNS2022-136150/ES/INTERNET DE LAS COSAS MOVILES CON TECNOLOGIAS INALAMBRICAS DE NUEVA GENERACION es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials es_ES
dc.description.bibliographicCitation Ramos-Sorroche, E.; Rubio-Aparicio, J.; Santa, J.; Guardiola, C.; Egea-Lopez, E. (2024). In-cabin and outdoor environmental monitoring in vehicular scenarios with distributed computing. Internet of Things. 25. https://doi.org/10.1016/j.iot.2023.101009 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.iot.2023.101009 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 25 es_ES
dc.identifier.eissn 2542-6605 es_ES
dc.relation.pasarela S\508727 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES


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