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Develop an Optical Sensor to Detect Pollution Incidents in Sewerage

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Develop an Optical Sensor to Detect Pollution Incidents in Sewerage

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dc.contributor.author Rocher, Javier es_ES
dc.contributor.author Aldegheishem, Abdulaziz es_ES
dc.contributor.author Alrajeh, Nabil es_ES
dc.contributor.author Lloret, Jaime es_ES
dc.date.accessioned 2023-09-27T18:02:07Z
dc.date.available 2023-09-27T18:02:07Z
dc.date.issued 2022-12-15 es_ES
dc.identifier.issn 1530-437X es_ES
dc.identifier.uri http://hdl.handle.net/10251/197251
dc.description.abstract [EN] Cities are big consumers of energy and big producers of pollution. In the past years, the concept of smart cities has been applied to reduce the release of pollutants and to reduce energy consumption. In this article, we present a wireless sensor network (WSN) based on solid sensor nodes to detect illicit discharges in sewerage. The solid sensor is an optical sensor that uses infrared light to determine the pollutant concentration in water. At 0 degrees, a photoreceptor receives infrared LED (IR LED) light and allows the current passage. This provokes a reduction of the internal resistance of the photoreceptor that can be measured. First, we tested different intensities of powered LEDs and resistances in the voltage divider. Once the best combinations had been selected, we calibrated our sensor. Our result suggested that the relative error of our prototype is 3.4% in the range of 200-5000 mg/L. es_ES
dc.description.sponsorship This work was supported in part by the Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia, under Grant RSP-2021/295; in part by the Ministerio de Educacion, Cultura y Deporte through the "Ayudas para contratacion predoctoral de Formacion de Profesorado Universi-tario (FPU) (Convocatoria 2016)" under Grant FPU16/05540; in part by the "Ministerio de Ciencia e Innovacion (MCIN)" under Project PID2020-114467RR-C33; in part by the "Ministerio de Agricultura, Pesca y Alimentacion" through the Project GO TECNOGAR; and in part by the ThinkInAzul Program by MCIN through the European Union NextGenerationEU under Grant PRTR-C17.I1 and the Generalitat Valenciana under Grant THINKINAZUL/2021/002. es_ES
dc.language Inglés es_ES
dc.publisher Institute of Electrical and Electronics Engineers es_ES
dc.relation.ispartof IEEE Sensors Journal es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Arduino es_ES
dc.subject Healthy living in smart cities es_ES
dc.subject LED es_ES
dc.subject LoRaWAN es_ES
dc.subject Smart cities es_ES
dc.subject Solid sensor es_ES
dc.subject Turbidity es_ES
dc.subject Wireless sensor network (WSN) es_ES
dc.subject.classification INGENIERÍA TELEMÁTICA es_ES
dc.title Develop an Optical Sensor to Detect Pollution Incidents in Sewerage es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1109/JSEN.2022.3219931 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114467RR-C33/ES/RED HETEROGENEA INTELIGENTE DE SENSORES INALAMBRICOS PARA MONITORIZAR Y ESTIMAR EL CONTENIDO DE RESINA DE CISTUS LADANIFER/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GV INNOV.UNI.CIENCIA//THINKINAZUL%2F2021%2F002//Red de Sensores y big data para la observacion del entorno marino (SALVADOR)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MECD//FPU16%2F05540/ES/FPU16%2F05540/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/KSU//RSP-2021%2F295//Researchers Supporting Project/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//PRTR-C17.I1/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Tecnología de Alimentos - Departament de Tecnologia d'Aliments es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia es_ES
dc.description.bibliographicCitation Rocher, J.; Aldegheishem, A.; Alrajeh, N.; Lloret, J. (2022). Develop an Optical Sensor to Detect Pollution Incidents in Sewerage. IEEE Sensors Journal. 22(24):24449-24457. https://doi.org/10.1109/JSEN.2022.3219931 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1109/JSEN.2022.3219931 es_ES
dc.description.upvformatpinicio 24449 es_ES
dc.description.upvformatpfin 24457 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 22 es_ES
dc.description.issue 24 es_ES
dc.relation.pasarela S\496520 es_ES
dc.contributor.funder King Saud University es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.contributor.funder MINISTERIO DE EDUCACION es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder Ministerio de Agricultura, Alimentación y Medio Ambiente es_ES


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