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dc.contributor.authorMinchala Avila, Luis Ismael
dc.contributor.authorYoumin, Zhang
dc.contributor.authorYungaicela Naula, Noe Marcelo
dc.contributor.authorGarza Castañón, Luis Eduardo
dc.date.accessioned2020-05-19T02:35:37Z-
dc.date.available2020-05-19T02:35:37Z-
dc.date.issued2019
dc.identifier.issn2076-3417
dc.identifier.urihttp://dspace.ucuenca.edu.ec/handle/123456789/34340-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85072396469&origin=inward
dc.descriptionAir pollution is one of the greatest risks for the health of people. In recent years, platforms based on Unmanned Aerial Vehicles (UAVs) for the monitoring of pollution in the air have been studied to deal with this problem, due to several advantages, such as low-costs, security, multitask and ease of deployment. However, due to the limitations in the flying time of the UAVs, these platforms could perform monitoring tasks poorly if the mission is not executed with an adequate strategy and algorithm. Their application can be improved if the UAVs have the ability to perform autonomous monitoring of the areas with a high concentration of the pollutant, or even to locate the pollutant source. This work proposes an algorithm to locate an air pollutant's source by using a UAV. The algorithm has two components: (i) a metaheuristic technique is used to trace the increasing gradient of the pollutant concentration, and (ii) a probabilistic component complements the method by concentrating the search in the most promising areas in the targeted environment. The metaheuristic technique has been selected from a simulation-based comparative analysis between some classical techniques. The probabilistic component uses the Bayesian methodology to build and update a probability map of the pollutant source location, with each new sensor information available, while the UAV navigates in the environment. The proposed solution was tested experimentally with a real quadrotor navigating in a virtual polluted environment. The results show the effectiveness and robustness of the algorithm.
dc.description.abstractAir pollution is one of the greatest risks for the health of people. In recent years, platforms based on Unmanned Aerial Vehicles (UAVs) for the monitoring of pollution in the air have been studied to deal with this problem, due to several advantages, such as low-costs, security, multitask and ease of deployment. However, due to the limitations in the flying time of the UAVs, these platforms could perform monitoring tasks poorly if the mission is not executed with an adequate strategy and algorithm. Their application can be improved if the UAVs have the ability to perform autonomous monitoring of the areas with a high concentration of the pollutant, or even to locate the pollutant source. This work proposes an algorithm to locate an air pollutant’s source by using a UAV. The algorithm has two components: (i) a metaheuristic technique is used to trace the increasing gradient of the pollutant concentration, and (ii) a probabilistic component complements the method by concentrating the search in the most promising areas in the targeted environment. The metaheuristic technique has been selected from a simulation-based comparative analysis between some classical techniques. The probabilistic component uses the Bayesian methodology to build and update a probability map of the pollutant source location, with each new sensor information available, while the UAV navigates in the environment. The proposed solution was tested experimentally with a real quadrotor navigating in a virtual polluted environment. The results show the effectiveness and robustness of the algorithm.
dc.language.isoes_ES
dc.sourceApplied Sciences (Switzerland)
dc.subjectBayesian
dc.subjectAir pollution
dc.subjectUAV
dc.subjectSource location
dc.subjectMetaheuristic
dc.titleUAV-Based air pollutant source localization using combined metaheuristic and probabilistic methods
dc.typeARTÍCULO
dc.ucuenca.idautor0301453486
dc.ucuenca.idautor0302626205
dc.ucuenca.idautor0000-0001-9752-6022
dc.ucuenca.idautor0000-0002-9731-5943
dc.identifier.doi10.3390/app9183712
dc.ucuenca.versionVersión publicada
dc.ucuenca.areaconocimientounescoamplio07 - Ingeniería, Industria y Construcción
dc.ucuenca.afiliacionYoumin, Z., Concordia University (Montreal), Montreal, Canada
dc.ucuenca.afiliacionGarza, L., Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico
dc.ucuenca.afiliacionMinchala, L., Universidad de Cuenca, Departamento de Ingeniería Eléctrica, Electrónica y Telecomunicaciones(DEET), Cuenca, Ecuador
dc.ucuenca.afiliacionYungaicela, N., Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico
dc.ucuenca.correspondenciaGarza Castañón, Luis Eduardo, legarza@itesm.mx
dc.ucuenca.volumenvol. 9
dc.ucuenca.indicebibliograficoSCOPUS
dc.ucuenca.factorimpacto0.379
dc.ucuenca.cuartilQ1
dc.ucuenca.numerocitaciones0
dc.ucuenca.areaconocimientofrascatiamplio2. Ingeniería y Tecnología
dc.ucuenca.areaconocimientofrascatiespecifico2.2 Ingenierias Eléctrica, Electrónica e Información
dc.ucuenca.areaconocimientofrascatidetallado2.2.1 Ingeniería Eléctrica y Electrónica
dc.ucuenca.areaconocimientounescoespecifico071 - Ingeniería y Profesiones Afines
dc.ucuenca.areaconocimientounescodetallado0714 - Electrónica y Automatización
dc.ucuenca.urifuentehttps://www.mdpi.com/2076-3417/9/18
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