Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility
Air pollution monitoring has recently become an issue of utmost importance in our society. Despite the fact that crowdsensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. Instead, deploying a fleet of UAVs could be considered an acceptab...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/8204353 |
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| _version_ | 1832554489757827072 |
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| author | Oscar Alvear Nicola Roberto Zema Enrico Natalizio Carlos T. Calafate |
| author_facet | Oscar Alvear Nicola Roberto Zema Enrico Natalizio Carlos T. Calafate |
| author_sort | Oscar Alvear |
| collection | DOAJ |
| description | Air pollution monitoring has recently become an issue of utmost importance in our society. Despite the fact that crowdsensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. Instead, deploying a fleet of UAVs could be considered an acceptable alternative. Embracing this approach, this paper proposes the use of UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. These UAVs are guided by our proposed Pollution-driven UAV Control (PdUC) algorithm, which is based on a chemotaxis metaheuristic and a local particle swarm optimization strategy. Together, they allow automatically performing the monitoring of a specified area using UAVs. Experimental results show that, when using PdUC, an implicit priority guides the construction of pollution maps by focusing on areas where the pollutants’ concentration is higher. This way, accurate maps can be constructed in a faster manner when compared to other strategies. The PdUC scheme is compared against various standard mobility models through simulation, showing that it achieves better performance. In particular, it is able to find the most polluted areas with more accuracy and provides a higher coverage within the time bounds defined by the UAV flight time. |
| format | Article |
| id | doaj-art-4af0c777e6734a06872dd86e7b66ebfc |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-4af0c777e6734a06872dd86e7b66ebfc2025-02-03T05:51:19ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/82043538204353Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor AccessibilityOscar Alvear0Nicola Roberto Zema1Enrico Natalizio2Carlos T. Calafate3Department of Computer Engineering, Universitat Politècnica de València, Camino de Vera, S/N, 46022 Valencia, SpainLaboratoire Heudiasyc, Sorbonne Universités, Université de Technologie de Compiègne, CNRS, 57 Avenue de Landshut, CS 60319, 60203 Compiegne Cedex, FranceLaboratoire Heudiasyc, Sorbonne Universités, Université de Technologie de Compiègne, CNRS, 57 Avenue de Landshut, CS 60319, 60203 Compiegne Cedex, FranceDepartment of Computer Engineering, Universitat Politècnica de València, Camino de Vera, S/N, 46022 Valencia, SpainAir pollution monitoring has recently become an issue of utmost importance in our society. Despite the fact that crowdsensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. Instead, deploying a fleet of UAVs could be considered an acceptable alternative. Embracing this approach, this paper proposes the use of UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. These UAVs are guided by our proposed Pollution-driven UAV Control (PdUC) algorithm, which is based on a chemotaxis metaheuristic and a local particle swarm optimization strategy. Together, they allow automatically performing the monitoring of a specified area using UAVs. Experimental results show that, when using PdUC, an implicit priority guides the construction of pollution maps by focusing on areas where the pollutants’ concentration is higher. This way, accurate maps can be constructed in a faster manner when compared to other strategies. The PdUC scheme is compared against various standard mobility models through simulation, showing that it achieves better performance. In particular, it is able to find the most polluted areas with more accuracy and provides a higher coverage within the time bounds defined by the UAV flight time.http://dx.doi.org/10.1155/2017/8204353 |
| spellingShingle | Oscar Alvear Nicola Roberto Zema Enrico Natalizio Carlos T. Calafate Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility Journal of Advanced Transportation |
| title | Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility |
| title_full | Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility |
| title_fullStr | Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility |
| title_full_unstemmed | Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility |
| title_short | Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility |
| title_sort | using uav based systems to monitor air pollution in areas with poor accessibility |
| url | http://dx.doi.org/10.1155/2017/8204353 |
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