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Journal of Advanced Transportation
Volume 2017 (2017), Article ID 8204353, 14 pages
Research Article

Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility

1Department of Computer Engineering, Universitat Politècnica de València, Camino de Vera, S/N, 46022 Valencia, Spain
2Department of Electrical Engineering, Electronics and Telecommunications, Universidad de Cuenca, Av. 12 de Abril, S/N, Cuenca, Ecuador
3Laboratoire Heudiasyc, Sorbonne Universités, Université de Technologie de Compiègne, CNRS, 57 Avenue de Landshut, CS 60319, 60203 Compiegne Cedex, France

Correspondence should be addressed to Oscar Alvear

Received 8 March 2017; Revised 16 June 2017; Accepted 9 July 2017; Published 7 August 2017

Academic Editor: Guizhen Yu

Copyright © 2017 Oscar Alvear et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.