Mobile Networks and Applications

, Volume 23, Issue 6, pp 1693–1702 | Cite as

A Discretized Approach to Air Pollution Monitoring Using UAV-based Sensing

  • Oscar AlvearEmail author
  • Carlos T. Calafate
  • Nicola Roberto Zema
  • Enrico Natalizio
  • Enrique Hernández-Orallo
  • Juan-Carlos Cano
  • Pietro Manzoni


Recently, Unmanned Aerial Vehicles (UAVs) have become a cheap alternative to sense pollution values in a certain area due to their flexibility and ability to carry small sensing units. In a previous work, we proposed a solution, called Pollution-driven UAV Control (PdUC), to allow UAVs to autonomously trace pollutant sources, and monitor air quality in the surrounding area. However, despite operational, we found that the proposed solution consumed excessive time, especially when considering the battery lifetime of current multi-rotor UAVs. In this paper, we have improved our previously proposed solution by adopting a space discretization technique. Discretization is one of the most efficient mathematical approaches to optimize a system by transforming a continuous domain into its discrete counterpart. The improvement proposed in this paper, called PdUC-Discretized (PdUC-D), consists of an optimization whereby UAVs only move between the central tile positions of a discretized space, avoiding monitoring locations separated by small distances, and whose actual differences in terms of air quality are barely noticeable. We also analyze the impact of varying the tile size on the overall process, showing that smaller tile sizes offer high accuracy at the cost of an increased flight time. Taking into account the obtained results, we consider that a tile size of 100 × 100 meters offers an adequate trade-off between flight time and monitoring accuracy. Experimental results show that PdUC-D drastically reduces the convergence time compared to the original PdUC proposal without loss of accuracy, and it also increases the performance gap with standard mobility patterns such as Spiral and Billiard.


UAV control system Air pollution monitoring Discretized systems 



This work was partially supported by the “Programa Estatal de Investigación, Desarrollo e Innovación Orientada a Retos de la Sociedad, Proyecto I+D+I TEC2014-52690-R”, the framework of the DIVINA Challenge Team, which is funded under the Labex MS2T program. Labex MS2T is supported by the French Government, through the program “Investments for the future” managed by the National Agency for Research (Reference: ANR-11-IDEX-0004-02), the “Programa de becas SENESCYT de la República del Ecuador”, and the Research Direction of the University of Cuenca.


  1. 1.
    Adam-poupart A, Brand A, Fournier M, Jerrett M, Smargiassi A (2014) Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian Maximum entropy–LUR approaches. Environ Health Perspect 970(2013):1–19. Google Scholar
  2. 2.
    Agency U.S.E.P. (2015) Air Quality Index Available:
  3. 3.
    Alvear O, Calafate CT, Hernández-Orallo E, Cano JC, Manzoni P (2015) Mobile Pollution Data Sensing Using UAVs The 13th International Conference on Advances in Mobile Computing and MultimediaGoogle Scholar
  4. 4.
    Alvear O, Zamora W, Calafate C, Cano JC, Manzoni P (2016) An architecture offering mobile pollution sensing with high spatial resolution. J Sens:2016Google Scholar
  5. 5.
    Alvear O, Zema NR, Natalizio E, Calafate CT (2017) Using uav-based systems to monitor air pollution in areas with poor accessibility. J Adv Transp:2017Google Scholar
  6. 6.
    Alvear OA, Zema NR, Natalizio E, Calafate CT (2017) A chemotactic pollution-homing uav guidance system. In: 2017 13th international Wireless communications and mobile computing conference (IWCMC). IEEE, pp 2115–2120Google Scholar
  7. 7.
    André M (2004) The artemis european driving cycles for measuring car pollutant emissions. Sci Total Environ 334:73–84CrossRefGoogle Scholar
  8. 8.
    Basu P, Redi J, Shurbanov V (2004) Coordinated flocking of uavs for improved connectivity of mobile ground nodes. In: 2004 IEEE Military communications conference, MILCOM, vol 3. IEEE, pp 1628–1634Google Scholar
  9. 9.
    Biomo JDMM, Kunz T, St-Hilaire M (2014) An enhanced gauss-markov mobility model for simulations of unmanned aerial ad hoc networks. In: 2014 7th IFIP Wireless and mobile networking conference (WMNC). IEEE, pp 1–8Google Scholar
  10. 10.
    Bouachir O, Abrassart A, Garcia F, Larrieu N (2014) A mobility model for uav ad hoc network. In: 2014 international conference on Unmanned aircraft systems (ICUAS). IEEE, pp 383–388Google Scholar
  11. 11.
    Cox TH, Nagy CJ, Skoog MA, Somers IA, Warner R Civil uav capability assessmentGoogle Scholar
  12. 12.
    Eisenman SB, Miluzzo E, Lane ND, Peterson RA, Ahn GS, Campbell AT (2009) Bikenet: a mobile sensing system for cyclist experience mapping. ACM Transactions on Sensor Networks (TOSN) 6(1):6CrossRefGoogle Scholar
  13. 13.
    Erman AT, van Hoesel L, Havinga P, Wu J (2008) Enabling mobility in heterogeneous wireless sensor networks cooperating with uavs for mission-critical management. IEEE Wirel Commun 15(6):38–46CrossRefGoogle Scholar
  14. 14.
    Fayyad U, Irani K (1993) Multi-interval discretization of continuous-valued attributes for classification learningGoogle Scholar
  15. 15.
    Hugenholtz CH, Moorman BJ, Riddell K, Whitehead K (2012) Small unmanned aircraft systems for remote sensing and earth science research. Eos, Trans Amer Geophysical Union 93(25):236–236CrossRefGoogle Scholar
  16. 16.
    Illingworth S, Allen G, Percival C, Hollingsworth P, Gallagher M, Ricketts H, Hayes H, adosz H, Crawley PD, Roberts G (2014) Measurement of boundary layer ozone concentrations on-board a Skywalker unmanned aerial vehicle. Atmos Sci Lett 15(4):252–258Google Scholar
  17. 17.
    Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, pp 760–766Google Scholar
  18. 18.
    Khan A, Schaefer D, Tao L, Miller DJ, Sun K, Zondlo MA, Harrison WA, Roscoe B, Lary DJ (2012) Low power greenhouse gas sensors for unmanned aerial vehicles. Remote Sens 4(5):1355–1368CrossRefGoogle Scholar
  19. 19.
    Kuiper E, Nadjm-Tehrani S (2006) Mobility models for uav group reconnaissance applications. In: 2006 International conference on wireless and mobile communications (ICWMC’06). IEEE, pp 33–33Google Scholar
  20. 20.
    McFrederick Q, Kathilankal J, Fuentes J (2008) Air pollution modifies floral scent trails. Atmos Environ 42(10):2336–2348CrossRefGoogle Scholar
  21. 21.
    MQ131 Ozone Sensor (2017) Datasheet:
  22. 22.
    Orfanus D, de Freitas EP (2014) Comparison of uav-based reconnaissance systems performance using realistic mobility models. In: 2014 6Th international congress on ultra modern telecommunications and control systems and workshops (ICUMT). IEEE, pp 248–253Google Scholar
  23. 23.
    Pajares G (2015) Overview and current status of remote sensing applications based on unmanned aerial vehicles (uavs). Photogram Eng Remote Sens 81(4):281–329CrossRefGoogle Scholar
  24. 24.
    Pujadas M, Plaza J, Teres Jx, Artıñano B, Millan M (2000) Passive remote sensing of nitrogen dioxide as a tool for tracking air pollution in urban areas: the madrid urban plume, a case of study. Atmos Environ 34(19):3041–3056CrossRefGoogle Scholar
  25. 25.
    R Core Team: R (2016) A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
  26. 26.
    Seaton A, Godden D, MacNee W, Donaldson K (1995) Particulate air pollution and acute health effects. The lancet 345(8943):176–178CrossRefGoogle Scholar
  27. 27.
    Stein ML (1999) Statistical interpolation of spatial data: some theory for kriging. Springer, New YorkCrossRefGoogle Scholar
  28. 28.
    Teh SK, Mejias L, Corke P, Hu W (2008) Experiments in integrating autonomous uninhabited aerial vehicles(uavs) and wireless sensor networks. In: 2008 Australasian Conference on Robotics and Automation (ACRA 08). The Australian Robotics and Automation Association Inc., Canberra.
  29. 29.
    Wan Y, Namuduri K, Zhou Y, Fu S (2013) A smooth-turn mobility model for airborne networks. IEEE Trans Veh Technol 62(7):3359–3370CrossRefGoogle Scholar
  30. 30.
    Wang W, Guan X, Wang B, Wang Y (2010) A novel mobility model based on semi-random circular movement in mobile ad hoc networks. Inf Sci 180(3):399–413CrossRefGoogle Scholar
  31. 31.
    Zhou B, Xu K, Gerla M (2004) Group and swarm mobility models for ad hoc network scenarios using virtual tracks. In: 2004 IEEE Military communications conference, MILCOM 2004, vol 1. IEEE, pp 289–294Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Electronics and TelecommunicationsUniversidad de CuencaCuencaEcuador
  2. 2.Department of Computer EngineeringUniversitat Politècnica de ValènciaValenciaSpain
  3. 3.IFSTTAR, COSYSUniv Lille Nord de FranceVilleneuve d’AscqFrance
  4. 4.CNRS, Laboratoire HeudiasycSorbonne Universités, Université de Technologie de CompiègneCompiegne CedexFrance

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