- -

Mobile Pollution Data Sensing Using UAVs

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Mobile Pollution Data Sensing Using UAVs

Show full item record

Alvear, O.; Tavares De Araujo Cesariny Calafate, CM.; Hernández Orallo, E.; Cano Escribá, JC.; Manzoni, P. (2015). Mobile Pollution Data Sensing Using UAVs. ACM. doi:10.1145/2837126.2843842

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/65645

Files in this item

Item Metadata

Title: Mobile Pollution Data Sensing Using UAVs
Author:
UPV Unit: Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Issued date:
Abstract:
Nowadays, the impact of global warming is causing societies to become more aware and responsive to environmental problems. As a result, pollution sensing is gaining more relevance. In order to have a strict control over ...[+]
Subjects: Mobile sensing , Multicopters , UAVs , Environmental monitoring.
Copyrigths: Reserva de todos los derechos
ISBN: 978-1-4503-3493-8
DOI: 10.1145/2837126.2843842
Publisher:
ACM
Publisher version: http://dl.acm.org/citation.cfm?id=2843842&CFID=628435637&CFTOKEN=10629677
Conference name: 13th International Conference on Advances in Mobile Computing and Multimedia (MoMM 2015)
Conference place: Brussels, Belgium
Conference date: December 11-13, 2015
Description: © ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM, In Proceedings of the 13th International Conference on Advances in Mobile Computing and Multimedia (pp. 393-397). http://dx.doi.org/10.1145/2837126.2843842
Type: Comunicación en congreso

References

European Topic Centre for Air Pollution and Climate Change Mitigation (ETC/ACM). http://acm.eionet.europa.eu/, Accessed: October 3, 2015.

United States Environmental Protection Agency (EPA). http://www.epa.gov/, Accessed: October 3, 2015.

A. Adam-Poupart, A. Brand, M. Fournier, M. Jerrett, and A. Smargiassi. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy--LUR approaches. Environmental health perspectives, 122(9):970, 2014. [+]
European Topic Centre for Air Pollution and Climate Change Mitigation (ETC/ACM). http://acm.eionet.europa.eu/, Accessed: October 3, 2015.

United States Environmental Protection Agency (EPA). http://www.epa.gov/, Accessed: October 3, 2015.

A. Adam-Poupart, A. Brand, M. Fournier, M. Jerrett, and A. Smargiassi. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy--LUR approaches. Environmental health perspectives, 122(9):970, 2014.

K. Anderson and K. Gaston. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3):138--146, 2013.

J. Bellvert, P. Zarco-Tejada, J. Girona, and E. Fereres. Mapping crop water stress index in a `pinot-noir' vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precision Agriculture, 15(4):361--376, 2014.

M. Brković and V. Sretović. Urban sensing--smart solutions for monitoring environmental quality: Case studies from serbia. In 48th ISOCARP International Congress: Fast Forward: Planning in a (hyper) dynamic urban context, Perm, Russia, 2012.

Y. Cheng, X. Li, Z. Li, S. Jiang, Y. Li, J. Jia, and X. Jiang. AirCloud: a cloud-based air-quality monitoring system for everyone. In Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, pages 251--265. ACM, 2014.

I. Colomina and P. Molina. Unmanned aerial systems for photogrammetry and remote sensing: A review. {ISPRS} Journal of Photogrammetry and Remote Sensing, 92:79--97, 2014.

T. Cox, C. Nagy, M. Skoog, and I. Somers. Civil UAV Capability Assessment. NASA, 2004.

R. P. Foundation. Raspberry pi. https://www.raspberrypi.org/, Accessed: October 10, 2015.

S. Gupte, P. Mohandas, and J. Conrad. A survey of quadrotor Unmanned Aerial Vehicles. In Southeastcon, 2012 Proceedings of IEEE, pages 1--6, March 2012.

S.-C. Hu, Y.-C. Wang, C.-Y. Huang, and Y.-C. Tseng. Measuring air quality in city areas by vehicular wireless sensor networks. Journal of Systems and Software, 84(11):2005--2012, 2011.

C. H. Hugenholtz, B. J. Moorman, K. Riddell, and K. Whitehead. Small unmanned aircraft systems for remote sensing and Earth science research. Eos, Transactions American Geophysical Union, 93(25):236--236, 2012.

S. Illingworth, G. Allen, C. Percival, P. Hollingsworth, M. Gallagher, H. Ricketts, H. Hayes, P. ÅĄadosz, D. Crawley, and G. Roberts. Measurement of boundary layer ozone concentrations on-board a Skywalker unmanned aerial vehicle. Atmospheric Science Letters, 15(4):252--258, 2014.

D. Industries. Grovepi. http://www.dexterindustries.com/grovepi/, Accessed: October 10, 2015.

L.-J. S. Liu and A. Rossini. Use of kriging models to predict 12-hour mean ozone concentrations in metropolitan toronto-a pilot study. Environment International, 22(6):677--692, 1996.

Q. McFrederick, J. Kathilankal, and J. Fuentes. Air pollution modifies floral scent trails. Atmospheric Environment, 42(10):2336--2348, 2008.

M. L. Stein. Statistical Interpolation of Spatial Data: Some Theory for Kriging. Springer, New York, 1999.

C. Zhang and J. Kovacs. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13(6):693--712, 2012.

[-]

This item appears in the following Collection(s)

Show full item record