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Wireless sensor network for forest fire detection
dc.contributor.author | Varela, Noel | spa |
dc.contributor.author | Díaz-Martinez, Jorge L | spa |
dc.contributor.author | Ospino, Adalberto | spa |
dc.contributor.author | Lizardo Zelaya, Nelson Alberto | spa |
dc.date.accessioned | 2021-01-04T21:15:02Z | |
dc.date.available | 2021-01-04T21:15:02Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 1877-0509 | spa |
dc.identifier.uri | https://hdl.handle.net/11323/7652 | spa |
dc.description.abstract | Some methods for fire detection include monitoring from watch towers and the use of satellite images [1] [2]. Unfortunately, these are not efficient due to several reasons, such as high infrastructure costs (sophisticated equipment), the fact that they require a large number of trained personnel and that they make real-time monitoring difficult, since when the phenomenon is detected, its speed of propagation has produced uncontrollable levels of damage. This paper proposes a method for detecting forest fires, using a network of wireless sensors and information fusion methods. | spa |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | |
dc.publisher | Corporación Universidad de la Costa | spa |
dc.rights | CC0 1.0 Universal | spa |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | spa |
dc.source | Procedia Computer Science | spa |
dc.subject | Data analysis | spa |
dc.subject | Wireless sensor network | spa |
dc.subject | Forest fire detection | spa |
dc.title | Wireless sensor network for forest fire detection | spa |
dc.type | Artículo de revista | spa |
dc.source.url | https://www.sciencedirect.com/science/article/pii/S1877050920317427 | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.identifier.doi | https://doi.org/10.1016/j.procs.2020.07.061 | spa |
dc.identifier.instname | Corporación Universidad de la Costa | spa |
dc.identifier.reponame | REDICUC - Repositorio CUC | spa |
dc.identifier.repourl | https://repositorio.cuc.edu.co/ | spa |
dc.relation.references | [1] Noureddine, H., & Bouabdellah, K. (2020). Field Experiment Testbed for Forest Fire Detection using Wireless Multimedia Sensor Network. International Journal of Sensors Wireless Communications and Control, 10(1), 3-14. | spa |
dc.relation.references | [2] Grover, K., Kahali, D., Verma, S., & Subramanian, B. (2020). WSN-Based System for Forest Fire Detection and Mitigation. In Emerging Technologies for Agriculture and Environment (pp. 249-260). Springer, Singapore. | spa |
dc.relation.references | [3] Chauhan, A., Semwal, S., & Chawhan, R. (2013, December). Artificial neural network-based forest fire detection system using wireless sensor network. In 2013 Annual IEEE India Conference (INDICON) (pp. 1-6). IEEE. | spa |
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dc.relation.references | [5]Nugroho, A. A., Iwan, I., Azizah, K. I. N., & Raswa, F. H. (2019). Peatland Forest Fire Prevention Using Wireless Sensor Network Based on Naïve Bayes Classifier. KnE Social Sciences, 20-34. | spa |
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dc.relation.references | [8] Zhang, J., Li, W., Yin, Z., Liu, S., & Guo, X. (2009, May). Forest fire detection system based on wireless sensor network. In 2009 4th IEEE conference on industrial electronics and applications (pp. 520-523). IEEE. | spa |
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dc.relation.references | [12] Saidi, H., Gretete, D., & Addaim, A. (2020). Game Theory for Wireless Sensor Network Security. In Fourth International Congress on Information and Communication Technology (pp. 259-269). Springer, Singapore. | spa |
dc.relation.references | [13] Aliady, W. A., & Al-Ahmadi, S. A. (2019). Energy Preserving Secure Measure Against Wormhole Attack in Wireless Sensor Networks. IEEE Access, 7, 84132-84141. | spa |
dc.relation.references | [14] Viloria, A., Hernandez-P, H., Lezama, O. B. P., & Orozco, V. D. (2020). Electric Consumption Pattern from Big Data (pp. 479–485). https://doi.org/10.1007/978-981-15-3125-5_47. | spa |
dc.relation.references | [15] Sanchez, L., Vásquez, C., Viloria, A., & Cmeza-Estrada. (2018). Conglomerates of Latin American countries and public policies for the sustainable development of the electric power generation sector. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10943 LNCS, pp. 759–766). Springer Verlag. https://doi.org/10.1007/978-3- 319-93803-5_71 | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |
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