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dc.creatorVarela, Noel
dc.creatorDíaz-Martinez, Jorge L
dc.creatorOspino, Adalberto
dc.creatorLizardo Zelaya, Nelson Alberto
dc.date.accessioned2021-01-04T21:15:02Z
dc.date.available2021-01-04T21:15:02Z
dc.date.issued2020
dc.identifier.issn1877-0509
dc.identifier.urihttps://hdl.handle.net/11323/7652
dc.description.abstractSome 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.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceProcedia Computer Sciencespa
dc.subjectData analysisspa
dc.subjectWireless sensor networkspa
dc.subjectForest fire detectionspa
dc.titleWireless sensor network for forest fire detectionspa
dc.typearticlespa
dcterms.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
dcterms.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
dcterms.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
dcterms.references[4] Ghugar, U., & Pradhan, J. (2020). ML-IDS: MAC Layer Trust-Based Intrusion Detection System for Wireless Sensor Networks. In Computational Intelligence in Data Mining (pp. 427-434). Springer, Singapore.spa
dcterms.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
dcterms.references[6]Biswas, P., & Samanta, T. (2020). True Event-Driven and Fault-Tolerant Routing in Wireless Sensor Network. Wireless Personal Communications, 1-23.spa
dcterms.references[7] Dubey, V., Kumar, P., & Chauhan, N. (2019). Forest fire detection system using IoT and artificial neural network. In International Conference on Innovative Computing and Communications (pp. 323-337). Springer, Singapore.spa
dcterms.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
dcterms.references[9] 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
dcterms.references[10] Hariyawan, M. Y., Gunawan, A., & Putra, E. H. (2013). Wireless sensor network for forest fire detection. Telkomnika, 11(3), 563.spa
dcterms.references[11] Mohapatra, S., & Khilar, P. M. (2020). Fault Diagnosis in Wireless Sensor Network Using Self/Non-self-Discrimination Principle. In Progress in Computing, Analytics and Networking (pp. 161-168). Springer, Singapore.spa
dcterms.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
dcterms.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
dcterms.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
dcterms.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_71spa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S1877050920317427spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.07.061


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