Wireless sensor network for forest fire detection
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...
- Autores:
-
Varela, Noel
Díaz-Martinez, Jorge L
Ospino, Adalberto
Lizardo Zelaya, Nelson Alberto
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7652
- Acceso en línea:
- https://hdl.handle.net/11323/7652
https://doi.org/10.1016/j.procs.2020.07.061
https://repositorio.cuc.edu.co/
- Palabra clave:
- Data analysis
Wireless sensor network
Forest fire detection
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Wireless sensor network for forest fire detection |
| title |
Wireless sensor network for forest fire detection |
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Wireless sensor network for forest fire detection Data analysis Wireless sensor network Forest fire detection |
| title_short |
Wireless sensor network for forest fire detection |
| title_full |
Wireless sensor network for forest fire detection |
| title_fullStr |
Wireless sensor network for forest fire detection |
| title_full_unstemmed |
Wireless sensor network for forest fire detection |
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Wireless sensor network for forest fire detection |
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Varela, Noel Díaz-Martinez, Jorge L Ospino, Adalberto Lizardo Zelaya, Nelson Alberto |
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Varela, Noel Díaz-Martinez, Jorge L Ospino, Adalberto Lizardo Zelaya, Nelson Alberto |
| dc.subject.spa.fl_str_mv |
Data analysis Wireless sensor network Forest fire detection |
| topic |
Data analysis Wireless sensor network Forest fire detection |
| description |
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. |
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2020 |
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2020 |
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2021-01-04T21:15:02Z |
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2021-01-04T21:15:02Z |
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https://hdl.handle.net/11323/7652 |
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https://doi.org/10.1016/j.procs.2020.07.061 |
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Corporación Universidad de la Costa |
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eng |
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eng |
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[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. [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. [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. [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. [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. [6]Biswas, P., & Samanta, T. (2020). True Event-Driven and Fault-Tolerant Routing in Wireless Sensor Network. Wireless Personal Communications, 1-23. [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. [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. [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. [10] Hariyawan, M. Y., Gunawan, A., & Putra, E. H. (2013). Wireless sensor network for forest fire detection. Telkomnika, 11(3), 563. [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. [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. [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. [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. [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 |
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Varela, NoelDíaz-Martinez, Jorge LOspino, AdalbertoLizardo Zelaya, Nelson Alberto2021-01-04T21:15:02Z2021-01-04T21:15:02Z20201877-0509https://hdl.handle.net/11323/7652https://doi.org/10.1016/j.procs.2020.07.061Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Varela, NoelDíaz-Martinez, Jorge LOspino, Adalberto-will be generated-orcid-0000-0003-1466-0424-600Lizardo Zelaya, Nelson Alberto-will be generated-orcid-0000-0002-3963-5690-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920317427Data analysisWireless sensor networkForest fire detectionWireless sensor network for forest fire detectionArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[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.[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.[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.[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.[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.[6]Biswas, P., & Samanta, T. (2020). True Event-Driven and Fault-Tolerant Routing in Wireless Sensor Network. Wireless Personal Communications, 1-23.[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.[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.[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.[10] Hariyawan, M. Y., Gunawan, A., & Putra, E. H. (2013). Wireless sensor network for forest fire detection. Telkomnika, 11(3), 563.[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.[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.[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.[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.[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). 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