The preservation of the natural ecosystem is a topical issue that is receiving increasing attention not only from the scientific community but from the entire world population. Forests and woodlands are the main actors responsible for mitigating climate change, able to absorb significant amounts of carbon dioxide. The preservation of the arboreal areas has been addressed through the adoption of various solutions. This paper proposes a new real-time fire monitoring and detection system based on Digital Mobile Radio (DMR) nodes and a Social Internet of Things (SIoT) platform on which artificial intelligence algorithms have been implemented. The results obtained show the ability to detect the slightest variation in the observed parameters, determining the direction and speed of fire propagation.

Pettorru, G., Fadda, M., Girau, R., Anedda, M., Giusto, D. (2023). An IoT-based electronic sniffing for forest fire detection. New York : IEEE [10.1109/ICCE56470.2023.10043411].

An IoT-based electronic sniffing for forest fire detection

Girau, R
;
2023

Abstract

The preservation of the natural ecosystem is a topical issue that is receiving increasing attention not only from the scientific community but from the entire world population. Forests and woodlands are the main actors responsible for mitigating climate change, able to absorb significant amounts of carbon dioxide. The preservation of the arboreal areas has been addressed through the adoption of various solutions. This paper proposes a new real-time fire monitoring and detection system based on Digital Mobile Radio (DMR) nodes and a Social Internet of Things (SIoT) platform on which artificial intelligence algorithms have been implemented. The results obtained show the ability to detect the slightest variation in the observed parameters, determining the direction and speed of fire propagation.
2023
2023 IEEE International Conference on Consumer Electronics (ICCE)
1
5
Pettorru, G., Fadda, M., Girau, R., Anedda, M., Giusto, D. (2023). An IoT-based electronic sniffing for forest fire detection. New York : IEEE [10.1109/ICCE56470.2023.10043411].
Pettorru, G; Fadda, M; Girau, R; Anedda, M; Giusto, D
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/942497
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