Design and Implementation of IoT-Enabled Intelligent Fire Detection System Using Neural Networks
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Science and Business Media Deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Fire detection systems are considered an integral part of any building. However, most fire detection systems use a single passive sensor that usually faces some unavoidable problems, especially with the use of simple processing systems using threshold and trend algorithms. Although more than a single sensor and fire information are used in some existing systems, the real-time fire information and firefighting forecasting are not monitored. Such information facilitates good decision making in firefighting and rescue operations. This paper develops a fast and smart fire detection and monitoring system that can detect and monitor fire incidents with low probability of detection error. The system involves IoT sensors that detect all necessary fire information including heating release rate smoke level, and CO2 level. Moreover, a fire detection and monitoring model based on artificial neural networks is developed to identify fire information in real-time. The proposed system was tested in a chamber box with around 20 experiments. The positive fire detection rate was high with fast fire detection rate. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Açıklama
12th International Conference on AI and Mobile Services, AIMS 2023 -- 23 September 2023 through 26 September 2023 -- Hawaii -- 302359
Anahtar Kelimeler
Building fire, Deep learning, Fire alarm, Internet of Things, Smart firefighting
Kaynak
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
14202 LNCS
