Almohammedi, A.A.Balfaqih, M.Nahas, S.Bokhari, A.Alqudsi, A.2024-09-292024-09-292023978-303145139-30302-9743https://doi.org/10.1007/978-3-031-45140-9_6https://hdl.handle.net/20.500.14619/966212th International Conference on AI and Mobile Services, AIMS 2023 -- 23 September 2023 through 26 September 2023 -- Hawaii -- 302359Fire 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.eninfo:eu-repo/semantics/closedAccessBuilding fireDeep learningFire alarmInternet of ThingsSmart firefightingConference Object10.1007/978-3-031-45140-9_62-s2.0-8517458797270Q36314202 LNCS