2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 14 , Issue 2 , PP: 08-24, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ola Khudhair Abbas 1 * , Fairuz Abdullah 2 , Nurul Asyikin Mohamed Radzi 3 , Aymen Dawood Salman 4
Doi: https://doi.org/10.54216/JISIoT.140202
This study presents a new adaptive routing protocol for fire emergencies, leveraging a newly created dataset and a hybrid deep learning approach to optimize decision-making and data routing strategies. The developed protocol integrates a hybrid of Convolutional Neural Networks (CNNs) with Bi-Directional Long Short-Term Memory (BiLSTMs) deep learning models to predict fires at early stages, effectively managing the dynamic and unpredictable nature of fire emergencies to prevent data loss and ensure packet delivery to the base station. Exhaustive validation was conducted utilizing the standard protocol to ensure the reliability and effectiveness of the proposed approach. Experimental results demonstrate the superior performance of the proposed hybrid-deep learning model and the significant enhancements in routing efficiency and monitored data preservation for the developed protocol compared to the standard protocol. The findings are useful in providing a reliable solution for adaptive routing during emergencies.
Adaptive Routing Protocol , Hybrid Deep-Learning Model , Fire-Adaptive Dataset , Routing Failure , Network Segmentation , Data Loss
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