New Adaptive-Clustered Routing Protocol for Indoor Fire Emergencies Using Hybrid CNN-BiLSTM Model: Development and Validation

Ola Khudhair; Fairuz; Nurul Asyikin Mohamed; Aymen Dawood

doi:https://doi.org/10.54216/JISIoT.140202

Full Length Article

Volume 14 • Issue 2 • PP: 08-24 • 2025

New Adaptive-Clustered Routing Protocol for Indoor Fire Emergencies Using Hybrid CNN-BiLSTM Model: Development and Validation

Ola Khudhair Abbas ^1*

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,

Fairuz Abdullah ²

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,

Nurul Asyikin Mohamed Radzi ²

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,

Aymen Dawood Salman ³

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¹Institute of Power Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia

²Institute of Power Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia; Department of Electrical and Electronics Engineering, College of Engineering,

³Department of Computer Engineering, University of Technology, Industry Street, Baghdad, Iraq

* Corresponding Author.

DOI https://doi.org/10.54216/JISIoT.140202

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Received: March 06, 2024 Revised: June 12, 2024 Accepted: October 03, 2024

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Abstract

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.

Keywords

Adaptive Routing Protocol Hybrid Deep-Learning Model Fire-Adaptive Dataset Routing Failure Network Segmentation Data Loss

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Abbas, Ola Khudhair, Abdullah, Fairuz, Radzi, Nurul Asyikin Mohamed, Salman, Aymen Dawood. "New Adaptive-Clustered Routing Protocol for Indoor Fire Emergencies Using Hybrid CNN-BiLSTM Model: Development and Validation." Journal of Intelligent Systems and Internet of Things, vol. Volume 14, no. Issue 2, 2025, pp. 08-24. DOI: https://doi.org/10.54216/JISIoT.140202

Abbas, O., Abdullah, F., Radzi, N., Salman, A. (2025). New Adaptive-Clustered Routing Protocol for Indoor Fire Emergencies Using Hybrid CNN-BiLSTM Model: Development and Validation. Journal of Intelligent Systems and Internet of Things, Volume 14(Issue 2), 08-24. DOI: https://doi.org/10.54216/JISIoT.140202

Abbas, Ola Khudhair, Abdullah, Fairuz, Radzi, Nurul Asyikin Mohamed, Salman, Aymen Dawood. "New Adaptive-Clustered Routing Protocol for Indoor Fire Emergencies Using Hybrid CNN-BiLSTM Model: Development and Validation." Journal of Intelligent Systems and Internet of Things Volume 14, no. Issue 2 (2025): 08-24. DOI: https://doi.org/10.54216/JISIoT.140202

Abbas, O., Abdullah, F., Radzi, N., Salman, A. (2025) 'New Adaptive-Clustered Routing Protocol for Indoor Fire Emergencies Using Hybrid CNN-BiLSTM Model: Development and Validation', Journal of Intelligent Systems and Internet of Things, Volume 14(Issue 2), pp. 08-24. DOI: https://doi.org/10.54216/JISIoT.140202

Abbas O, Abdullah F, Radzi N, Salman A. New Adaptive-Clustered Routing Protocol for Indoor Fire Emergencies Using Hybrid CNN-BiLSTM Model: Development and Validation. Journal of Intelligent Systems and Internet of Things. 2025;Volume 14(Issue 2):08-24. DOI: https://doi.org/10.54216/JISIoT.140202

O. Abbas, F. Abdullah, N. Radzi, A. Salman, "New Adaptive-Clustered Routing Protocol for Indoor Fire Emergencies Using Hybrid CNN-BiLSTM Model: Development and Validation," Journal of Intelligent Systems and Internet of Things, vol. Volume 14, no. Issue 2, pp. 08-24, 2025. DOI: https://doi.org/10.54216/JISIoT.140202

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