Deep learning solution for central-node integration challenge in clustered routing protocols during fire emergencies.
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| Title: | Deep learning solution for central-node integration challenge in clustered routing protocols during fire emergencies. |
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| Authors: | Abbas, Ola Khudhair1,2 (AUTHOR) ulakhudeer8@gmail.com, Abdullah, Fairuz1,3 (AUTHOR) fairuz@uniten.edu.my, Radzi, Nurul Asyikin Mohamed1,3 (AUTHOR) asyikin@uniten.edu.my, Salman, Aymen Dawood4 (AUTHOR) aymen.d.salman@uotechnology.edu.iq |
| Source: | EURASIP Journal on Wireless Communications & Networking. 12/9/2025, Vol. 2025 Issue 1, p1-38. 38p. |
| Subjects: | Deep learning, Adaptive routing (Computer network management), Convolutional neural networks, Network performance, Long short-term memory, Fire prevention, Network routing protocols |
| Abstract: | Many clustered routing protocols integrate mediator nodes within the network structure, such as relays, gateways, and forwarders. These nodes collect and aggregate data from the clusters and then submit the data to the Base Station (BS), facilitating communication between clusters. It eliminates the redundancy in data and minimizes the delay by reducing the transmission distance to the BS. However, integrating such nodes poses significant challenges in dynamic environments, such as fire incidents. Based on their location and responsibility, their loss often leads to increased packet loss and suboptimal routing decisions under changing network conditions. This paper proposes a solution to this challenge by introducing a novel adaptive routing protocol. A hybrid deep learning model is employed to predict the loss of such nodes and select a suitable alternative node, combining dual-layer convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTMs). The proposed protocol was validated against the non-adaptive approach during three scenarios and two fire directions. Key evaluation metrics used are the throughput, packet delivery ratio (PDR), packet loss ratio (PLR), delay optimization, and packet prioritization ratio. Simulation results demonstrate the effectiveness of the proposed approach in enhancing the central node's performance during fire incidents and ensuring reliable delivery of monitored data to its destination. For the first fire scenario, the proposed approach records 8.08% PLR and 91.91% PDR, while for the second fire scenario, it records 8.27% PLR and 91.27% PDR. This study provides a robust framework for central node-based and clustered protocols, ensuring reliable communication in critical scenarios. [ABSTRACT FROM AUTHOR] |
| Copyright of EURASIP Journal on Wireless Communications & Networking is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 189912144 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Deep learning solution for central-node integration challenge in clustered routing protocols during fire emergencies. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Abbas%2C+Ola+Khudhair%22">Abbas, Ola Khudhair</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> ulakhudeer8@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Abdullah%2C+Fairuz%22">Abdullah, Fairuz</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> fairuz@uniten.edu.my</i><br /><searchLink fieldCode="AR" term="%22Radzi%2C+Nurul+Asyikin+Mohamed%22">Radzi, Nurul Asyikin Mohamed</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> asyikin@uniten.edu.my</i><br /><searchLink fieldCode="AR" term="%22Salman%2C+Aymen+Dawood%22">Salman, Aymen Dawood</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> aymen.d.salman@uotechnology.edu.iq</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22EURASIP+Journal+on+Wireless+Communications+%26+Networking%22">EURASIP Journal on Wireless Communications & Networking</searchLink>. 12/9/2025, Vol. 2025 Issue 1, p1-38. 38p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+routing+%28Computer+network+management%29%22">Adaptive routing (Computer network management)</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Network+performance%22">Network performance</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Fire+prevention%22">Fire prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Network+routing+protocols%22">Network routing protocols</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Many clustered routing protocols integrate mediator nodes within the network structure, such as relays, gateways, and forwarders. These nodes collect and aggregate data from the clusters and then submit the data to the Base Station (BS), facilitating communication between clusters. It eliminates the redundancy in data and minimizes the delay by reducing the transmission distance to the BS. However, integrating such nodes poses significant challenges in dynamic environments, such as fire incidents. Based on their location and responsibility, their loss often leads to increased packet loss and suboptimal routing decisions under changing network conditions. This paper proposes a solution to this challenge by introducing a novel adaptive routing protocol. A hybrid deep learning model is employed to predict the loss of such nodes and select a suitable alternative node, combining dual-layer convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTMs). The proposed protocol was validated against the non-adaptive approach during three scenarios and two fire directions. Key evaluation metrics used are the throughput, packet delivery ratio (PDR), packet loss ratio (PLR), delay optimization, and packet prioritization ratio. Simulation results demonstrate the effectiveness of the proposed approach in enhancing the central node's performance during fire incidents and ensuring reliable delivery of monitored data to its destination. For the first fire scenario, the proposed approach records 8.08% PLR and 91.91% PDR, while for the second fire scenario, it records 8.27% PLR and 91.27% PDR. This study provides a robust framework for central node-based and clustered protocols, ensuring reliable communication in critical scenarios. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of EURASIP Journal on Wireless Communications & Networking is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s13638-025-02538-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 38 StartPage: 1 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Adaptive routing (Computer network management) Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Network performance Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Fire prevention Type: general – SubjectFull: Network routing protocols Type: general Titles: – TitleFull: Deep learning solution for central-node integration challenge in clustered routing protocols during fire emergencies. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Abbas, Ola Khudhair – PersonEntity: Name: NameFull: Abdullah, Fairuz – PersonEntity: Name: NameFull: Radzi, Nurul Asyikin Mohamed – PersonEntity: Name: NameFull: Salman, Aymen Dawood IsPartOfRelationships: – BibEntity: Dates: – D: 09 M: 12 Text: 12/9/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 16871472 Numbering: – Type: volume Value: 2025 – Type: issue Value: 1 Titles: – TitleFull: EURASIP Journal on Wireless Communications & Networking Type: main |
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