CCSW‐RLNC: A Deep Learning‐Enhanced Color‐Coded Sliding Window Approach for Efficient Network Coding.
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| Title: | CCSW‐RLNC: A Deep Learning‐Enhanced Color‐Coded Sliding Window Approach for Efficient Network Coding. |
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| Authors: | Akilandeswary, G.1 (AUTHOR) akilandeswaryg@stjosephstechnology.ac.in, Manickam, J. Martin Leo2 (AUTHOR) |
| Source: | International Journal of Communication Systems. Jun2026, Vol. 39 Issue 9, p1-12. 12p. |
| Subjects: | Linear network coding, Deep learning, Network performance, Mathematical optimization, Adaptive routing (Computer network management), Data integrity |
| Abstract: | This paper focuses on developing an innovative Color‐Coded Sliding Window Random Linear Network Coding (CCSW‐RLNC) strategy, where the sliding window size dynamically adapts based on network conditions, including intermediate node availability, power limitations, channel noise, and multihop transmission characteristics. The system features a special two‐direction mechanism, where data windows are moved from left to right as color priority is shifted from right to left. The central color has the highest weightage to ensure data integrity. The enhancement of this approach is through a deep learning model, which optimizes the window parameters and adaptation process. The primary targets are to enhance energy efficiency and maximize throughput through the optimal synchronization of encoder and decoder and to achieve minimum latency. The color‐coding scheme serves as both a data representation system and a priority management system, enabling the more accurate reconstruction of original data packets. This new hybrid, combining RLNC, color‐coding, and deep learning adaptability addresses realistic network limitations while enhancing system performance and overall reliability. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Communication Systems is the property of Wiley-Blackwell 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193690607 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: CCSW‐RLNC: A Deep Learning‐Enhanced Color‐Coded Sliding Window Approach for Efficient Network Coding. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Akilandeswary%2C+G%2E%22">Akilandeswary, G.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> akilandeswaryg@stjosephstechnology.ac.in</i><br /><searchLink fieldCode="AR" term="%22Manickam%2C+J%2E+Martin+Leo%22">Manickam, J. Martin Leo</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Communication+Systems%22">International Journal of Communication Systems</searchLink>. Jun2026, Vol. 39 Issue 9, p1-12. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Linear+network+coding%22">Linear network coding</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Network+performance%22">Network performance</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+routing+%28Computer+network+management%29%22">Adaptive routing (Computer network management)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+integrity%22">Data integrity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper focuses on developing an innovative Color‐Coded Sliding Window Random Linear Network Coding (CCSW‐RLNC) strategy, where the sliding window size dynamically adapts based on network conditions, including intermediate node availability, power limitations, channel noise, and multihop transmission characteristics. The system features a special two‐direction mechanism, where data windows are moved from left to right as color priority is shifted from right to left. The central color has the highest weightage to ensure data integrity. The enhancement of this approach is through a deep learning model, which optimizes the window parameters and adaptation process. The primary targets are to enhance energy efficiency and maximize throughput through the optimal synchronization of encoder and decoder and to achieve minimum latency. The color‐coding scheme serves as both a data representation system and a priority management system, enabling the more accurate reconstruction of original data packets. This new hybrid, combining RLNC, color‐coding, and deep learning adaptability addresses realistic network limitations while enhancing system performance and overall reliability. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Communication Systems is the property of Wiley-Blackwell 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: 10.1002/dac.70516 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Linear network coding Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Network performance Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Adaptive routing (Computer network management) Type: general – SubjectFull: Data integrity Type: general Titles: – TitleFull: CCSW‐RLNC: A Deep Learning‐Enhanced Color‐Coded Sliding Window Approach for Efficient Network Coding. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Akilandeswary, G. – PersonEntity: Name: NameFull: Manickam, J. Martin Leo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10745351 Numbering: – Type: volume Value: 39 – Type: issue Value: 9 Titles: – TitleFull: International Journal of Communication Systems Type: main |
| ResultId | 1 |
