Variational Quantum Eigensolver‐Driven Cut‐Vertex Analysis for Secure IoT Wireless Sensor Network Monitoring.
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| Title: | Variational Quantum Eigensolver‐Driven Cut‐Vertex Analysis for Secure IoT Wireless Sensor Network Monitoring. |
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| Authors: | Matheswaran, Saravanan1 (AUTHOR) msaravanandr@veltech.edu.in, Pichamuthu, Rajaram2 (AUTHOR) rajaramp.sse@saveetha.com, Srinivasan, Karthik3 (AUTHOR) k.parimala@seu.edu.sa, Sengodan, Prabaharan4 (AUTHOR) s.prabaharan@galgotiasuniversity.edu.in |
| Source: | International Journal of Communication Systems. Jun2026, Vol. 39 Issue 9, p1-20. 20p. |
| Subjects: | Quantum computing, Wireless sensor network security, Intrusion detection systems (Computer security), Graph connectivity, Particle swarm optimization, Wireless sensor networks |
| Abstract: | In recent times, secure and energy‐efficient link monitoring in wireless sensor networks plays a significant role in facilitating reliable communication between Internet of Things applications. The classical articulation‐point detection methods efficiently identified the structural vulnerabilities but do not incorporate a node level attributes. This research proposes a novel variational quantum eigensolver–based decision tree for secure and energy‐efficient link monitoring in the Internet of Things towards wireless sensor networks by reformulating the structural monitoring as a weighted connectivity optimization problem. Based on the quantum computation, the variational quantum eigensolver is designed as a quantum‐assisted optimizer within localized subgraphs to evaluate weighted connectivity degradation. The decision tree is applied to predict real‐time intrusion by using the cut vertices and provides efficient and interpretable identification of malicious activity. In addition, the particle swarm optimization with dynamic opposition strategy is designed for selecting the monitoring nodes under connectivity constraints, which ensures full coverage as well as connectivity. The experimental validation signifies that the variational quantum eigensolver–based decision tree scheme achieves a superior accuracy of 98.3% and lower energy consumption of 73.6 J at the 250th node. As demonstrated by comparative and statistical analysis, the efficiency, scalability, and security of the proposed framework are superior to the conventional methods, supporting real‐time, resource‐limited Internet of Things–wireless sensor network settings. [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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193690604 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Variational Quantum Eigensolver‐Driven Cut‐Vertex Analysis for Secure IoT Wireless Sensor Network Monitoring. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Matheswaran%2C+Saravanan%22">Matheswaran, Saravanan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> msaravanandr@veltech.edu.in</i><br /><searchLink fieldCode="AR" term="%22Pichamuthu%2C+Rajaram%22">Pichamuthu, Rajaram</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> rajaramp.sse@saveetha.com</i><br /><searchLink fieldCode="AR" term="%22Srinivasan%2C+Karthik%22">Srinivasan, Karthik</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> k.parimala@seu.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Sengodan%2C+Prabaharan%22">Sengodan, Prabaharan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> s.prabaharan@galgotiasuniversity.edu.in</i> – 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, p1-20. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Quantum+computing%22">Quantum computing</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+sensor+network+security%22">Wireless sensor network security</searchLink><br /><searchLink fieldCode="DE" term="%22Intrusion+detection+systems+%28Computer+security%29%22">Intrusion detection systems (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+connectivity%22">Graph connectivity</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+sensor+networks%22">Wireless sensor networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In recent times, secure and energy‐efficient link monitoring in wireless sensor networks plays a significant role in facilitating reliable communication between Internet of Things applications. The classical articulation‐point detection methods efficiently identified the structural vulnerabilities but do not incorporate a node level attributes. This research proposes a novel variational quantum eigensolver–based decision tree for secure and energy‐efficient link monitoring in the Internet of Things towards wireless sensor networks by reformulating the structural monitoring as a weighted connectivity optimization problem. Based on the quantum computation, the variational quantum eigensolver is designed as a quantum‐assisted optimizer within localized subgraphs to evaluate weighted connectivity degradation. The decision tree is applied to predict real‐time intrusion by using the cut vertices and provides efficient and interpretable identification of malicious activity. In addition, the particle swarm optimization with dynamic opposition strategy is designed for selecting the monitoring nodes under connectivity constraints, which ensures full coverage as well as connectivity. The experimental validation signifies that the variational quantum eigensolver–based decision tree scheme achieves a superior accuracy of 98.3% and lower energy consumption of 73.6 J at the 250th node. As demonstrated by comparative and statistical analysis, the efficiency, scalability, and security of the proposed framework are superior to the conventional methods, supporting real‐time, resource‐limited Internet of Things–wireless sensor network settings. [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.70513 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1 Subjects: – SubjectFull: Quantum computing Type: general – SubjectFull: Wireless sensor network security Type: general – SubjectFull: Intrusion detection systems (Computer security) Type: general – SubjectFull: Graph connectivity Type: general – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Wireless sensor networks Type: general Titles: – TitleFull: Variational Quantum Eigensolver‐Driven Cut‐Vertex Analysis for Secure IoT Wireless Sensor Network Monitoring. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Matheswaran, Saravanan – PersonEntity: Name: NameFull: Pichamuthu, Rajaram – PersonEntity: Name: NameFull: Srinivasan, Karthik – PersonEntity: Name: NameFull: Sengodan, Prabaharan 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 |
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