Advancements in physical layer key generation: a review on channel reciprocity and IoT security techniques.
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| Title: | Advancements in physical layer key generation: a review on channel reciprocity and IoT security techniques. |
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| Authors: | Shah, Syed Shafaq Ali1, Noor, Ajab2 ajabnoor051@gmail.com, Liang, Ruiyue3, Zadran, Rahmat Ullah4 |
| Source: | Telkomnika. Feb2026, Vol. 24 Issue 1, p196-205. 10p. |
| Subjects: | Internet of things, Physical layer security, Deep learning, Artificial neural networks, Millimeter wave devices, Machine learning |
| Abstract: | With the burgeoning internet of things (IoT), securing communication becomes paramount. Traditional cryptography does not meet computational needs and brute-force attacks. This review explores the state-of-the-art physical layer secret key generation (PLKG) that takes advantage of the inherent reciprocity and randomness of wireless channels. We investigate cutting-edge techniques such as feature extraction networks, domain-adversarial training, and deep learning-based approaches, evaluating their effects on the security and efficiency of key generation. In addition to these methods, the review addresses real-world challenges such as multi-user scenarios, reconciliation overhead, and inconsistent channel measurement. We believe that improved key generation rates and security can be achieved through the use of millimeter wave technology and full-duplex communication. To strengthen the robustness of key generation, the paper concludes by suggesting future directions, such as incorporating more random sources, such as physiological signals and sensor data. This comprehensive overview offers deep insights into the state-of-the-art and paves the way for reliable communication in ever more complicated IoT settings. [ABSTRACT FROM AUTHOR] |
| Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192065411 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Advancements in physical layer key generation: a review on channel reciprocity and IoT security techniques. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shah%2C+Syed+Shafaq+Ali%22">Shah, Syed Shafaq Ali</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Noor%2C+Ajab%22">Noor, Ajab</searchLink><relatesTo>2</relatesTo><i> ajabnoor051@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Liang%2C+Ruiyue%22">Liang, Ruiyue</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Zadran%2C+Rahmat+Ullah%22">Zadran, Rahmat Ullah</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Telkomnika%22">Telkomnika</searchLink>. Feb2026, Vol. 24 Issue 1, p196-205. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+layer+security%22">Physical layer security</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Millimeter+wave+devices%22">Millimeter wave devices</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the burgeoning internet of things (IoT), securing communication becomes paramount. Traditional cryptography does not meet computational needs and brute-force attacks. This review explores the state-of-the-art physical layer secret key generation (PLKG) that takes advantage of the inherent reciprocity and randomness of wireless channels. We investigate cutting-edge techniques such as feature extraction networks, domain-adversarial training, and deep learning-based approaches, evaluating their effects on the security and efficiency of key generation. In addition to these methods, the review addresses real-world challenges such as multi-user scenarios, reconciliation overhead, and inconsistent channel measurement. We believe that improved key generation rates and security can be achieved through the use of millimeter wave technology and full-duplex communication. To strengthen the robustness of key generation, the paper concludes by suggesting future directions, such as incorporating more random sources, such as physiological signals and sensor data. This comprehensive overview offers deep insights into the state-of-the-art and paves the way for reliable communication in ever more complicated IoT settings. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University 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: 10.12928/TELKOMNIKA.v24i1.27340 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 196 Subjects: – SubjectFull: Internet of things Type: general – SubjectFull: Physical layer security Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Millimeter wave devices Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Advancements in physical layer key generation: a review on channel reciprocity and IoT security techniques. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shah, Syed Shafaq Ali – PersonEntity: Name: NameFull: Noor, Ajab – PersonEntity: Name: NameFull: Liang, Ruiyue – PersonEntity: Name: NameFull: Zadran, Rahmat Ullah IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16936930 Numbering: – Type: volume Value: 24 – Type: issue Value: 1 Titles: – TitleFull: Telkomnika Type: main |
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