Deep Learning‐Based Passive Localization System Using Radio Tomography Images and Wi‐Fi RSSI.
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| Title: | Deep Learning‐Based Passive Localization System Using Radio Tomography Images and Wi‐Fi RSSI. |
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| Authors: | Jabbar, Muhammad1 (AUTHOR) m.jabbar@uog.edu.pk, Shoaib, Umar1 (AUTHOR), Zakaria, Khiati Abdel Ilah2 (AUTHOR), Lazaro, Antonio (AUTHOR) antonioramon.lazaro@urv.cat |
| Source: | International Journal of Distributed Sensor Networks. 6/15/2026, Vol. 2026, p1-17. 17p. |
| Subjects: | Wireless localization, Deep learning, Tomography, Convolutional neural networks, Internet of things, Wireless mesh networks |
| Abstract: | Passive localization is an important technology within Internet of Things (IoT) applications that allows for tracking and monitoring of people without needing active transmitters. This feature proves useful in privacy‐conscious areas like security and healthcare, where active tracking or wearable devices are not practicable. Traditional radio tomographic imaging (RTI) methods suffer limitations due to vulnerability to environmental noise and rough spatial resolution. To overcome these issues, we suggest a new framework that combines a wireless mesh of 14 ESP32‐transceiver nodes, multipath‐sensitive RTI modeling, and a deep convolutional neural network (CNN) for accurate localization. The system records bidirectional received signal strength indicator (RSSI) measures to produce RTIs, which are then optimized by CNN to provide submeter accuracy. Experimental tests illustrate a precision of 92.81%, outperforming traditional RTI methods and other machine learning baselines. The suggested solution successfully alleviates line‐of‐sight (LOS) and nonline‐of‐sight (NLOS) problems, ensuring scalability and cost‐effectiveness for real‐world IoT applications. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Distributed Sensor Networks 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: 194609711 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Deep Learning‐Based Passive Localization System Using Radio Tomography Images and Wi‐Fi RSSI. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jabbar%2C+Muhammad%22">Jabbar, Muhammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> m.jabbar@uog.edu.pk</i><br /><searchLink fieldCode="AR" term="%22Shoaib%2C+Umar%22">Shoaib, Umar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zakaria%2C+Khiati+Abdel+Ilah%22">Zakaria, Khiati Abdel Ilah</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lazaro%2C+Antonio%22">Lazaro, Antonio</searchLink> (AUTHOR)<i> antonioramon.lazaro@urv.cat</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Distributed+Sensor+Networks%22">International Journal of Distributed Sensor Networks</searchLink>. 6/15/2026, Vol. 2026, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Wireless+localization%22">Wireless localization</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Tomography%22">Tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+mesh+networks%22">Wireless mesh networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Passive localization is an important technology within Internet of Things (IoT) applications that allows for tracking and monitoring of people without needing active transmitters. This feature proves useful in privacy‐conscious areas like security and healthcare, where active tracking or wearable devices are not practicable. Traditional radio tomographic imaging (RTI) methods suffer limitations due to vulnerability to environmental noise and rough spatial resolution. To overcome these issues, we suggest a new framework that combines a wireless mesh of 14 ESP32‐transceiver nodes, multipath‐sensitive RTI modeling, and a deep convolutional neural network (CNN) for accurate localization. The system records bidirectional received signal strength indicator (RSSI) measures to produce RTIs, which are then optimized by CNN to provide submeter accuracy. Experimental tests illustrate a precision of 92.81%, outperforming traditional RTI methods and other machine learning baselines. The suggested solution successfully alleviates line‐of‐sight (LOS) and nonline‐of‐sight (NLOS) problems, ensuring scalability and cost‐effectiveness for real‐world IoT applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Distributed Sensor Networks 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: 10.1155/dsn/1877891 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Wireless localization Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Tomography Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Internet of things Type: general – SubjectFull: Wireless mesh networks Type: general Titles: – TitleFull: Deep Learning‐Based Passive Localization System Using Radio Tomography Images and Wi‐Fi RSSI. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jabbar, Muhammad – PersonEntity: Name: NameFull: Shoaib, Umar – PersonEntity: Name: NameFull: Zakaria, Khiati Abdel Ilah – PersonEntity: Name: NameFull: Lazaro, Antonio IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: 6/15/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15501329 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: International Journal of Distributed Sensor Networks Type: main |
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