iScene: An interpretable framework with hierarchical edge services for scene risk identification in 6G internet of vehicles.
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| Title: | iScene: An interpretable framework with hierarchical edge services for scene risk identification in 6G internet of vehicles. |
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| Authors: | Zhong, Wuchang1 (AUTHOR), Wang, Siming1 (AUTHOR), Yu, Rong1 (AUTHOR) yurong@gdut.edu.cn |
| Source: | IET Communications (Wiley-Blackwell). Dec2024, Vol. 18 Issue 20, p1900-1914. 15p. |
| Subjects: | Internet traffic, Traffic safety, Internet safety, Data modeling, Algorithms |
| Abstract: | Scene risk identification is essential for the traffic safety of Internet of Vehicles. However, the performance of existing risk identification approaches is heavily limited by the imbalanced historical data and the poor model interpretability. Meanwhile, the large processing delay and the potential privacy leakage threat also restrict their application. In this paper, a novel risk identification model is proposed that leverages the synthetic minority over‐sampling technique nearest neighbor (SMOTEENN) method to balance between high‐risk and low‐risk data. The risk identification model has fine interpretability by using recursive feature elimination cross validation (RFECV) with the Shapley additive explanation (SHAP) to analyze the importance of different features, and further elaborately design the Focal Loss function to tackle the disparity between the difficult and easy sample learning. The proposed interpretability scene risk identification framework, named iScene, is built on the infrastructure of 6G space‐air‐ground integrated networks (SAGINs) with blockchain assistance. The model updata efficiency and privacy preservation are effectively enhanced. An elastic computing offloading algorithm is applied to minimize the system overhead under the hierarchical edge service architecture. The experimental evaluation is carried out to verify the effectiveness of the proposed risk identification framework. The results indicate that the G‐Mean value is increased by 23.4%, while the task average response delay is reduced by 21.2%, compared to that in the traditional risk identification approaches with local computing services. [ABSTRACT FROM AUTHOR] |
| Copyright of IET Communications (Wiley-Blackwell) 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: 181730767 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: iScene: An interpretable framework with hierarchical edge services for scene risk identification in 6G internet of vehicles. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhong%2C+Wuchang%22">Zhong, Wuchang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Siming%22">Wang, Siming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Rong%22">Yu, Rong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yurong@gdut.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IET+Communications+%28Wiley-Blackwell%29%22">IET Communications (Wiley-Blackwell)</searchLink>. Dec2024, Vol. 18 Issue 20, p1900-1914. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Internet+traffic%22">Internet traffic</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+safety%22">Traffic safety</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+safety%22">Internet safety</searchLink><br /><searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Scene risk identification is essential for the traffic safety of Internet of Vehicles. However, the performance of existing risk identification approaches is heavily limited by the imbalanced historical data and the poor model interpretability. Meanwhile, the large processing delay and the potential privacy leakage threat also restrict their application. In this paper, a novel risk identification model is proposed that leverages the synthetic minority over‐sampling technique nearest neighbor (SMOTEENN) method to balance between high‐risk and low‐risk data. The risk identification model has fine interpretability by using recursive feature elimination cross validation (RFECV) with the Shapley additive explanation (SHAP) to analyze the importance of different features, and further elaborately design the Focal Loss function to tackle the disparity between the difficult and easy sample learning. The proposed interpretability scene risk identification framework, named iScene, is built on the infrastructure of 6G space‐air‐ground integrated networks (SAGINs) with blockchain assistance. The model updata efficiency and privacy preservation are effectively enhanced. An elastic computing offloading algorithm is applied to minimize the system overhead under the hierarchical edge service architecture. The experimental evaluation is carried out to verify the effectiveness of the proposed risk identification framework. The results indicate that the G‐Mean value is increased by 23.4%, while the task average response delay is reduced by 21.2%, compared to that in the traditional risk identification approaches with local computing services. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IET Communications (Wiley-Blackwell) 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: 10.1049/cmu2.12704 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1900 Subjects: – SubjectFull: Internet traffic Type: general – SubjectFull: Traffic safety Type: general – SubjectFull: Internet safety Type: general – SubjectFull: Data modeling Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: iScene: An interpretable framework with hierarchical edge services for scene risk identification in 6G internet of vehicles. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhong, Wuchang – PersonEntity: Name: NameFull: Wang, Siming – PersonEntity: Name: NameFull: Yu, Rong IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 17518628 Numbering: – Type: volume Value: 18 – Type: issue Value: 20 Titles: – TitleFull: IET Communications (Wiley-Blackwell) Type: main |
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