Cloud-Based Secure Data Management for Internet of Vehicles Platforms

The rapid growth of Internet of Vehicles (IoV) platforms has raised significant concerns about data security and storage efficiency. This study proposes a novel approach that is integrating cloud computing, machine learning, and advanced database management to enhance IoV data security and storage. MongoDB is utilized for data storage. The logistic regression and Support Vector Machine (SVM) algorithms are combined for security detection. By this way, our method demonstrates improved performance over traditional approaches. Experimental results showed a 4.60% increase in data recognition precision, a 4.66% higher recall, and a 2.55% improvement in the F1 score compared to existing models. The proposed system also exhibits enhanced data storage efficiency and robust security detection capabilities. These findings demonstrate our method has significant potential for improving IoV platform security and data management in real-world applications.

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Publication Details

Journal
Tehnicki vjesnik - Technical Gazette
Published
2026-05-01
DOI
https://doi.org/10.17559/tv-20240428001508
Primary Topic
Vehicular Ad Hoc Networks (VANETs)
Type
article
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article

Cloud-Based Secure Data Management for Internet of Vehicles Platforms

Yang Xing, Huihui Wang, Ke Xiang
Tehnicki vjesnik - Technical Gazette
Vehicular Ad Hoc Networks (VANETs)
article

Cloud-Based Secure Data Management for Internet of Vehicles Platforms

Yang Xing, Huihui Wang, Ke Xiang
article en

Abstract

The rapid growth of Internet of Vehicles (IoV) platforms has raised significant concerns about data security and storage efficiency. This study proposes a novel approach that is integrating cloud computing, machine learning, and advanced database management to enhance IoV data security and storage. MongoDB is utilized for data storage. The logistic regression and Support Vector Machine (SVM) algorithms are combined for security detection. By this way, our method demonstrates improved performance over traditional approaches. Experimental results showed a 4.60% increase in data recognition precision, a 4.66% higher recall, and a 2.55% improvement in the F1 score compared to existing models. The proposed system also exhibits enhanced data storage efficiency and robust security detection capabilities. These findings demonstrate our method has significant potential for improving IoV platform security and data management in real-world applications.

Tehnicki vjesnik - Technical GazetteVol. 33(3)
China Telecom (China) (CN), Geely (China) (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 12%
Vehicular Ad Hoc Networks (VANETs)
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Cloud-Based Secure Data Management for Internet of Vehicles Platforms — Yang Xing, Huihui Wang, et al. · Tehnicki vjesnik - Technical Gazette (2026) | TGRS Research Map | TGRS