Large-size data distribution in IoV based on 5G/6G compatible heterogeneous network
Article
Yin, X., Liu, J., Cheng, X. and Xiong, X. 2022. Large-size data distribution in IoV based on 5G/6G compatible heterogeneous network. IEEE Transactions on Intelligent Transportation Systems. 25 (7), pp. 9840-9852. https://doi.org/10.1109/TITS.2021.3118701
| Type | Article |
|---|---|
| Title | Large-size data distribution in IoV based on 5G/6G compatible heterogeneous network |
| Authors | Yin, X., Liu, J., Cheng, X. and Xiong, X. |
| Abstract | The distribution of large-size data block in the Internet of Vehicles (IoV), especially in the urban IoV with dense vehicles, is still a challenge issue. Though the methods based on 5G-cellular network commonly can efficiently distribute the large-size file in IoVs, they also have obvious defects, such as occupying scarce 5G resources, generating communication fees and limited service coverage. This paper focuses on distributing large-size data block in IoVs based on dedicated vehicular ad hoc network to reduce the relying on cellular (5G/6G) resources. A heterogeneous vehicular network (HetVNET), in which a short-range OFDM wideband communication (SOWC) with ultra-high data rate and the original IEEE 802.11p protocol are included, is first proposed. To match with the proposed HetVNET, a content-centric data distribution scheme based on edge caching is designed. The content-centric data distribution architecture for distributing large-size file, the cache node selection and the process of data distribution and control based on the HetVNET are studied. The 5G/6G communication can be enabled in the scheme to further enhance the engineering stability of the massive infrastructure-to-vehicle (I2V) broadcast in IoVs. The evaluation results show that the proposed approach has low delivery delay and high penetration ratio. |
| Keywords | Heterogeneous network; Internet of Vehicles; data distribution; large-size file |
| Sustainable Development Goals | 9 Industry, innovation and infrastructure |
| Middlesex University Theme | Sustainability |
| Publisher | IEEE |
| Journal | IEEE Transactions on Intelligent Transportation Systems |
| ISSN | 1524-9050 |
| Electronic | 1558-0016 |
| Publication dates | |
| Online | 15 Oct 2021 |
| Jul 2022 | |
| Publication process dates | |
| Submitted | 22 Jun 2021 |
| Accepted | 15 Sep 2021 |
| Deposited | 20 Aug 2024 |
| Output status | Published |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/TITS.2021.3118701 |
| Web of Science identifier | WOS:000732179800001 |
| Language | English |
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Guo, X., Huang, Y., Ye, J., Yin, S., Li, M., Li, Z., Yiu, S. and Cheng, X. 2021. ShadowFPE: new encrypted web application solution based on shadow DOM. Mobile Networks and Applications. 26 (4), pp. 1733-1746. https://doi.org/10.1007/s11036-019-01509-yLearning context-aware outfit recommendation
Abugabah, A., Cheng, X. and Wang, J. 2020. Learning context-aware outfit recommendation. Symmetry. 12 (6), pp. 1-13. https://doi.org/10.3390/sym12060873