The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Big Data Driven Vehicular Networks
Open accessAbstract
VANETs enable information exchange among vehicles, other end devices and public networks, which plays a key role in road safety/infotainment, intelligent transportation systems, and self-driving systems. As vehicular connectivity soars, and new on-road mobile applications and technologies emerge, VANETs are generating an ever-increasing amount of data, requiring fast and reliable transmissions through VANETs. On the other hand, a variety of VANETs related data can be analyzed and utilized to improve the performance of VANETs. In this article, we first review VANETs technologies to efficiently and reliably transmit big data. Then, the methods employing big data for studying VANETs characteristics and improving VANETs performance are discussed. Furthermore, we present a case study where machine learning schemes are applied to analyze VANETs measurement data for efficiently detecting negative communication conditions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913
Cited by
Dang, S., Amin, O., Shihada, B., & Alouini, M.-S. (2020) · Nature Electronics · doi:10.1038/s41928-019-0355-6 · Cited by 1,790
Fu, Y., Li, C., Yu, F. R., Luan, T. H., & Zhao, P. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3297996 · Cited by 96
Shen, M., Zhang, J., Zhu, L., Xu, K., & Tang, X. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2019.2957425 · Cited by 83
Xue, J., Yu, K., Zhang, T., Zhou, H., Zhao, L., & Shen, X. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3347580 · Cited by 78
Self-citationApplications of Game Theory in Vehicular Networks: A Survey
Sun, Z., Liu, Y., Wang, J., Li, G., Anil, C., Li, K., Guo, X., Sun, G., Tian, D., & Cao, D. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3108466 · Cited by 68
Metadata
- Authors
- Cheng, N., Lyu, F., Chen, J., Xu, W., Zhou, H., Zhang, S., & Shen, X.
- Venue
- IEEE Network
- Published
- 2018-11
- Volume
- 32
- Issue
- 6
- Pages
- 160-167
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W2963103668
- Cited by
- 307
- References
- 1
- FWCI
- 17.69
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Big Data Driven Vehicular Networks. https://app.cochat.ai/papers/10.1109/mnet.2018.1700460
- The paper
- Cheng, N., Lyu, F., Chen, J., Xu, W., Zhou, H., Zhang, S., & Shen, X. (2018). Big Data Driven Vehicular Networks. IEEE Network. https://doi.org/10.1109/mnet.2018.1700460
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
