The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Resource Allocation in 5G IoV Architecture Based on SDN and Fog-Cloud Computing
Abstract
In the traditional cloud-based Internet of Vehicles (IoV) architecture, it is difficult to guarantee the low latency requirements of the current intelligent transportation system (ITS). As a supplement to cloud computing, fog computing can effectively alleviate the bottlenecks of cloud computing bandwidth and computing resources and improve the quality of service (QoS) of the IoV. However, as a distributed system that operates near users, fog computing has a complicated network structure. In the complex and dynamic IoV environment, to effectively manage these computing resources with different attributes and provide high-quality services, it is necessary to design an efficient architecture and a resource allocation algorithm. Therefore, on the basis of fog-cloud computing and software-defined networking (SDN), a novel 5G IoV architecture is designed. In addition, after fully considering the service requirements of the IoV, a model of four objectives is constructed, and a many-objective optimization algorithm is proposed. The experiment results show that the proposed algorithm outperforms the other state-of-the-art algorithms.
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
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Zhang, Q., & Li, H. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.892759 · Cited by 10,069
Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2444095 · Cited by 8,630 · cited 1×
“Cloud computing has substantially promoted the development of the Internet of Things (IoT) [5].”
BackgroundDeb, K., & Jain, H. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2281535 · Cited by 7,133
Indicator-Based Selection in Multiobjective Search
Zitzler, E., & Künzli, S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30217-9_84 · Cited by 2,259
A faster algorithm for calculating hypervolume
While, L., Hingston, P., Barone, L., & Huband, S. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.851275 · Cited by 1,100
Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey
Abboud, K., Omar, H. A., & Zhuang, W. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2591558 · Cited by 785
Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization
Li, M., Yang, S., & Liu, X. (2014) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2262178 · Cited by 714
Kaiwartya, O., Abdullah, A. H., Cao, Y., Altameem, A., Prasad, M., Lin, C.-T., & Liu, X. (2016) · IEEE Access · doi:10.1109/access.2016.2603219 · Cited by 711
Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research
Nasrallah, A., Thyagaturu, A. S., Alharbi, Z., Wang, C., Shao, X., Reisslein, M., & ElBakoury, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2869350 · Cited by 695
Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization
Wang, H., Jiao, L., & Yao, X. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2350987 · Cited by 558
Cited by
Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy
Zhang, X., Li, J., Zhou, J., Zhang, S., Wang, J., Yuan, Y., Liu, J., & Li, J. (2025) · Automotive Innovation · doi:10.1007/s42154-024-00310-2 · Cited by 103
Metadata
- Authors
- Cao, B., Sun, Z., Zhang, J., & Gu, Y.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2021-06
- Volume
- 22
- Issue
- 6
- Pages
- 3832-3840
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W3125842443
- Cited by
- 267
- References
- 38
- FWCI
- 23.94
- Type
- journal article
- Language
- en
- Open access
- closed
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: Resource Allocation in 5G IoV Architecture Based on SDN and Fog-Cloud Computing. https://app.cochat.ai/papers/10.1109/tits.2020.3048844
- The paper
- Cao, B., Sun, Z., Zhang, J., & Gu, Y. (2021). Resource Allocation in 5G IoV Architecture Based on SDN and Fog-Cloud Computing. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2020.3048844
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.
