The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Joint Computing and Caching in 5G-Envisioned Internet of Vehicles: A Deep Reinforcement Learning-Based Traffic Control System
Abstract
Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.
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
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562
Efficient multi-user computation offloading for mobile-edge cloud computing
Chen, X., Jiao, L., Li, W., & Fu, X. (2016) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2015.2487344 · Cited by 2,675
Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading
You, C., Huang, K., Chae, H., & Kim, B.-H. (2017) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2016.2633522 · Cited by 1,503
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471
Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network
Chen, M., & Hao, Y. (2018) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2018.2815360 · Cited by 1,068
Bi, S., & Zhang, Y. J. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2821664 · Cited by 870
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey
Mao, Q., Hu, F., & Hao, Q. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2846401 · Cited by 789
Ning, Z., Dong, P., Kong, X., & Xia, F. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2868616 · Cited by 519
Self-citationVehicular Fog Computing: Enabling Real-Time Traffic Management for Smart Cities
Ning, Z., Huang, J., & Wang, X. (2019) · IEEE Wireless Communications · doi:10.1109/mwc.2019.1700441 · Cited by 425
Self-citationKaur, K., Garg, S., Aujla, G. S., Kumar, N., Rodrigues, J. J. P. C., & Guizani, M. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700622 · Cited by 353
Cited by
Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning
Yu, Z., Hu, J., Min, G., Zhao, Z., Miao, W., & Hossain, M. S. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3017474 · Cited by 332
Wu, Q., Zhao, Y., Fan, Q., Fan, P., Wang, J., & Zhang, C. (2023) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3221271 · Cited by 191
Internet of Vehicles: Key Technologies, Network Model, Solutions and Challenges With Future Aspects
Qureshi, K. N., Din, S., Jeon, G., & Piccialli, F. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2994972 · Cited by 169
Wei, K., Zhang, L., Guo, Y., & Jiang, X. (2020) · IEEE Access · doi:10.1109/access.2020.2971654 · Cited by 107
Self-citationBao, W., Wu, C., Guleng, S., Zhang, J., Yau, K.-L. A., & Ji, Y. (2021) · China Communications · doi:10.23919/jcc.2021.06.004 · Cited by 71
Metadata
- Authors
- Ning, Z., Zhang, K., Wang, X., Obaidat, M. S., Guo, L., Hu, X., Hu, B., Guo, Y., Sadoun, B., & Kwok, R. Y. K.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2021-08
- Volume
- 22
- Issue
- 8
- Pages
- 5201-5212
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W3004421451
- Cited by
- 268
- References
- 30
- FWCI
- 24.05
- 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: Joint Computing and Caching in 5G-Envisioned Internet of Vehicles: A Deep Reinforcement Learning-Based Traffic Control System. https://app.cochat.ai/papers/10.1109/tits.2020.2970276
- The paper
- Ning, Z., Zhang, K., Wang, X., Obaidat, M. S., Guo, L., Hu, X., Hu, B., Guo, Y., Sadoun, B., & Kwok, R. Y. K. (2021). Joint Computing and Caching in 5G-Envisioned Internet of Vehicles: A Deep Reinforcement Learning-Based Traffic Control System. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2020.2970276
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.
