The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Vehicular Edge Computing and Networking: A Survey
Open accessAbstract
As one key enabler of Intelligent Transportation System (ITS), Vehicular Ad Hoc Network (VANET) has received remarkable interest from academia and industry. The emerging vehicular applications and the exponential growing data have naturally led to the increased needs of communication, computation and storage resources, and also to strict performance requirements on response time and network bandwidth. In order to deal with these challenges, Mobile Edge Computing (MEC) is regarded as a promising solution. MEC pushes powerful computational and storage capacities from the remote cloud to the edge of networks in close proximity of vehicular users, which enables low latency and reduced bandwidth consumption. Driven by the benefits of MEC, many efforts have been devoted to integrating vehicular networks into MEC, thereby forming a novel paradigm named as Vehicular Edge Computing (VEC). In this paper, we provide a comprehensive survey of state-of-art research on VEC. First of all, we provide an overview of VEC, including the introduction, architecture, key enablers, advantages, challenges as well as several attractive application scenarios. Then, we describe several typical research topics where VEC is applied. After that, we present a careful literature review on existing research work in VEC by classification. Finally, we identify open research issues and discuss future research directions.
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Cites
A Survey on Mobile Edge Computing: The Communication Perspective
Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2745201 · Cited by 5,607
A survey of mobile cloud computing: architecture, applications, and approaches
Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013) · Wireless Communications and Mobile Computing · doi:10.1002/wcm.1203 · Cited by 2,391 · cited 2×
“In order to tackle these issues, Mobile Cloud Computing (MCC) is widely regarded as a promising solution [14].”
BackgroundMobile edge computing, Fog et al.: A survey and analysis of security threats and challenges
Roman, R., Lopez, J., & Mambo, M. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2016.11.009 · Cited by 1,338
Blockchain-based decentralized trust management in vehicular networks
Yang, Z., Yang, K., Lei, L., Zheng, K., & Leung, V. C. M. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2836144 · Cited by 932
Deep Learning Empowered Task Offloading for Mobile Edge Computing in Urban Informatics
Zhang, K., Zhu, Y., Leng, S., He, Y., Maharjan, S., & Zhang, Y. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2903191 · Cited by 309
Self-citationCheng, H. T., Shan, H., & Zhuang, W. (2011) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2010.11.009 · Cited by 273
Deep Reinforcement Learning for Mobile Edge Caching: Review, New Features, and Open Issues
Zhu, H., Cao, Y., Wang, W., Jiang, T., & Jin, S. (2018) · IEEE Network · doi:10.1109/mnet.2018.1800109 · Cited by 153 · cited 2×
“rests in contents are changing with time in different contexts, e.g., location, network topology, as well as personal status. This makes content popularity unknown before performing caching operation [68]. The authors of [87] try to solve this issue by employing deep learning. In [87], the probability prediction of re…”
MethodA study on the characteristics of douyin short videos and implications for edge caching
Chen, Z., He, Q., Mao, Z., Chung, H.-M., & Maharjan, S. (2019) · Proceedings of the ACM Turing Celebration Conference - China · doi:10.1145/3321408.3323082 · Cited by 94
Self-citationHuang, X., Yu, R., Pan, M., & Shu, L. (2018) · IEEE Access · doi:10.1109/access.2018.2868002 · Cited by 33
Cited by
Arthurs, P., Gillam, L., Krause, P., Wang, N., Halder, K., & Mouzakitis, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3084396 · Cited by 339
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
Liu, L., Zhao, M., Yu, M., Jan, M. A., Lan, D., & Taherkordi, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3142566 · Cited by 303
Self-citationOn 5G-V2X Use Cases and Enabling Technologies: A Comprehensive Survey
Alalewi, A., Dayoub, I., & Cherkaoui, S. (2021) · IEEE Access · doi:10.1109/access.2021.3100472 · Cited by 207
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
“W ITH the development of the internet of vehicles (IoV) and cloud computing, caching technology facilitates various real-time vehicular applications for vehicular users (VUs), such as automatic navigation, pattern recognition and multimedia entertainment [1], [2], [3].”
Metadata
- Authors
- Liu, L., Chen, C., Pei, Q., Maharjan, S., & Zhang, Y.
- Venue
- Mobile Networks and Applications
- Published
- 2020-07-25
- Volume
- 26
- Issue
- 3
- Pages
- 1145-1168
- ISSN
- 1383-469X (ISSN-L)
- 1572-8153
- OpenAlex
- W3045049179
- Cited by
- 597
- References
- 9
- FWCI
- 40.31
- Type
- journal article
- Language
- en
- Open access
- green
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