The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art
Open accessAbstract
Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.
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Cites
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Maddikunta, P. K. R., Pham, Q.-V., B, P., Deepa, N., Dev, K., Gadekallu, T. R., Ruby, R., & Liyanage, M. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2021.100257 · Cited by 1,890
Self-citationFederated learning meets blockchain in edge computing: Opportunities and challenges
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Self-citationBlockchain for 5G and beyond networks: A state of the art survey
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Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N. (2020) · IEEE Access · doi:10.1109/access.2020.3009328 · Cited by 413
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BackgroundSelf-citationA Survey on Task Offloading in Multi-access Edge Computing
Islam, A., Debnath, A., Ghose, M., & Chakraborty, S. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102225 · Cited by 271
Metadata
- Authors
- Pham, Q.-V., Fang, F., Ha, V. N., Piran, M. J., Le, M., Le, L. B., Hwang, W.-J., & Ding, Z.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 116974-117017
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3034606981
- Cited by
- 870
- References
- 32
- FWCI
- 69.54
- Type
- journal article
- Language
- en
- Open access
- gold
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