An Overview on Edge Computing Research
Open accessAbstract
With the rapid development of the Internet of Everything (IoE), the number of smart devices connected to the Internet is increasing, resulting in large-scale data, which has caused problems such as bandwidth load, slow response speed, poor security, and poor privacy in traditional cloud computing models. Traditional cloud computing is no longer sufficient to support the diverse needs of today's intelligent society for data processing, so edge computing technologies have emerged. It is a new computing paradigm for performing calculations at the edge of the network. Unlike cloud computing, it emphasizes closer to the user and closer to the source of the data. At the edge of the network, it is lightweight for local, small-scale data storage and processing. This article mainly reviews the related research and results of edge computing. First, it summarizes the concept of edge computing and compares it with cloud computing. Then summarize the architecture of edge computing, keyword technology, security and privacy protection, and finally summarize the applications of edge computing.
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Reduction in network burden from edge computing enhances the efficiency of network bandwidth utilization.
“This reduction in network burden enhances the efficiency of network bandwidth utilization [21].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [21]
Model distillation and edge computing may reduce energy consumption without sacrificing performance.
“model distillation [47] and edge computing [20], which may reduce energy consumption without sacrificing performance [119]”
From Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education (2025) · cites this paper as [20]
TLMs are widely integrated into edge computing and IoT environments, allowing devices to process and interpret natural language commands locally, without reliance on cloud infrastructure.
“TLMs are widely integrated into edge computing [140] and IoT environments [141], allowing devices to process and interpret natural language commands locally, without reliance on cloud infrastructure.”
From Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions (2025) · cites this paper as [140]
Cites
Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733 · see citing works
The emergence of edge computing
Satyanarayanan, M. (2017) · Computer · doi:10.1109/mc.2017.9 · Cited by 2,832 · see citing works
Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City
Barthélemy, J., Verstaevel, N., Forehead, H., & Perez, P. (2019) · Sensors · doi:10.3390/s19092048 · Cited by 203 · see citing works
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Ahmad, S., Shakeel, I., Mehfuz, S., & Ahmad, J. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100568 · Cited by 197 · see citing works
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
“This reduction in network burden enhances the efficiency of network bandwidth utilization [21].”
BackgroundA survey on optimization techniques for edge artificial intelligence (ai)
Surianarayanan, C., Lawrence, J. J., Chelliah, P. R., Prakash, E., & Hewage, C. (2023) · Sensors · doi:10.3390/s23031279 · Cited by 131 · see citing works
Edge computing based secure health monitoring framework for electronic healthcare system
Singh, A., & Chatterjee, K. (2023) · Cluster Computing · doi:10.1007/s10586-022-03717-w · Cited by 69 · see citing works
Lyu, W., Zhang, S., Chung, T., Sun, Y., & Zhang, Y. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100383 · Cited by 68 · see citing works
“Second, educators can explore lower-resource AI solutions, such as model distillation [47] and edge computing [20], which may reduce energy consumption without sacrificing performance [119].”
Lamaakal, I., Maleh, Y., El Makkaoui, K., Ouahbi, I., Pławiak, P., Alfarraj, O., Almousa, M., & Abd El-Latif, A. A. (2025) · Sensors · doi:10.3390/s25051318 · Cited by 60 · see citing works
Lema, D. G., Usamentiaga, R., & García, D. F. (2024) · Integration · doi:10.1016/j.vlsi.2023.102127 · Cited by 41 · see citing works
Machine learning at the edge for AI-enabled multiplexed pathogen detection
Ganjalizadeh, V., Meena, G. G., Stott, M. A., Hawkins, A. R., & Schmidt, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-31694-6 · Cited by 24 · see citing works
Navigating the DevOps landscape
Zhang, X., Zhao, P., & Jaskolka, J. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112331 · Cited by 21 · see citing works
MERRC: A Memristor-Enabled Reconfigurable Low-Power Reservoir Computing Architecture at the Edge
Nowshin, F., Huang, Y., Sarkar, M. R., Xia, Q., & Yi, Y. (2024) · IEEE Transactions on Circuits and Systems I: Regular Papers · doi:10.1109/tcsi.2023.3329337 · Cited by 12 · see citing works
- Authors
- Cao, K., Liu, Y., Meng, G., & Sun, Q.
- Affiliations
- Shenyang Jianzhu University (CN)
- ORCID
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 85714-85728
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3021951958
- Semantic Scholar
- 98b53b924793dc57a0b0e2f5fe6d546760786854
- Cited by
- 1,254
- References
- 3
- Citations by year
- 2026: 149 · 2025: 311 · 2024: 345 · 2023: 281 · 2022: 114 · 2021: 47 · 2020: 7
- FWCI
- 52.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61602323)
- National Postdoctoral Foundation of China (2016M591455)
- Youth Seedling Foundation of Liaoning Province (lnqn201913)
- Natural Science Funds of Liaoning Province (2019MS264, 20180550019)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Keywords
- edge computing, cloud computing comparison, edge computing architecture, Internet of Everything
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