The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Edge Computing with Artificial Intelligence: A Machine Learning Perspective
Open accessAbstract
Recent years have witnessed the widespread popularity of Internet of things (IoT). By providing sufficient data for model training and inference, IoT has promoted the development of artificial intelligence (AI) to a great extent. Under this background and trend, the traditional cloud computing model may nevertheless encounter many problems in independently tackling the massive data generated by IoT and meeting corresponding practical needs. In response, a new computing model called edge computing (EC) has drawn extensive attention from both industry and academia. With the continuous deepening of the research on EC, however, scholars have found that traditional (non-AI) methods have their limitations in enhancing the performance of EC. Seeing the successful application of AI in various fields, EC researchers start to set their sights on AI, especially from a perspective of machine learning, a branch of AI that has gained increased popularity in the past decades. In this article, we first explain the formal definition of EC and the reasons why EC has become a favorable computing model. Then, we discuss the problems of interest in EC. We summarize the traditional solutions and hightlight their limitations. By explaining the research results of using AI to optimize EC and applying AI to other fields under the EC architecture, this article can serve as a guide to explore new research ideas in these two aspects while enjoying the mutually beneficial relationship between AI and EC.
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Cites
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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
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“DRL combined with curriculum learning [111] has been used to realize a bottom-up energy management scheme [110].”
MethodThe emergence of edge computing
Satyanarayanan, M. (2017) · Computer · doi:10.1109/mc.2017.9 · Cited by 2,832
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2918951 · Cited by 2,455 · cited 3×
“In Reference [11], authors focus on the motivation and research work of deploying AI algorithm on the edge of the network.”
BackgroundMethodInfluentialDeep Learning With Edge Computing: A Review
Chen, J., & Ran, X. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2921977 · Cited by 1,540 · cited 1×
“Various methods of fast implementation of DL reasoning in the combination of end devices, edge servers and cloud, and the methods of training DL models in multiple edge devices are also discussed in Reference [14].”
MethodConvergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498 · cited 2×
“To achieve the best performance of DL training and reasoning, [15] comprehensively discusses how to design EC architecture with communication, computing power, and energy consumption constraints.”
BackgroundMethodThe role of big data in smart city
Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., Ahmed, E., & Chiroma, H. (2016) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.05.002 · Cited by 1,103
Cited by
A review of IoT applications in healthcare
Li, C., Wang, J., Wang, S., & Zhang, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2023.127017 · Cited by 369
Fahim, Y. A., Hasani, I. W., Kabba, S., & Ragab, W. M. (2025) · European Journal of Medical Research · doi:10.1186/s40001-025-03196-w · Cited by 329
A comprehensive survey of deep learning-based lightweight object detection models for edge devices
Mittal, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10877-1 · Cited by 244
Survey: federated learning data security and privacy-preserving in edge-Internet of Things
Li, H., Ge, L., & Tian, L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10774-7 · Cited by 112
“EC approaches combining ML (Hua et al. 2023; Ning et al. 2023) and deep learning (DL) (Ahmad et al. 2023; Zhang et al. 2023a; Ghosh and Grolinger 2020) have been applied in many fields.”
Horizontal Federated Recommender System: A Survey
Wang, L., Zhou, H., Bao, Y., Yan, X., Shen, G., & Kong, X. (2024) · ACM Computing Surveys · doi:10.1145/3656165 · Cited by 37
Metadata
- Authors
- Hua, H., Li, Y., Wang, T., Dong, N., Li, W., & Cao, J.
- Venue
- ACM Computing Surveys
- Published
- 2023-01-16
- Volume
- 55
- Issue
- 9
- Pages
- 1-35
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4291972732
- Cited by
- 563
- References
- 24
- FWCI
- 50.77
- Type
- journal article
- Language
- en
- Open access
- bronze
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