Enabling AI in Future Wireless Networks: A Data Life Cycle Perspective
Open accessAbstract
Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network Device AI, Access AI, User Device AI and Data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area.
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The FL-based offloading scheme in [64] improves network resource utilization without sacrificing learning accuracy.
“By simulating with MNIST datasets consisting of thousands of images, the proposed FL-based offloading scheme can improve network resource utilization without sacrificing the learning accuracy of local models.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [64]
The Federated Learning at User Side section (p. 26) says FL-based offloading trains data locally and eliminates channel congestion, supporting a network-resource benefit. It does not say that this offloading scheme maintains local-model learning accuracy; the 90% accuracy reported on the same page concerns a separate federated edge-computing scheme. The cited passages also do not establish the sentence’s MNIST simulation detail. (LLM, conf=0.84)
Experimental — accuracy not yet measuredFL achieves privacy preservation with guaranteed training accuracy.
“The implementation results verify the efficiency of FL in terms of low communication latency due to model learning without sharing raw data and privacy preservation with guaranteed training accuracy.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [64]
The Federated Learning at User Side section (p. 26) says FL trains on local data without sharing raw data, supporting the privacy claim, and describes low-latency learning. It reports high training accuracy for a particular federated edge-computing scheme, but does not guarantee training accuracy for FL generally. (LLM, conf=0.87)
Experimental — accuracy not yet measuredFL achieves low communication latency due to model learning without sharing raw data.
“The implementation results verify the efficiency of FL in terms of low communication latency due to model learning without sharing raw data and privacy preservation with guaranteed training accuracy.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [64]
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Cited by
Sorted by citations
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Self-citationTask Offloading With Multi-Tier Computing Resources in Next Generation Wireless Networks
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- Authors
- Nguyen, D. C., Cheng, P., Ding, M., Lopez-Perez, D., Pathirana, P. N., Li, J., Seneviratne, A., Li, Y., & Poor, H. V.
- Affiliations
- Deakin University (AU)
- The University of Sydney (AU)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Huawei Technologies (France) (FR)
- Tomsk Polytechnic University (RU)
- UNSW Sydney (AU)
- Princeton University (US)
- ORCID
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2021
- Volume
- 23
- Issue
- 1
- Pages
- 553-595
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W3131052410
- Cited by
- 160
- References
- 199
- Citations by year
- 2026: 11 · 2025: 26 · 2024: 40 · 2023: 28 · 2022: 31 · 2021: 24
- FWCI
- 9.74
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- Figshare (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- CSIRO Data61, Australia
- U.S. National Science Foundation (CCF-1908308)
- ARC (DE190100162)
- National Key Research and Development Program of China (2018YFB1004800)
- National Natural Science Foundation of China (61727802, 61872184)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Data and IoT Technologies
- Keywords
- data-driven AI, Internet of Things, edge AI, data lifecycle, data provenance, intelligent networks
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