Task Offloading With Multi-Tier Computing Resources in Next Generation Wireless Networks
Open accessAbstract
With the development of next-generation wireless networks, the Internet of Things (IoT) is evolving towards the intelligent IoT (iIoT), where intelligent applications usually have stringent delay and jitter requirements. In order to provide low-latency services to heterogeneous users in the emerging iIoT, multi-tier computing was proposed by effectively combining edge computing and fog computing. More specifically, multi-tier computing systems compensate for cloud computing through task offloading and dispersing computing tasks to multi-tier nodes along the continuum from the cloud to things. In this paper, we investigate key techniques and directions for wireless communications and resource allocation approaches to enable task offloading in multi-tier computing systems. A multi-tier computing model, with its main functionality and optimization methods, is presented in detail. We hope that this paper will serve as a valuable reference and guide to the theoretical, algorithmic, and systematic opportunities of multi-tier computing towards next-generation wireless networks.
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Edge computing and 5G networks offer promising solutions by processing data closer to its source, significantly reducing latency and enhancing synchronization.
“Edge computing and 5G networks offer promising solutions by processing data closer to its source, significantly reducing latency and enhancing synchronization [127,128].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [128]
The Introduction and the section on edge-intelligence task offloading explain that processing tasks near devices can substantially reduce latency, including in 5G-integrated multi-tier systems. The passages do not establish that this approach enhances synchronization, so that part of the attributed claim is unsupported. (LLM, conf=0.86)
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Toward immersive communications in 6G
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“Edge computing and 5G networks offer promising solutions by processing data closer to its source, significantly reducing latency and enhancing synchronization [127,128].”
- Authors
- Wang, K., Jin, J., Yang, Y., Zhang, T., Nallanathan, A., Tellambura, C., & Jabbari, B.
- Affiliations
- East China Normal University (CN)
- Swinburne University of Technology (AU)
- Peng Cheng Laboratory (CN)
- National Institute of Standards and Technology (US)
- Queen Mary University of London (GB)
- University of Alberta (CA)
- Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA
- ORCID
- Venue
- IEEE Journal on Selected Areas in Communications
- Published
- 2023-02
- Volume
- 41
- Issue
- 2
- Pages
- 306-319
- ISSN
- 0733-8716 (ISSN-L)
- 1558-0008
- OpenAlex
- W4312674606
- Cited by
- 95
- References
- 110
- Citations by year
- 2026: 15 · 2025: 32 · 2024: 25 · 2023: 21 · 2022: 2
- FWCI
- 8.88
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Journal on Selected Areas in Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61801463)
- National Key Research and Development Program of China (2020YFB2104300)
- Major Key Project of Peng Cheng Laboratory (PCL2021A15)
- Joint Funds of the National Natural Science Foundation of China (U21B2002)
- National Development and Reform Commission of China (NDRC) through the “fifth-generation (5G) Network Enabled Intelligent Medicine and Emergency Rescue System for Giant Cities.”
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- task offloading, edge computing, fog computing, Internet of Things, resource allocation, low-latency services, heterogeneous users, next-generation wireless networks, cloud computing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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