HetMEC: Heterogeneous Multi-Layer Mobile Edge Computing in the 6 G Era
Abstract
Driven by an increasing number of mobile applications, mobile edge computing (MEC) has been considered as a promising candidate to support the huge amount of data processing services. However, the conventional MEC suffers from the insufficient utilization of computing and transmission resources through the entire network, resulting in inevitably long processing and transmission latency especially to computation-intensive applications in the 6 G era. In this article, we propose a heterogeneous multi-layer mobile edge computing (HetMEC), where different devices, ranging from edge devices (EDs), i.e., the mobile devices that generate raw data of computing tasks in the radio access networks, to the cloud center (CC), are inherently involved different layers of the network and collaborate for data processing. To support a low-latency service, a reinforcement learning-based framework is constructed to adapt to the unstable wireless environments as well as the dynamically varying data generation speed of each ED. Under this framework, key research issues and solutions including task offloading, cognitive radio based spectrum access, pricing scheme design, and network congestion control are presented. Some further research directions and opening issues are also discussed in the perception of network planning and optimization, network control, and application-specific issues.
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Cell therapy research is carried out for critical disease treatment.
“For instance, cell therapy research is carried out for critical disease treatment [47], [48].”
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6G With Smart Healthcare, Haptic Communication
Kousalya, K., Mohana, R. S., Sasipriyaa, N., Prabha, C., & Udhayakumar, T. (2022) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-7998-9636-4.ch013
Future Intelligent Communication with 6G Technology: A Review
Jain, S., Yadav, A. K., Kumar, R., V, Yadav, V., & Singh, D. (2023) · International Journal of Sensors Wireless Communications and Control · doi:10.2174/0122103279270825231023095946
Task Partition-Based Intelligent Offloading for Cache-Assisted Cloud-Edge Cooperation Networks
Fang, C., Luo, H., Yang, X., Xie, H., Tan, F., Tseng, S., & Dong, M. (2023) · Globecom · doi:10.1109/globecom54140.2023.10436922
Kumar, L., Sadhu, A. K., & Dasgupta, R. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650142
Smart Healthcare Innovations Powered by 6G Networks
Ashok, P., Hallur, G. G., & Chirputkar, A. (2025) · Recent Patents on Engineering · doi:10.2174/0118722121360907250209163714
Achieving Ultra-Reliable Low Latency Communication in 5G and Beyond
Bajracharya, R., & Shrestha, R. (2026) · Sensors · doi:10.3390/s26113485
- Authors
- Zhang, Y., Di, B., Wang, P., Lin, J., & Song, L.
- Affiliations
- Peking University (CN)
- Venue
- IEEE Transactions on Vehicular Technology
- Published
- 2020-04
- Volume
- 69
- Issue
- 4
- Pages
- 4388-4400
- ISSN
- 0018-9545 (ISSN-L)
- 1939-9359
- OpenAlex
- W3007747556
- Cited by
- 74
- References
- 40
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 11 · 2023: 14 · 2022: 19 · 2021: 18 · 2020: 7
- FWCI
- 6.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Vehicular Technology (publishedVersion)
- Funders
- National Natural Science Foundation of China (61625101, 61829101, 61941101)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Keywords
- mobile edge computing, 6G, task offloading, congestion control, low-latency services, reinforcement learning, edge devices
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