The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT
Open accessAbstract
Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this article, we consider the optimization of channel selection that is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.
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Cites
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Mobile Edge Computing: A Survey on Architecture and Computation Offloading
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Dynamic computation offloading for mobile-edge computing with energy harvesting devices
Mao, Y., Zhang, J., & Letaief, K. B. (2016) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2016.2611964 · Cited by 1,705
Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading
You, C., Huang, K., Chae, H., & Kim, B.-H. (2017) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2016.2633522 · Cited by 1,503
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Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing
Liu, C.-F., Bennis, M., Debbah, M., & Poor, H. V. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2898573 · Cited by 435
When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning
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Self-citationFog/Edge Computing-Based IoT (FECIoT): Architecture, Applications, and Research Issues
Omoniwa, B., Hussain, R., Javed, M. A., Bouk, S. H., & Malik, S. A. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2875544 · Cited by 284
A Novel Multichannel Internet of Things Based on Dynamic Spectrum Sharing in 5G Communication
Liu, X., Jia, M., Zhang, X., & Lu, W. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2847731 · Cited by 268
Cited by
Resource Scheduling in Edge Computing: A Survey
Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3106401 · Cited by 588
“Similarly, the study in [44], [108], [149], [232] also utilized matching”
BackgroundResult6G Wireless Systems: A Vision, Architectural Elements, and Future Directions
Khan, L. U., Yaqoob, I., Imran, M., Han, Z., & Hong, C. S. (2020) · IEEE Access · doi:10.1109/access.2020.3015289 · Cited by 384
Towards edge computing in intelligent manufacturing: Past, present and future
Nain, G., Pattanaik, K. K., & Sharma, G. K. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.01.010 · Cited by 283
Zhou, Z., Jia, Z., Liao, H., Lu, W., Mumtaz, S., Guizani, M., & Tariq, M. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3137349 · Cited by 169
Self-citationFederated Learning for Industrial Internet of Things in Future Industries
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Wireless Communications · doi:10.1109/mwc.001.2100102 · Cited by 166
Metadata
- Authors
- Liao, H., Zhou, Z., Zhao, X., Zhang, L., Mumtaz, S., Jolfaei, A., Ahmed, S. H., & Bashir, A. K.
- Venue
- IEEE Internet of Things Journal
- Published
- 2020-05
- Volume
- 7
- Issue
- 5
- Pages
- 4260-4277
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W2998266342
- Cited by
- 286
- References
- 27
- FWCI
- 24.10
- Type
- journal article
- Language
- en
- Open access
- green
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- Liao, H., Zhou, Z., Zhao, X., Zhang, L., Mumtaz, S., Jolfaei, A., Ahmed, S. H., & Bashir, A. K. (2020). Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT. IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2019.2963371
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