Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2375
- Type
- D - Journal article
- DOI
-
10.1109/jiot.2019.2963371
- Title of journal
- IEEE Internet of Things Journal
- Article number
- -
- First page
- 4260
- Volume
- 7
- Issue
- 5
- ISSN
- 2327-4662
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2019
- URL
-
https://e-space.mmu.ac.uk/625219/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
7
- Research group(s)
-
D - Smart Infrastructure
- Citation count
- 41
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper was the outcome of an industry-academia consortium based in China, Portugal, Australia, USA, and UK funded by National Natural Science Foundation, China, State Grid Corporation of China, European Regional Development Fund and The Regional Operational Program of the Algarve. This work uses the concepts of machine learning, Lyapunov optimization, and matching theory to optimize energy budgets and service reliability by providing guaranteed throughput in Industrial IoT applications. The paper has become a key reference for researchers in the field and has been downloaded more than 1170 times since January 2020.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -