An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence
- Submitting institution
-
University of Derby
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 784989-2
- Type
- D - Journal article
- DOI
-
10.1109/TII.2019.2902877
- Title of journal
- IEEE Transactions on Industrial Informatics
- Article number
- -
- First page
- 4295
- Volume
- 15
- Issue
- 7
- ISSN
- 1551-3203
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- URL
-
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8658146
- 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
-
4
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research advanced data analytics in edge computing from back-end servers to front-end devices. The advancement is the incorporation of deep learning into a fully decentralised environment, supporting artificial intelligence (AI) on the resource-constrained edge devices. The outcome is significant as this work laid a foundation of network representation and enabled computational expensive AI algorithms trained in end-to-end devices to support timely and accurate decision making on the edge without dedicated servers.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
