Edge enhanced deep learning system for large-scale video stream analytics.
Conference item
Muhammad, A., Anjum, Ashiq, Yaseen, M. Usman, Zamani, A. Reza, Balouek-Thomert, Daniel, Rana, Omer and Parashar, Manish 2018. Edge enhanced deep learning system for large-scale video stream analytics. IEEE. https://doi.org/10.1109/CFEC.2018.8358733
| Authors | Muhammad, A., Anjum, Ashiq, Yaseen, M. Usman, Zamani, A. Reza, Balouek-Thomert, Daniel, Rana, Omer and Parashar, Manish |
|---|---|
| Abstract | Applying deep learning models to large-scale IoT data is a compute-intensive task and needs significant computational resources. Existing approaches transfer this big data from IoT devices to a central cloud where inference is performed using a machine learning model. However, the network connecting the data capture source and the cloud platform can become a bottleneck. We address this problem by distributing the deep learning pipeline across edge and cloudlet/fog resources. The basic processing stages and trained models are distributed towards the edge of the network and on in-transit and cloud resources. The proposed approach performs initial processing of the data close to the data source at edge and fog nodes, resulting in significant reduction in the data that is transferred and stored in the cloud. Results on an object recognition scenario show 71\% efficiency gain in the throughput of the system by employing a combination of edge, in-transit and cloud resources when compared to a cloud-only approach. |
| Keywords | Edge computing; Deep learning; Machine learning; Cloud computing; Stream analytics |
| Year | 2018 |
| Journal | Proceedings of the 2nd International Conference on Fog and Edge Computing (ICFEC) |
| Publisher | IEEE |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/CFEC.2018.8358733 |
| Web address (URL) | https://ieeexplore.ieee.org/document/8358733/ |
| hdl:10545/622729 | |
| ISBN | 9781538664889 |
| File | File Access Level Open |
| File | File Access Level Restricted |
| Output status | Published |
| Publication dates | 14 May 2018 |
| Publication process dates | |
| Deposited | 22 May 2018, 13:16 |
| Contributors | University of Derby, Rutgers University and University of Cardiff |
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