Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CFEC.2018.8358733 | 2018 IEEE 2nd International Conference on Fog and Edge Computing (ICFEC) |
Keywords | DocType | ISBN |
edge enhanced deep learning system,large-scale video stream analytics,large-scale IoT data,IoT devices,central cloud,machine learning model,data capture source,cloud platform,deep learning pipeline,data source,fog nodes,cloud-only approach,Big Data,cloudlet-fog resources | Conference | 978-1-5386-6489-6 |
Citations | PageRank | References |
3 | 0.39 | 13 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Ali | 1 | 110 | 22.83 |
| Ashiq Anjum | 2 | 333 | 38.33 |
| Muhammad Usman Yaseen | 3 | 25 | 3.42 |
| Ali Reza Zamani | 4 | 43 | 6.93 |
| Daniel Balouek-Thomert | 5 | 16 | 7.84 |
| Omer Rana | 6 | 104 | 15.28 |
| Manish Parashar | 7 | 3876 | 343.30 |