Name
Title | ||
|---|---|---|
Energy-Aware Distributed Edge ML for mHealth Applications With Strict Latency Requirements |
Abstract | ||
|---|---|---|
Edge machine learning (Edge ML) is expected to serve as a key enabler for real-time mobile health (mHealth) applications. However, its reliability is governed by the limited energy and computing resources of user equipment (UE), along with the wireless channel variations and dynamic resource allocation at edge servers. In this letter, we incorporate both UE and edge server computing to satisfy the... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LWC.2021.3117876 | IEEE Wireless Communications Letters |
Keywords | DocType | Volume |
Feature extraction,Servers,Wireless communication,Real-time systems,Monitoring,Resource management,Optimization | Journal | 10 |
Issue | ISSN | Citations |
12 | 2162-2337 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Omar Hashash | 1 | 0 | 0.68 |
| Sanaa Sharafeddine | 2 | 0 | 1.69 |
| Zaher Dawy | 3 | 0 | 0.68 |
| Amr Mohamed | 4 | 0 | 0.34 |
| Elias Yaacoub | 5 | 6 | 1.46 |