Name
Abstract | ||
|---|---|---|
The collaboration among mobile devices to form an edge cloud for sharing computation and data can drastically reduce the tasks that need to be transmitted to the cloud. Moreover, reinforcement learning (RL) research has recently begun to intersect with edge computing to reduce the amount of data (and tasks) that needs to be transmitted over the network. For battery-powered Internet of Things (IoT) devices, the energy consumption in collaborating edge devices emerges as an important problem. To address this problem, we propose an RL-based Droplet framework for autonomous energy management. Droplet learns the power-related statistics of the devices and forms a reliable group of resources for providing a computation environment on-the-fly. We compare the energy reductions achieved by two different state-of-the-art RL algorithms. Further, we model a reward strategy for edge devices that participate in the mobile device cloud service. The proposed strategy effectively achieves a 10% gain in the rewards earned compared to state-of-the-art strategies. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/INFCOMW.2019.8845263 | IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) |
Keywords | Field | DocType |
Mobile Edge Computing,Device Clouds,Internet of Things,Reinforcement Learning | Edge computing,Energy management,Computer science,Mobile edge computing,Edge device,Mobile device,Energy consumption,Distributed computing,Reinforcement learning,Cloud computing | Conference |
ISSN | ISBN | Citations |
2159-4228 | 978-1-7281-1879-6 | 4 |
PageRank | References | Authors |
0.41 | 13 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Venkatraman Balasubramanian | 1 | 16 | 1.99 |
| Zaman, M.F. | 2 | 35 | 5.92 |
| moayad aloqaily | 3 | 331 | 37.67 |
| Saed Alrabaee | 4 | 4 | 0.41 |
| Maria Gorlatova | 5 | 7 | 1.11 |
| Martin Reisslein | 6 | 1661 | 114.91 |