Reinforcing the Edge: Autonomous Energy Management for Mobile Device Clouds
| dc.contributor.author | Balasubramanian, Venkatraman | |
| dc.contributor.author | Zaman, Faisal | |
| dc.contributor.author | Aloqaily, Moayad | |
| dc.contributor.author | Alrabaee, Saed | |
| dc.contributor.author | Gorlatova, Maria | |
| dc.contributor.author | Reisslein, Martin | |
| dc.date.accessioned | 2021-11-18T06:10:05Z | |
| dc.date.available | 2021-11-18T06:10:05Z | |
| dc.date.copyright | © 2019 | |
| dc.date.issued | 2019 | |
| dc.description | This conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2019), available online at: https://doi.org/10.1109/INFCOMW.2019.8845263 | en_US |
| dc.description.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. © 2019 IEEE. | en_US |
| dc.identifier.citation | Balasubramanian, V., Zaman, F., Aloqaily, M., Alrabaee, S., Gorlatova, M., & Reisslein, M. (2019). Reinforcing the edge: Autonomous energy management for mobile device clouds. Paper presented at the INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019, 44-49. https://doi.org/10.1109/INFCOMW.2019.8845263 | en_US |
| dc.identifier.isbn | 978-172811878-9 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12519/467 | |
| dc.language.iso | en | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation | Authors Affiliations : Balasubramanian, V., Arizona State UniversityAZ, United States; Zaman, F., University of Ottawa, Ottawa, United States; Aloqaily, M., Canadian University Dubai, Dubai, United Arab Emirates; Alrabaee, S., United Arab Emirates University (UAEU), Al Ain, United Arab Emirates; Gorlatova, M., Duke University, NC, USANC, United States; Reisslein, M., Arizona State UniversityAZ, United States | |
| dc.relation.ispartofseries | INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019; | |
| dc.rights | Permission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc. | |
| dc.rights.holder | Copyright : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
| dc.subject | Device Clouds | en_US |
| dc.subject | Internet of Things | en_US |
| dc.subject | Mobile Edge Computing | en_US |
| dc.subject | Reinforcement Learning | en_US |
| dc.title | Reinforcing the Edge: Autonomous Energy Management for Mobile Device Clouds | en_US |
| dc.type | Conference Paper | en_US |