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
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