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School of Engineering, Applied Science and Technology
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School of Engineering, Applied Science and Technology
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http://hdl.handle.net/20.500.12519/564
This community includes articles, book chapters, and conferences published by the School of Engineering, Applied Science and Technology faculty members.
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Browsing School of Engineering, Applied Science and Technology by Author "Alrabaee, Saed"
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Reinforcing the Edge: Autonomous Energy Management for Mobile Device Clouds
(
Institute of Electrical and Electronics Engineers Inc.
,
2019
)
Balasubramanian, Venkatraman
;
Zaman, Faisal
;
Aloqaily, Moayad
;
Alrabaee, Saed
;
Gorlatova, Maria
;
Reisslein, Martin
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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.
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