Isa, ISM, Musa, MOI, El-Gorashi, TEH et al. (2 more authors) (2018) Energy Efficiency of Fog Computing Health Monitoring Applications. In: ICTON 2018. ICTON 2018: 20th International Conference on Transparent Optical Networks, 01-05 Jul 2018, Bucharest, Romania. IEEE ISBN 978-1-5386-6605-0
Abstract
Fog computing offers a scalable and effective solution to overcome the increasing processing and networking demands of Internet of Thing (IoT) devices. In this paper, we investigate the use of fog computing for health monitoring applications. We consider a heart monitoring application where patients send their 30 minute recording of Electrocardiogram (ECG) signal for processing, analysis, and decision making at fog processing units within the time constraint recommended by the American Heart Association (AHA) to save heart patients when an abnormality in the ECG signal is detected. The locations of the processing servers are optimized so that the energy consumption of both the processing and networking equipment are minimised. The results show that processing the ECG signal at fog processing units yields a total energy consumption saving of up to 68% compared to processing the at the central cloud.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. This is an author produced version of a paper published in ICTON 2018. 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. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Fog Computing; Health Monitoring; ECG Signal; Gigabit Passive Optical Netwotk (GPON); Energy Efficiency; Mixed Integer Linear Programming (MILP) |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/K503836/1 EPSRC EP/H040536/1 EPSRC EP/K016873/1 EPSRC EP/R511717/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Aug 2018 15:37 |
Last Modified: | 03 Dec 2018 15:16 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICTON.2018.8473698 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:134747 |