Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud
Forooghifar, Farnaz ; Aminifar, Amir LU
and Atienza, David
(2019)
In IEEE Transactions on Biomedical Circuits and Systems
13(6).
p.1338-1350
- Abstract
The integration of wearable devices in humans' daily lives has grown significantly in recent years and still continues to affect different aspects of high-quality life. Thus, ensuring the reliability of the decisions becomes essential in biomedical applications, while representing a major challenge considering battery-powered wearable technologies. Transferring the complex and energy-consuming computations to fogs or clouds can significantly reduce the energy consumption of wearable devices and result in a longer lifetime of these systems with a single battery charge. In this work, we aim to distribute the complex and energy-consuming machine-learning computations between the edge, fog, and cloud, based on the notion of self-awareness... (More)
The integration of wearable devices in humans' daily lives has grown significantly in recent years and still continues to affect different aspects of high-quality life. Thus, ensuring the reliability of the decisions becomes essential in biomedical applications, while representing a major challenge considering battery-powered wearable technologies. Transferring the complex and energy-consuming computations to fogs or clouds can significantly reduce the energy consumption of wearable devices and result in a longer lifetime of these systems with a single battery charge. In this work, we aim to distribute the complex and energy-consuming machine-learning computations between the edge, fog, and cloud, based on the notion of self-awareness that takes into account the complexity and reliability of the algorithm. We also model and analyze the trade-offs in terms of energy consumption, latency, and performance of different Internet of Things (IoT) solutions. We consider the epileptic seizure detection problem as our real-world case study to demonstrate the importance of our proposed self-aware methodology.
(Less)
- author
- Forooghifar, Farnaz
; Aminifar, Amir
LU
and Atienza, David
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- cloud, distributed health monitoring, edge, epilepsy, fog, IoT, self-awareness
- in
- IEEE Transactions on Biomedical Circuits and Systems
- volume
- 13
- issue
- 6
- pages
- 1338 - 1350
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- pmid:31689205
- scopus:85074567379
- ISSN
- 1932-4545
- DOI
- 10.1109/TBCAS.2019.2951222
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: IEEE
- id
- 2767bb5d-243d-44fb-bf79-7889ea78c1e6
- date added to LUP
- 2022-02-05 01:21:52
- date last changed
- 2024-04-25 10:10:09
@article{2767bb5d-243d-44fb-bf79-7889ea78c1e6,
abstract = {{<p>The integration of wearable devices in humans' daily lives has grown significantly in recent years and still continues to affect different aspects of high-quality life. Thus, ensuring the reliability of the decisions becomes essential in biomedical applications, while representing a major challenge considering battery-powered wearable technologies. Transferring the complex and energy-consuming computations to fogs or clouds can significantly reduce the energy consumption of wearable devices and result in a longer lifetime of these systems with a single battery charge. In this work, we aim to distribute the complex and energy-consuming machine-learning computations between the edge, fog, and cloud, based on the notion of self-awareness that takes into account the complexity and reliability of the algorithm. We also model and analyze the trade-offs in terms of energy consumption, latency, and performance of different Internet of Things (IoT) solutions. We consider the epileptic seizure detection problem as our real-world case study to demonstrate the importance of our proposed self-aware methodology.</p>}},
author = {{Forooghifar, Farnaz and Aminifar, Amir and Atienza, David}},
issn = {{1932-4545}},
keywords = {{cloud; distributed health monitoring; edge; epilepsy; fog; IoT; self-awareness}},
language = {{eng}},
number = {{6}},
pages = {{1338--1350}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Biomedical Circuits and Systems}},
title = {{Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud}},
url = {{http://dx.doi.org/10.1109/TBCAS.2019.2951222}},
doi = {{10.1109/TBCAS.2019.2951222}},
volume = {{13}},
year = {{2019}},
}