EdgeHealth: An Energy-Efficient Edge-based Remote mHealth Monitoring System
Author | Emam A. |
Author | Abdellatif A.A. |
Author | Mohamed A. |
Author | Harras K.A. |
Available date | 2020-04-09T12:27:30Z |
Publication Date | 2019 |
Publication Name | IEEE Wireless Communications and Networking Conference, WCNC |
Resource | Scopus |
ISSN | 15253511 |
Abstract | Promoting smart and scalable remote health monitoring systems is challenging due to the enormous amount of collected data that needs to be processed and transferred given the limited network resources and battery-operated devices. Thus, the conventional cloud computing paradigm alone, is not always the most suitable solution for enabling such systems. In this context, we propose and implement a smart edge-based health system that aims at decreasing the system latency and energy consumption, while optimizing the delivery of the medical data. In particular, we formulate a multi-objective optimization framework that enables an edge node to dynamically adjust compression parameters and select the optimal radio access technology (RAT) while maintaining a trade-off between energy consumption, latency, and distortion. Furthermore, to evaluate and verify our framework, we develop an experimental testbed, where a data emulator is implemented to send EEG data to an edge node that classifies, compresses, and transfers the gathered data through the optimal RAT to the health cloud. Our experimental results show that the proposed system can offer about 30% energy savings while decreasing the delivery time to half of its value compared to a system that lacks edge processing capabilities. |
Sponsor | This work was made possible by NPRP grant # 7-684-1-127 from the Qatar National Research Fund (a member of Qatar Foundation). The work of Amr Mohamed and Alaa Abdellatif is partially supported by NPRP grant #8-408-2-172. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | edge computing EEG signal Mobile-Health multi-RAT Seizures detection |
Type | Conference Paper |
Volume Number | 2019-April |
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