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AuthorHashash O.
AuthorSharafeddine S.
AuthorDawy Z.
AuthorMohamed A.
AuthorYaacoub E.
Available date2022-04-21T08:58:20Z
Publication Date2021
Publication NameIEEE Wireless Communications Letters
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/LWC.2021.3117876
URIhttp://hdl.handle.net/10576/30051
AbstractEdge machine learning (Edge ML) is expected to serve as a key enabler for real-time mobile health (mHealth) applications. However, its reliability is governed by the limited energy and computing resources of user equipment (UE), along with the wireless channel variations and dynamic resource allocation at edge servers. In this letter, we incorporate both UE and edge server computing to satisfy the strict latency requirements of mHealth applications while efficiently utilizing the UE's energy resources. Specifically, we separate the feature extraction and classification processes of Edge ML inference and formulate an optimization problem to distribute them between the UE and the edge server while determining the optimal UE transmit power. We demonstrate the effectiveness of the proposed approach using an mHealth case study for predicting epileptic seizures using data from wearable health devices. 2012 IEEE.
SponsorQatar Foundation;Qatar National Research Fund
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectEnergy resources
Extraction
Feature extraction
Interactive computer systems
mHealth
Mobile edge computing
Optimization
Real time systems
Resource allocation
Features extraction
Mobile health systems
Neurological mobile health system
Optimisations
Real - Time system
Resource management
Seizure detection and prediction.
Seizure prediction
Seizure-detection
Wireless communications
Machine learning
TitleEnergy-Aware Distributed Edge ML for mHealth Applications with Strict Latency Requirements
TypeArticle
Pagination2791-2794
Issue Number12
Volume Number10
dc.accessType Abstract Only


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