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المؤلفHashash O.
المؤلفSharafeddine S.
المؤلفDawy Z.
المؤلفMohamed A.
المؤلفYaacoub E.
تاريخ الإتاحة2022-04-21T08:58:20Z
تاريخ النشر2021
اسم المنشورIEEE Wireless Communications Letters
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/LWC.2021.3117876
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30051
الملخصEdge 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.
راعي المشروعQatar Foundation;Qatar National Research Fund
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعEnergy 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
العنوانEnergy-Aware Distributed Edge ML for mHealth Applications with Strict Latency Requirements
النوعArticle
الصفحات2791-2794
رقم العدد12
رقم المجلد10
dc.accessType Abstract Only


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