November 4, 2019 Journal article Open Access

Forooghifar, Farnaz; Aminifar, Amir; Atienza Alonso, David

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    <subfield code="a">ML-edge: Enabling Machine-Learning-Based Health Monitoring in Edge Sensors via Architectural Customization</subfield>
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    <subfield code="a">&lt;p&gt;The integration of wearable devices in humans&amp;#39; 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.&lt;/p&gt;</subfield>
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