November 4, 2019 Journal article Open Access

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

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  <dc:creator>Forooghifar, Farnaz</dc:creator>
  <dc:creator>Aminifar, Amir</dc:creator>
  <dc:creator>Atienza Alonso, David</dc:creator>
  <dc:date>2019-11-04</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://zenodo.org/record/3903306</dc:identifier>
  <dc:identifier>10.1109/TBCAS.2019.2951222</dc:identifier>
  <dc:identifier>oai:zenodo.org:3903306</dc:identifier>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/825111/</dc:relation>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/785907/</dc:relation>
  <dc:relation>info:eu-repo/grantAgreement/SNSF/Project+funding/200020_182009/</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/deephealth</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:title>Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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