November 4, 2019 Journal article Open Access

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

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  <identifier identifierType="URL">https://zenodo.org/record/3903306</identifier>
  <creators>
    <creator>
      <creatorName>Forooghifar, Farnaz</creatorName>
      <givenName>Farnaz</givenName>
      <familyName>Forooghifar</familyName>
      <affiliation>EPFL</affiliation>
    </creator>
    <creator>
      <creatorName>Aminifar, Amir</creatorName>
      <givenName>Amir</givenName>
      <familyName>Aminifar</familyName>
      <affiliation>EPFL</affiliation>
    </creator>
    <creator>
      <creatorName>Atienza Alonso, David</creatorName>
      <givenName>David</givenName>
      <familyName>Atienza Alonso</familyName>
      <affiliation>EPFL</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-11-04</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3903306</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TBCAS.2019.2951222</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/deephealth</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&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;</description>
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  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/825111/">825111</awardNumber>
      <awardTitle>Deep-Learning and HPC to Boost Biomedical Applications for Health</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/785907/">785907</awardNumber>
      <awardTitle>Human Brain Project Specific Grant Agreement 2</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100001711</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/SNSF/Project+funding/200020_182009/">200020_182009</awardNumber>
      <awardTitle>ML-edge: Enabling Machine-Learning-Based Health Monitoring in Edge Sensors via Architectural Customization</awardTitle>
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