November 4, 2019 Journal article Open Access

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

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{
  "DOI": "10.1109/TBCAS.2019.2951222", 
  "author": [
    {
      "family": "Forooghifar, Farnaz"
    }, 
    {
      "family": "Aminifar, Amir"
    }, 
    {
      "family": "Atienza Alonso, David"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2019, 
        11, 
        4
      ]
    ]
  }, 
  "abstract": "<p>The integration of wearable devices in humans&#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.</p>", 
  "title": "Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud", 
  "type": "article-journal", 
  "id": "3903306"
}