Journal article Open Access

# Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud

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

### Citation Style Language JSON Export

{
"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"
}
15
52
views