Journal article Open Access
Forooghifar, Farnaz; Aminifar, Amir; Atienza Alonso, David
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.
Name | Size | |
---|---|---|
EPFL - Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness from Edge to Cloud_preprint.pdf
md5:d714a3a3cb2efeed81f6b80a8b692e6e |
1.8 MB | Download |
Views | 80 |
Downloads | 110 |
Data volume | 196.7 MB |
Unique views | 57 |
Unique downloads | 107 |