@inproceedings{eea07bd08b394fdeafbc0287f6de5395,
title = "Dynamic-Deep: Tune ECG Task Performance and Optimize Compression in IoT Architectures",
abstract = "Monitoring medical data, e.g., Electrocardiogram (ECG) signals, is a common application of Internet of Things (IoT) devices. Compression methods are often applied on the massive amounts of sensor data generated prior to sending it to the Cloud to reduce the storage and delivery costs. A lossy compression provides high compression gain (CG), but may reduce the performance of an ECG application (downstream task) due to information loss. Previous works on ECG monitoring focus either on optimizing the signal reconstruction or the task's performance. Instead, we advocate a self-adapting lossy compression solution that allows configuring a desired performance level on the downstream tasks while maintaining an optimized CG that reduces Cloud costs.We propose Dynamic-Deep, a task-aware compression geared for IoT-Cloud architectures. Our compressor is trained to optimize the CG while maintaining the performance requirement of the downstream tasks chosen out of a wide range. In deployment, the IoT edge device adapts the compression and sends an optimized representation for each data segment, accounting for the downstream task's desired performance without relying on feedback from the Cloud. We conduct an extensive evaluation of our approach on common ECG datasets using two popular ECG applications, which includes heart rate (HR) arrhythmia classification. We demonstrate that Dynamic-Deep can be configured to improve HR classification F1-score in a wide range of requirements. One of which is tuned to improve the F1-score by 3 and increases CG by up to 83% compared to the previous state-of-the-art (autoencoder-based) compressor. Analyzing Dynamic-Deep on the Google Cloud Platform, we observe a 97% reduction in cloud costs compared to a no compression solution.To the best of our knowledge, Dynamic-Deep is the first end-to-end system architecture proposal to focus on balancing the need for high performance of cloud-based downstream tasks and the desire to achieve optimized compression in IoT ECG monitoring settings.",
author = "Eli Brosh and Elad Wasserstein and Anat Bremler-Barr",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022 ; Conference date: 25-04-2022 Through 29-04-2022",
year = "2022",
doi = "10.1109/NOMS54207.2022.9789801",
language = "الإنجليزيّة",
series = "Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Pal Varga and Granville, {Lisandro Zambenedetti} and Alex Galis and Istvan Godor and Noura Limam and Prosper Chemouil and Jerome Francois and Marc-Oliver Pahl",
booktitle = "Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022",
address = "الولايات المتّحدة",
}