Towards Green Edge Intelligence. Ben Cheikh, S. & Sigg, S. In Proceedings of the 13th International Conference on the Internet of Things, of IoT '23, pages 197–199, New York, NY, USA, 2024. Association for Computing Machinery. ![Towards Green Edge Intelligence [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This study presents our ongoing activities, along with a demonstration that showcases the integration of these endeavours into a real-world application. We demonstrate the integration of IoT devices with energy harvesting systems, as well as the incorporation of deep learning techniques into IoT devices. Finally, we consider the utilization of radio frequency (RF) technology for gesture detection and classification, based on deep learning algorithms.
@inproceedings{10.1145/3627050.3630737,
author = {Ben Cheikh, Sami and Sigg, Stephan},
title = {Towards Green Edge Intelligence},
year = {2024},
isbn = {9798400708541},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3627050.3630737},
doi = {10.1145/3627050.3630737},
abstract = {This study presents our ongoing activities, along with a demonstration that showcases the integration of these endeavours into a real-world application. We demonstrate the integration of IoT devices with energy harvesting systems, as well as the incorporation of deep learning techniques into IoT devices. Finally, we consider the utilization of radio frequency (RF) technology for gesture detection and classification, based on deep learning algorithms.},
booktitle = {Proceedings of the 13th International Conference on the Internet of Things},
pages = {197–199},
numpages = {3},
keywords = {Cloud computing, E-Health, Edge intelligence, Internet of Things, Smart Cities, Wireless power transfer},
location = {Nagoya, Japan},
series = {IoT '23}
}
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