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Titlebook: Internet of Things; 5th The Global IoT S Aurora González-Vidal,Ahmed Mohamed Abdelgawad,Lat Conference proceedings 2022 The Editor(s) (if a

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书目名称Internet of Things
副标题5th The Global IoT S
编辑Aurora González-Vidal,Ahmed Mohamed Abdelgawad,Lat
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Internet of Things; 5th The Global IoT S Aurora González-Vidal,Ahmed Mohamed Abdelgawad,Lat Conference proceedings 2022 The Editor(s) (if a
描述This book constitutes revised selected papers from the refereed proceedings of the 5th The Global IoT Summit, GIoTS 2022, which took place in Dublin, Ireland, in June 20–23, 2022. .The 33 full papers included in this book were carefully reviewed and.selected from 75 submissions. They were organized in topical sections as follows: ioT enabling technologies; ioT applications, services and real implementations; ioT security, privacy and data protection; and ioT pilots, testbeds and experimentation results..
出版日期Conference proceedings 2022
关键词5G/B5G/6G; Ambient Intelligence; artificial intelligence; computer networks; computer security; computer
版次1
doihttps://doi.org/10.1007/978-3-031-20936-9
isbn_softcover978-3-031-20935-2
isbn_ebook978-3-031-20936-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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IntellIoT: Intelligent IoT Environmentses of next generation IoT applications and highlight associated challenges for future research. We further present the IntellIoT framework that comprises the required components to address the identified challenges.
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An Interoperable Framework for Heterogeneous IoT infrastructure to Unlock Data Value semantics that create closed ecosystems. To address above mentioned interoperability issue, we proposed an IoT interoperability framework to support different layers of heterogeneous sensors, edge devices, on-premise platform and even cloud platforms, so as to unlock data value.
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Sleep Stage Detection on a Wearable Headband Using Deep Neural Networksral network. We then apply transfer learning to the sleep classifier with data extracted from our device. Overall, we were able to successfully create a model with data from the medical device and obtain a 81.19% sleep stage classification accuracy.
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Assessing Efficiency Benefits of Edge Intelligencesally compare deep learning based on energy consumption measurements for inference and deep learning, on recognition gradients, and on number of classes. Sustainability is assessed with deep learning lifecycle efficiency and life cycle recognition efficiency metrics that include the number of times models are used.
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Conference proceedings 2022in topical sections as follows: ioT enabling technologies; ioT applications, services and real implementations; ioT security, privacy and data protection; and ioT pilots, testbeds and experimentation results..
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