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Abstract
ABSTRACT The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. Smartness, however, remains a substantial challenge for IoT applications. Recent advances in networked sensor technologies, computing, and machine learning have made it possible for building new smart IoT applications. In this paper, we propose a novel approach: the Neural Knowledge DNA based Smart Internet of Things that enables IoT to extract knowledge from past experiences, as well as to store, evolve, share, and reuse such knowledge aiming for smart functions. By catching decision events, this approach helps IoT gather its own daily operation experiences, and it uses such experiences for knowledge discovery with the support of machine learning technologies. An initial case study is presented at the end of this paper to demonstrate how this approach can help IoT applications become smart: the proposed approach is applied to fitness wristbands to enable human action recognition.
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Authors (7)
-
Haoxi Zhang
- CUIT, China Cyber security
-
Fei Li
- CUIT, China Cyber security
-
Juan Wang
- CUIT, China Cyber security
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Zuli Wang
- CUIT, China Cyber security
-
Lei Shi
- CUIT, China Cyber security
-
Cesar Sanin
- Ubniversity of Newcastle, Australia School of Engineering
Cite as
Full text
- Publication version
- Accepted or Published Version
- License
- Copyright (2020 Taylor & Francis Group, LLC)
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
CYBERNETICS AND SYSTEMS
no. 51,
pages 258 - 264,
ISSN: 0196-9722 - Language:
- English
- Publication year:
- 2020
- Bibliographic description:
- Zhang H., Li F., Wang J., Wang Z., Shi L., Sanin C., Szczerbicki E.: The Neural Knowledge DNA Based Smart Internet of Things// CYBERNETICS AND SYSTEMS -Vol. 51,iss. 2 (2020), s.258-264
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2019.1705545
- Verified by:
- Gdańsk University of Technology
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