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Abstract
The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. The main reason behind this interest is the capabilities of the IoT for seamlessly integrating classical networks and networked objects, and hence allows people to create an intelligent environment based on this powerful integration. However, how to extract useful information from data produced by IoT and facilitate standard knowledge sharing amongst different IoT systems, are still open issues to be addressed. In this paper, we propose a novel approach, the Experience-Oriented Smart Things (EOST), that utilizes deep learning and knowledge representation concept called Decisional DNA to help IoT systems acquire, represent, and store knowledge, as well as share it amid various domains where it can be required to support decisions. We demonstrate our approach in a set of experiments, in which the IoT systems use knowledge gained from past experience to make decisions and predictions. The presented initial results show that the EOST is a very promising approach for knowledge capture, representation, sharing, and reusing in IoT systems.
Citations
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7
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Web of Science
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Authors (5)
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Haoxi Zhang
- Chengdu University Computer Science
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Fei Li
- Chengdu University Computer Science
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Juan Wang
- Chengdu University Computer Science
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Cesar Sanín
- Newcastle University Computer Science
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2016.1276771
- License
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Details
- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
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CYBERNETICS AND SYSTEMS
no. 48,
edition 3,
pages 162 - 181,
ISSN: 0196-9722 - Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Zhang H., Li F., Wang J., Sanín C., Szczerbicki E.: Experience-Oriented Intelligence for Internet of Things// CYBERNETICS AND SYSTEMS. -Vol. 48, iss. 3 (2017), s.162-181
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2016.1276771
- Verified by:
- Gdańsk University of Technology
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