Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61858
Title: Experience-Oriented Intelligence for Internet of Things
Contributor(s): Zhang, Haoxi (author); Li, Fei (author); Wang, Juan (author); Wang, Zuli (author); Sanin, Cesar  (author)orcid ; Szczerbicki, Edward (author)
Publication Date: 2017
Early Online Version: 2017-03-02
DOI: 10.1080/01969722.2016.1276771
Handle Link: https://hdl.handle.net/1959.11/61858
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 allowing 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 among 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. Decisional DNA motivation stems from the role of deoxyribonucleic acid (DNA) in storing and sharing information and knowledge. 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.

Publication Type: Journal Article
Source of Publication: Cybernetics and Systems, 48(3), p. 162-181
Publisher: Taylor & Francis Inc
Place of Publication: United States of America
ISSN: 1087-6553
0196-9722
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

Files in This Item:
1 files
File SizeFormat 
Show full item record

SCOPUSTM   
Citations

7
checked on Nov 23, 2024
Google Media

Google ScholarTM

Check

Altmetric


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.