The Neural Knowledge DNA Based Smart Internet of Things

Title
The Neural Knowledge DNA Based Smart Internet of Things
Publication Date
2020-02
Author(s)
Zhang, Haoxi
Li, Fei
Wang, Juan
Wang, Zuli
Shi, Lei
Sanin, Cesar
( author )
OrcID: https://orcid.org/0000-0001-8515-417X
Email: cmaldon3@une.edu.au
UNE Id une-id:cmaldon3
Szczerbicki, Edward
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
Taylor & Francis Inc
Place of publication
United States of America
DOI
10.1080/01969722.2019.1705545
UNE publication id
une:1959.11/61810
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.

Link
Citation
Cybernetics and Systems, 51(2), p. 258-264
ISSN
1087-6553
0196-9722
Start page
258
End page
264

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