Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61816
Title: Experience based knowledge representation for Internet of Things and Cyber Physical Systems with case studies
Contributor(s): Sanin, Cesar  (author)orcid ; Haoxi, Zhang (author); Shafiq, Imran (author); Waris, Md Maqbool (author); de Oliveira, Caterine Silva (author); Szczerbicki, Edward (author)
Publication Date: 2019-03
Early Online Version: 2018-02-10
DOI: 10.1016/j.future.2018.01.062
Handle Link: https://hdl.handle.net/1959.11/61816
Abstract: 

Cyber Physical Systems and Internet of Things have grown significant attention from industry and academia during the past decade. The main reason behind this interest is the capabilities of such technologies to revolutionize human life since they appear as seamlessly integrating classical networks, networked objects and people to create more efficient environments. However, enhancing these technologies with intelligent skills becomes an even more interesting and enticing scenario. In this paper, we propose and illustrate through a number of case studies how Decisional DNA, a multi-domain knowledge structure based on experience, can be implemented as a comprehensive embedded knowledge representation for Internet of Things and Cyber Physical Systems. Decisional DNA gathers explicit experiential knowledge based on formal decision events and uses this knowledge to support decision-making processes. The main advantages of using Decisional DNA are as follows: (i) offers a standardized form of the collected knowledge and experience, (ii) provides versatility and dynamicity of the knowledge structure, (iii) stipulates storage of day-to-day explicit experience in a single configuration, (iv) delivers transportability and shareability of the knowledge, and (v) provides predicting capabilities based on the collected experience. Consequently, test and results of the presented implementation of Decisional DNA case studies support it as a technology that can improve and be applied to the aforementioned technologies enhancing them with intelligence by predicting capabilities and facilitating knowledge engineering processes.

Publication Type: Journal Article
Source of Publication: Future Generation Computer Systems, v.92, p. 604-616
Publisher: Elsevier BV * North-Holland
Place of Publication: The Netherlands
ISSN: 1872-7115
0167-739X
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

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