CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Xiang Fei, Nazaraf Shah, Nandor Verba, Kuo-Ming Chao, Victor Sanchez-Anguix, Jacek Lewandowski, Anne James, Zahid Usman

Research output: Contribution to journalArticle

6 Citations (Scopus)
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Abstract

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber–physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in Cloud and Fog architectures for better fulfilment of the requirements of mission criticality and time criticality arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a Cloud and Fog architecture.

Original languageEnglish
Pages (from-to)435-450
Number of pages16
JournalFuture Generation Computer Systems
Volume90
Early online date5 Jul 2018
DOIs
Publication statusPublished - Jan 2019

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Fog
Learning systems
Feedback
Decision making
Monitoring
Sensors

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Future Generation Computer Systems , Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Future Generation Computer Systems, (2018)DOI: 10.1016/j.future.2018.06.042

© 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Analytics
  • Cloud computing
  • Cyber–physical systems (CPS)
  • Edge computing
  • Fog computing
  • Machine learning

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey. / Fei, Xiang; Shah, Nazaraf; Verba, Nandor; Chao, Kuo-Ming; Sanchez-Anguix, Victor; Lewandowski, Jacek; James, Anne; Usman, Zahid.

In: Future Generation Computer Systems, Vol. 90, 01.2019, p. 435-450.

Research output: Contribution to journalArticle

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