Skip to search boxSkip to navigationSkip to main content

The role of big data analytics in industrial Internet of Things

  • National University of Computer and Emerging Science
    ,
  • Kyung Hee University
    ,
  • Khalifa University of Science and Technology
    ,
  • King Saud University
    ,
  • Swinburne University of Technology
    ,
  • Cardiff University
Research Output: Contribution to journal Article Peer-review

Abstract

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed, serving as future research directions.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 247-259 (13 pages)

Journal (Volume, Issue Number)

Future Generation Computer Systems (Volume 99)

Publication milestones

  • Accepted/In press - 08/04/2019
  • Published - 29/04/2019

Publication status

Published - 29/04/2019

ISSN

0167-739X

External Publication IDs

  • Scopus: 85065055289