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Big data analytics challenges to implementing the intelligent Industrial Internet of Things (IIoT) systems in sustainable manufacturing operations

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  • Qi, Quansong
  • Xu, Zhiyong
  • Rani, Pratibha

Abstract

The overlap of the growth of big data with that of the Internet of Things (IoT) is well reflected by the dramatic surge in the use of devices connected to IoT and the exponential rise in data consumption. Huge numbers of sensors and devices deployed in the industry sector have resulted in the production of massive big data in the industrial IoT (IIoT). The literature consists of many studies conducted on big data analytics (BDA) and IIoT, though it still lacks research into the most important challenges to the growth of intelligent IIoT systems. This paper presents an innovative integrated method using the multi-objective optimization on the basis of a ratio analysis plus the full multiplicative form (MULTIMOORA) and criteria interaction through inter-criteria correlation (CRITIC) under the q-rung orthopair fuzzy sets (q-ROFSs). In the proposed method, CRITIC is used to calculate the attribute weights, whereas MULTIMOORA is utilized to estimate the ranking of options on the q-ROFSs. Then, a case study is accomplished on the challenges of BDA in the process of developing intelligent IIoT systems in the environment of industry 4.0. Furthermore, comparative and sensitivity analyses are conducted on the proposed approach to prove the capability of the developed framework in the prioritization of intelligent IIoT systems.

Suggested Citation

  • Qi, Quansong & Xu, Zhiyong & Rani, Pratibha, 2023. "Big data analytics challenges to implementing the intelligent Industrial Internet of Things (IIoT) systems in sustainable manufacturing operations," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:tefoso:v:190:y:2023:i:c:s0040162523000860
    DOI: 10.1016/j.techfore.2023.122401
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    References listed on IDEAS

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