Cognitive Automation for Smart Decision Making in Industrial Internet of Things

Geetanjali Rathee, Farhan Ahmad, Razi Iqbal, Mithun Mukherjee

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Classical automated schemes in the industrial Internet of Things (IIoT) are challenged by the problems related to huge record storage and the way they respond. To properly manage the manufacturing settings, cognitive systems aim to find a way to efficiently adapt their actions based on uncertainty management and sensory data. However, due to the lack of existing IT integration, cognitive systems are not fully exploited by organizations. In this article, we provide a novel decision-making process in industrial informatics during information transmission, manufacturing, and storing records through the simple additive weighting and analytic hierarchy process. The proposed mechanism is analyzed and validated rigorously using various sensing and decision-making parameters against a baseline solution in industrial parameter settings. The simulation results suggest that the proposed mechanism leads to 87% efficiency in terms of better detection of the sensor node, decision-making, and alteration of transmitted data during analyses of product manufacturing in the IIoT.

Original languageEnglish
Article number9154511
Pages (from-to)2152-2159
Number of pages8
JournalIEEE Transactions on Industrial Informatics
Issue number3
Early online date3 Aug 2020
Publication statusPublished - Mar 2021
Externally publishedYes


  • Industrial Internet of Things
  • Cognitive Automation
  • Data Sharing
  • SAW
  • AHP
  • Decision making process
  • simple additive weighting (SAW)
  • industrial Internet of Things (IIoT)
  • decision-making process
  • Analytic hierarchy process (AHP)
  • cognitive automation
  • data sharing

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications


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