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 paper, we provide a novel decision-making process in the industrial informatics during information transmission, manufacturing and storing records through the Simple Additive Weighting (SAW) and Analytic Hierarchy Process (AHP). The proposed mechanism is analyzed and validated rigorously using various sensing and decision-making parameters against a baseline solution. 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 IIoT.
- Industrial Internet of Things
- Cognitive Automation
- Data Sharing
- Decision making process