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.

中文翻译：

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工业物联网智能决策的认知自动化


工业物联网 (IIoT) 中的经典自动化方案受到与大量记录存储及其响应方式相关的问题的挑战。为了正确管理制造环境，认知系统旨在找到一种基于不确定性管理和传感数据有效调整其行为的方法。然而，由于缺乏现有的IT集成，认知系统没有被组织充分利用。在本文中，我们通过简单的加法加权和层次分析过程，在信息传输、制造和存储记录过程中提供了一种新颖的工业信息学决策过程。使用各种传感和决策参数对照工业参数设置中的基准解决方案对所提出的机制进行了严格的分析和验证。仿真结果表明，在工业物联网产品制造分析过程中，所提出的机制在更好地检测传感器节点、决策和传输数据更改方面可实现 87% 的效率。