Department of Information Communication Engineering, Mokwon University, Daejeon-si 35349, Korea.
Sensors (Basel). 2022 Jun 20;22(12):4645. doi: 10.3390/s22124645.
Data created at industrial sites through industrial internet of things devices are now being processed automatically or in real-time in the industrial structure, due to the application of artificial intelligence technology to industrial sites. However, the expenses of autonomous or real-time data processing and steady data processing (analysis, prediction, prescription, and implementation) necessitate a new processing method. We propose a blockchain-based industrial internet of things information reinforcement model in this work that may reliably ensure the integrity of industrial internet of things data produced at industrial locations. The proposed model processes industrial internet of things data that may occur at endpoints at industrial sites into the blockchain by processing data generated by the same industrial internet of things device independently. As a result, the IIoT data sent to the industrial internet of things server can be evaluated more readily, and production accuracy may be enhanced. The proposed model optimizes industrial internet of things information linkage by stochastically reflecting the information based on attribute value frequency. By dynamically aggregating the related data of industrial internet of things information acquired as a seed through hierarchical subnets, the proposed model increases stability and accuracy. Furthermore, the proposed model may be used to enhance an organizations' operational efficiency (consulting and training, for example) and strategic decision-making by utilizing fundamental knowledge about items produced at industrial locations. Furthermore, the proposed model allows for information sharing and system connectivity between industrial locations, allowing for close collaboration between industrial internet of things features. As a result of the performance evaluation, the proposed model included an industrial internet of things sensor to the blockchain, eliminating the need for an extra function in the manufacturing process and reducing the time required to validate the integrity of industrial internet of things data. In addition, as a result of analyzing industrial internet of things data by an algorithm according to the number of simulated clouds, the accuracy of industrial internet of things information was improved by 2.5% to 3%, on average.
由于人工智能技术在工业现场的应用，现在通过工业物联网设备在工业结构中自动或实时处理在工业现场创建的数据。然而，自主或实时数据处理和稳定数据处理（分析、预测、处方和实施）的费用需要一种新的处理方法。在这项工作中，我们提出了一种基于区块链的工业物联网信息增强模型，该模型可以可靠地确保工业现场产生的工业物联网数据的完整性。所提出的模型通过独立处理同一工业物联网设备生成的数据，将可能在工业现场端点处发生的工业物联网数据处理到区块链中。因此，可以更轻松地评估发送到工业物联网服务器的 IIoT 数据，并且可以提高生产精度。所提出的模型通过基于属性值频率随机反映信息来优化工业物联网信息链接。通过通过分层子网获取的工业物联网信息的相关数据的动态聚合，所提出的模型提高了稳定性和准确性。此外，所提出的模型可以通过利用工业现场生产的物品的基本知识来提高组织的运营效率（例如咨询和培训）和战略决策制定。此外，所提出的模型允许工业现场之间的信息共享和系统连接，允许工业物联网功能之间的紧密协作。通过性能评估，所提出的模型将工业物联网传感器包含到区块链中，从而消除了在制造过程中需要额外功能的需求，并减少了验证工业物联网数据完整性所需的时间。此外，通过根据模拟云的数量通过算法分析工业物联网数据，工业物联网信息的准确性平均提高了 2.5%至 3%。
