A Lightweight Sensing Data Integrity Detection Method for the Industrial Internet of Things

Data sharing provides necessary data support for collaborative production among smart factories (SFs) and improves the production efficiency of the manufacturing industry. However, the quality of shared data cannot meet the needs of collaborative production of high-precision manufacturing processes among SFs, which hinders in-depth cooperation among SFs to a certain extent. In order to solve the above problems, we propose a lightweight sensing data integrity detection method for the industrial Internet of Things (IIoT) that performs integrity detection on the shared sensing data before the SF shares the data. Firstly, we design and implement a sensing data integrity feature extraction method to extract the integrity features of sensing data. Then, we design and implement a lightweight sensing data integrity detection method based on fuzzy support vector machines that detects the integrity of sensing data based on the integrity characteristics of sensing data. Finally, we conduct simulation experiments on the proposed method. Through theoretical and simulation experiments, it has been proven that compared with the traditional method, the accuracy of the proposed method is improved by 11.40%, the false alarm rate (FAR) is reduced by 79.49%, the missing alarm rate (MAR) is reduced by 71.80%, the detection time is reduced by 72.40%, and the energy consumption is reduced by 12.04%.