# A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things +
## 4 Specific Applications and Performance Analysis
#### Applications of the IIoT Data  
We use the scheme proposed in this paper and the two previous schemes to collect data from large-scale mechanic industrial sites [49]. Through many test analyses in different scenarios, it is concluded that the cost of accessing industrial sites to collect data using an embedded industrial gateway is three to four times that of the scheme proposed in this paper, and the effectiveness of the data is not advantageous. The method of data collection using a data terminal unit is similar in cost and data validity to the scheme in this paper. Its disadvantage is that it is extremely complex to develop, can only collect a single dataset, and has poor privacy.  
In order to further measure the performance of the system proposed in this paper, we applied the scheme to the industrial site of a large manipulator and collected data for several large manipulators. We collected a total of 890 acceleration and sound data generated during normal operation. We can improve the algorithm for collecting datasets to identify potential problems and make necessary adjustments to the arm.  
In the first test, we focused solely on the data collected by the sound sensor and designed an algorithm called the acoustic wave width (AWW) algorithm, which is based on the working frequency of the manipulator. The AWW algorithm calculates the total wavelength of the machine's working interval and counts the acoustic convolution data that reach a predetermined threshold. In the first test dataset, there were 890 Operator Work Events (MWEs). There were 7 error detections and 17 missed detections in the AWW algorithm, and its accuracy was 97.30%.  
To improve the accuracy and stability of working state recognition for large-scale manipulators, we incorporated acceleration data to assist in detection in addition to the sound sensor. This approach eliminates the noise interference caused by collisions between surrounding arms. To solve the asynchronous problem of the two sensors, we designed a finite state machine detection (FSMD) algorithm. The FSMD algorithm matches the asynchronous sound characteristics with the acceleration characteristics by dividing the present-state (PS) and sub-state (SS). This process filters out any mismatched noise signals [50].  
The results of the second test on the previous dataset show that the large manipulator worked 890 times normally. The AWW algorithm combined with the FSMD algorithm had 5 error detections and 12 missed detections, and its accuracy rate was 98.09%, which has significantly improved. The state table of the finite state machine algorithm is shown in Table 1.  
In the third experiment, we used the feature fusion network designed in this paper to process the dataset. There was one error detection and three missed detections in the preprocessed dataset, and the accuracy rate was 99.56%, which improved significantly. By observing and comparing the data of three times, it is clear that the sonic algorithm can effectively detect the working data of a large manipulator. However, the accuracy can be significantly improved by adding the finite state machine algorithm. At the same time, the feature fusion network designed in this paper can further improve the recognition accuracy after preprocessing the dataset. Therefore, the system can successfully realize real-time monitoring and fault data detection of the industrial site. In addition to the tests mentioned above that demonstrate the feasibility of the system, we have also tested it in an industrial setting using a large shooting arm and reached consistent conclusions. The system device has a high accuracy rate, and it can be further improved through the implementation of specific algorithms and special networks. We plan to conduct experiments with it and compare more data, as well as develop more algorithms for different scenarios.