## 4 Results
### Experimental Wireless Transmission of Features Using Edge Processing  
Finally, the edge processing obtained in the experiments up to this point is implemented in a sensor device that supports LPWA communication, and only the feature values are aggregated via LPWA communication and visualised in a web application. First, we describe the edge processing procedure for the sensor device. Edge processing repeats the following steps in accordance with the testbed cycle. 1. Detection of outages between cycles
2. Measurement of uniform motion from left end to right end
3. Measuring stops at the right-hand end
4. Measurement of uniform motion from the right end to the left end
5. Calculation of features
6. Transmission of features  
In order to be able to respond either when the testbed stage is stopped at the left end or when it has already been cycled, the sensor device first detects the stop condition between cycles. This is determined by whether the norm of the three-axis acceleration measured at the sensor device falls within the norm of the ballscrew stop. If the norm at ballscrew stop is measured continuously for longer than the stop time at the right end, the ballscrew is judged to be in an inter-cycle stop state. If the norm is measured for a period longer  
Figure 20: Dispersion values in uniform motion from the right end to the left end. than the stopping time at the ballscrew stop, it is judged to be the start of a cycle and the measurement of uniform motion from the left end to the right end is started. As the time taken for uniform motion from the left end to the right end is constant, the fixed length data are measured and stored in memory. Next, after stopping the measurement during the stop at the right end, the measurement of the uniform motion from the right end to the left end is carried out with a fixed length. As the norm of the three-axis acceleration has been measured in the previous steps, these values are used to calculate the feature values. Here, the variance is calculated for the 1300 th sample to the 1400 th sample in the cycle waveform, i.e., the first 100 samples of the uniform motion from the right end to the left end. The calculation results are then sent to the gateway via LPWA communication as the feature values for this cycle. The packets sent by the sensor device include the device ID and the feature values. This allows the web server that receives the packet from the gateway to identify which sensor device the data comes from. Using the sensor device with the above edge processing implemented, data were collected on the testbed for one hour at a tightening torque of 11 N, followed by one hour at a tightening torque of 1 N. The data were visualised in a web application. The actual visualisation page of the web application is shown in Figure 21.  
In this graph, the dispersion values at a tightening torque of 11 N are displayed from 16:13 to 17:13 on 30 October 2023, and the dispersion values at a tightening torque of 1 N are displayed from 17:14 to 18:14. The graph shows that the dispersion values in the section with a tightening torque of 11 N tend to be larger than those in the section with a tightening torque of 1 N. However, the values vary from cycle to cycle, with some cycles having a dispersion value in the range of the dispersion value of the fastening torque 1 N when the dispersion value of the fastening torque 11 N is small, and some cycles having a dispersion value in the range of the dispersion value of the fastening torque 11 N when the dispersion value of the fastening torque 1 N is large. The value of the dispersion value of the tightening torque 1 N is the range of the dispersion value of the tightening torque 11 N. Here, it is considered that the cycles of tightening torque 11 N and tightening torque 1 N are discriminated by a threshold value. As a threshold value, the average of the dispersion values of each tightening torque interval is calculated and a value halfway between these averages is used.