# Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0
## 4 Discussion  
To be able to evaluate the assembly system, i.e., to interpret the results obtained, it has to be noted, that the laboratory, as well as the assembling line itself, consists of metal elements such as tools, holders, cables, etc. ; therefore, in no circumstances could a homogeneous distribution be obtained. It has to be pointed out that near Anchor 4, an industrial SCARA robot arm was located, which presented a huge metal obstacle, and therefore had a huge influence on the distribution of the RSSI values (Figure 8d). In the middle of the assembling line, Anchor 5 was positioned. The assembling line was rather narrow; therefore, the distribution of the RSSI signal in the case of Anchor 5 was rather diverse (Figure 8e). Results presented in Figure 8 were mainly used to check whether the anchors were positioned well. Based on the presented data, one can conclude that the anchors were  
Figure 13: Accuracy of localization by using WkNN for different numbers of neighboring points. (**a**) \(k=1\); (**b**) \(k=2\); (**c**) \(k=3\). positioned well, and that the number of the anchors was satisfactory for the required coverage of the assembling line. Based on the presented data, it can be concluded that the RSSI values obtained at the industrial laboratory are distributed more evenly than at the test laboratory [28]. This was particularly true in the case of Anchor 5. Anchor 5 was located at the center of the assembly line, and due to relatively small size of the line, the signal distribution was rather even. In the case of Anchor 5, the majority of the measurements had an RSSI value 13, while the other values (from RSSI 8 to RSSI 15 were distributed over only 15 measurement points. In the case of Anchor 4, the majority of measurements had an RSSI value of 8, 9, and 10. The most probable reason for this result is the presence of SCARA robot in that area. In the case of Anchors 1, 2 and 3, the majority of measurement results are 7, 8, 9, and 10. The characteristic values of the anchors were almost the same. Only in the case of Anchor 5 there existed a difference. The reason for this deviation was that Anchor 5 was placed on the center of the assembly structure. When analyzing the cumulative distribution function for quality determination in the case of localization, it can be noted that the localization effectiveness was better if the CDF curve had a greater slope. In that case, the probability of localizing the points with lower error was higher. In this case, all curves showed excellent results. The localization accuracy for this case did not depend highly on the surroundings in which the wireless technology was used. For both surroundings, higher probability of lower errors was obtained using WkNN algorithms. In the case of the localization with \"unknown\" test points, the quality differed significantly compared to the quality in the case of localization with \"known\" test points. In this case too, the best result was obtained by using WkNN localization algorithm, with the probability of 0.5 for accuracies of 1 m. It can be noticed that the neural network provided better results in the case of the industrial laboratory. This can be explained having in mind that the resolution in the industrial laboratory was higher (the distance between measured RSSI values was 10 cm). The quality of the localization was better if WkNN algorithms were used and if the RSSI map was determined by measuring more points, i.e., using higher resolution. The influence of the anchor structure to the quality of the localization yielded best results when WkNN algorithm with three anchors was used. For example, if the accuracy was 1.5 m, the probability was 0.8 with three anchors compared with 0.6 for five anchors. In the case of using neural networks, no significant improvements were obtained by changing the number of anchors. Slightly better results were obtained by using five anchors. Influence of the laboratory structure to the quality of the localization was presented in Figure 12 for NN and Figure 13 for WkNN.