# A Model for Working Environment Monitoring in Smart Manufacturing
## 6 Conclusions  
This paper presented a model for the monitoring of a working environment in smart manufacturing, and designed with the idea of including emerging technologies and contemporary approaches in building complex cyber-physical systems. A trained convolutional neural network on synthetic data is part of the proposed model, and is applicable to a variety of smart industrial monitoring scenarios. The model had to be easily developed in the form of a testing platform, with the usage of open-source hardware and software for platform development. The model and the platform were tested for their validity and applicability. The experiment showed that the usage of the described testing platform and up to 10 Arduino/Genuino based sensor nodes could simulate a wireless smart industrial monitoring network with maximum load of 174.11 packets per second. This traffic load is suitable for small- and middle-scale smart industrial monitoring systems. The results  
Figure 12: Communication flow between the server and the four sensor nodes with error packets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Packet Type** & \begin{tabular}{c} **Rate** \\ **0.62** \\ **pck/s** \\ \end{tabular} & \begin{tabular}{c} **\%** \\ **pck/s** \\ \end{tabular} & \begin{tabular}{c} **Rate** \\ **57.00** \\ **pck/s** \\ \end{tabular} & \begin{tabular}{c} **\%** \\ **pck/s** \\ \end{tabular} & \
\begin{tabular}{c} **Rate** \\ **174.11** \\ **pck/s** \\ \end{tabular} &
\begin{tabular}{c} **\%** \\ **pck/s** \\ \end{tabular} \\ \hline All Packets & 6410 & 100 & 32,046 & 100 & 123,006 & 100 & 641,430 & 100 \\ TCP Dup ACK & 51 & 0.8 & 123 & 0.38 & 65 & 0.05 & 312 & 0.05 \\ TCP Retransmission & 108 & 1.68 & 13 & 0.04 & 30 & 0.02 & 2 & 0.00 \\ TCP Spurious Retransmission & 52 & 0.81 & 120 & 0.37 & 65 & 0.05 & 312 & 0.05 \\ Publish Message & 3083 & 48.1 & 15,869 & 49.52 & 61,412 & 49.93 & 320,397 & 49.95 \\ ACK Message & 3083 & 48.1 & 15,877 & 49.54 & 61,413 & 49.93 & 320,400 & 49.95 \\ RST/ACK Message & 5 & 0.08 & 7 & 0.02 & 2 & 0.00 & 1 & 0.00 \\ SYN Message & 13 & 0.2 & 17 & 0.05 & 8 & 0.01 & 2 & 0.00 \\ FIN Message & 5 & 0.08 & 4 & 0.01 & 2 & 0.00 & 1 & 0.00 \\ Connect Command & 5 & 0.08 & 8 & 0.02 & 4 & 0.00 & 1 & 0.00 \\ Connect ACK & 5 & 0.08 & 8 & 0.02 & 4 & 0.00 & 1 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Packet load and statistics per packet type for four different scenarios. also showed that the system works properly, with a low percentage of packet errors, and without increasing errors with a rising number of transmitted packets per second. The testing platform proved to be efficient for: prototype building; a test bed for application development; a laboratory for engineering education at universities; a portable experimental set, which can be deployed at factories, university labs, and other specific facilities. The possible functionality of the middleware layer was shown on the example of hazardous particles management in an indoor environment; in this case with dust particles. The convolutional neural network was trained on synthetic data and served as an example and proof of concept. The validity of this setting was verifiable using a test set. The accuracy on the test set of 82% could be increased, first of all by changing the architecture of the neural network, and then by using a more extensive training set. The application of this solution could have an impact on resource savings: vents are turned on or off depending on the amount of dust particles and their dispersion. In future research it is necessary to examine the validity of such a solution in the case of other pollutants and their combinations. The future research goal will be the investigation of the applicability of the proposed model in different areas, such as equipment maintenance, inventory, goods, and human resources monitoring. The expansion of the model may be also directed into the process of integration of proposed models, with cloud, edge, and fog environments.