# A Model for Working Environment Monitoring in Smart Manufacturing
## 3 Model of a System for Monitoring Working Environments in Smart Manufacturing
### Indoor Dust Control Simulation  
Since the middleware layer is intended for data storage, processing and analysis, it is interesting to examine the functionality of this layer. In this section, the example of the data analyses module (8) presented in Figure 2 is given. This module assumes the usage of \"light\" or \"heavy\" sensor nodes with an integrated dust sensor. Analysis of the data collected by the sensors was done on data related to dust particle distribution and numerous harmful gases, so in the real system it is a mixture of harmful substances. Of course, dust is not the only type of pollution, but the presence of dust is one of the key parameters of the environment considered here. Bearing in mind that we were not able to deploy and use the sensors in a real environment for a long time, we used a simulation, with the aim of proving the validity of the concept. However, the simulation used the floor plan of a real plant of a paper haberdashery company, in fact one room with two machines for the preparation of paper materials for further processing. These machines generate large amounts of dust and other harmful substances, so this part of the plant is suitable for being simulated, in terms of generating and distributing harmful (primarily dust) particles. A high concentration of dust particles in the work environment is dangerous for workers. Therefore, their concentration in the work environment is regulated by legal acts. A schematic view of the floor plan is shown in Figure 3.  
There are two machines, A and B, located symmetrically in the room's center, and which are dust pollution emitters; a large amount of dust particles are generated during the operation of these machines. Dust sensors are intended to be part of \"heavy\" nodes, which are stationary and located on the walls of the room in protected places. There are 10 wall-mounted sensor nodes (1-10, Figure 3), and two vents V1 and V2, which can be operated separately. First of all, we studied some of the previous research on indoor air quality assessment. The assessment of indoor air quality (IAQ) was of interest, which differs somewhat from the measurement and assessment of outdoor air quality index (AQI). IAQ is the air quality within and around buildings and structures, and it relates to the health and comfort of people [35,36]. As in [36], IAQ is significantly affected by three primary factors: outdoor air quality, human activity, and construction materials (as well as equipment and furniture). The operation of ventilation systems also has a significant influence on IAQ. According to [36,37] the main indoor air pollutants include NOx, some volatile or non-volatile organic compounds (VOCs), SO2, O3, CO, PMs, radon, toxic metals, and microorganisms. PMs are carbonaceous particles associated with organic chemicals and reactive metals. By size, PMs are classified into PM10 of diameter <10 \(\upmu\)m, PM2.5 of diameter <2.5 \(\upmu\)m, and PM0.1 of diameter <0.1 \(\upmu\)m. Indoor PM sources include particles that migrate from the outdoor environment and particles generated by cooking, fossil fuel combustion activities, smoking, machine operation, and various indoor activities. The list of common indoor pollutants, as well as IAQ guidelines and standards can be found in [36]. Similarly to [36], this research deals with devices that mimic the functionalities of mammals' olfaction system. These devices are based on IoT, and are called \"electronic noses\" (E-noses). They consists of four basic components:  
* A multi-sensor array,
* Software with digital pattern recognition algorithms,
* An artificial neural network (ANN) or similar,
* Reference library databases. These devices have attracted great interest, because of their low-cost and portability. In [38] a tiny air-quality monitoring system is presented, in order to monitor indoor air  
Figure 3: The floor-plan of a paper haberdashery company. quality, and with CO and PM sensors installed. This real-time device proved to be capable of assessing IAQ values.