# A Model for Working Environment Monitoring in Smart Manufacturing
## 3 Model of a System for Monitoring Working Environments in Smart Manufacturing
### Model Description
The Arduino/Genuino and TI Launchpad platforms allow integration with the variety of sensors and other components. The advantage of using Arduino/Genuino and the enlisted TI boards lies in the huge supporting community and accessibility to a lot of code examples, wiring schemes, and low cost components. Sensor stations, particularly for application in smart industrial environments, can be different. The authors have had a positive experience in using sensors, such as: proximity sensors, dust sensors, and temperature (DHT11, DHT22, LM35, and TMP36), sound, photo, and a number of gas  
Figure 2: The architecture of the software components of the system. sensors (MQ-2, MQ-3, MQ-4, MQ-6, MQ-7, and MQ-135). For the testing platform the MQ-135 sensor was used in the majority of sensor nodes. This sensor can monitor gaseous ammonia (NH3), benzene, ethyl alcohol, and carbonic dioxide (CO2). The example of the data analyses module for dust monitoring, which is presented in the next subsection, assumes the usage of a dust sensor as a main component. Besides the sensors, the sensor nodes, can have OLED or LCD displays, LEDs, SD card loggers, real time clocks (RTC), actuators, and other components. The transport layer components were designed on the basis and experience of the project 6 th Sense, which started as a student project for the Texas Instruments competition TI Innovation Challenge 2016 [33]. The prototype sensor nodes evolved from the sensor nodes used in the 6 th Sense project, with the incorporation of emerging IoT protocols, such as MQTT [34]. The model described in this paper presents the fusion of these two models, with the hierarchical architecture and heterogeneous connectivity technology based on the first model, and with the MQTT protocol, and messaging bus oriented architecture based on the second model, and with changes in the sensor data packet format. The experiment and measurement setting, measured values, measurement tools, and method of analyses were completely different from previous studies, and performed with greater number of devices, and different message formats. The architecture of the software components of the system is shown in Figure 2. At the perception layer (I) WPAN, Wi-Fi, and Ethernet (wired) sensor nodes are deployed; the transport layer (II) contains microcontroller boards and sensor nodes with the ability to convert data transfer protocol from the wireless protocol from the perception layer, to IoT protocols applicable for the core of the system, in this case the MQTT protocol. The middleware layer (III) contains components such as Mosquito MQTT broker (6), which handles the messaging bus, DB read/write to data store module (7), and an analytics module (8); the application layer contains a user application API (9) and alarm module (10). The other components of the system placed in the middleware layer (III) were a module for database reading and writing (7), a module for data analyses (8), and a database. The database considered for the full scale system model was NoSQL MongoDB. For the purpose of building the small scale system testing platform, SQLite was selected, because of its simplicity and functionality. The application layer (IV) had an application API and alarm subsystem. The components were not included in the testing platform at this stage of research. The business layer was not included in the testing platform nor in the model of the system.