# A Model for Working Environment Monitoring in Smart Manufacturing
## 2 Related Work  
A literature review indicated that the aim of condition monitoring in smart manufacturing environments is directed toward ensuring efficient operation and maintenance of manufacturing equipment and toward the monitoring of conditions that impact worker health and safety. [PERSON] et al. [19] presented two industrial cases related to use of wireless sensor networks (WSN) for improving worker safety and health conditions. These two cases relate to collision avoidance (between workers and fork-lift trucks, or between any types of industrial vehicles) and working at height. WSN motes were used for detecting the indoor position and speed of workers and industrial vehicles. Fixed nodes within the factory were used for localization of mobile vehicles and workers. The core of the system was the communications backbone that transmits information to a remote control center [32]. MEMSIC IRIS motes working in the 2.4 GHz frequency band were used as WSN motes, while sensor data were processed by an onboard Atmega328P, which was preloaded with an Arduino bootloader. A barometer included in the PCB was used for the height measurements. Two tests were conducted in the TRW automotive factory in Pamplona (Spain). The system deployment included fixed and mobile WSN motes, a coordinator node, WiFi routers, and a WiMAX connection to a remote control center. The results revealed that WSNs supported by a heterogeneous wireless backbone can be used within industrial settings for preventing accidents and improving the safety and health of workers. [PERSON] et al. [12] proposed an ontology-based intelligent smart condition monitoring for Industry 4.0 manufacturing. The ontology defines domain knowledge related to condition monitoring, which is used in an intelligent condition monitoring system (ICMS) for prognostics tasks in manufacturing processes. The domain ontology contains the following modules: (1) manufacturing module: contains the knowledge relevant to production activities and tasks; (2) context module: represents the concepts relevant for the context, such as activity, identity, time, and location; and (3) condition monitoring module: formalizes the knowledge related to machinery prognostics and health management. Condition monitoring was performed with an ICMS with three layers: (1) physical space layer: responsible for collecting data from the manufacturing environment; (2) cyber physical interface: responsible for storing and processing the collected sensor data; and (3) cyber space layer: responsible for decision making related to machine operation and maintenance. The proposed ontology and ICMS were evaluated for a conditional maintenance task of bearings in rotating machinery. A five-layer architecture for health monitoring and management of manufacturing workers was presented in [10]. The layers in the proposed architecture were: (1) data collection layer: responsible for data sensing, acquisition, and processing; (2) network and communication layer: responsible for end-to-end connectivity of the health monitoring applications; (3) data processing layer: responsible for necessary calculations and statistical measurements on received aggregated data; (4) management layer: responsible for dealing with results and outcomes of data processing; and (5) service layer: responsible for end-user connectivity for hospitals, emergency departments, ambulances, police stations, and insurance companies. The system implementation enabled monitoring of physical conditions in the working environment (metallic dust, noise, and temperature), as well as the physical condition parameters of manufacturing workers (heart rate, blood pressure, and body temperature). The evaluation of the proposed system was made from the perspective of insurance and compensation for manufacturing workers. The implementation results revealed that the proposed architecture contributed to the improvement of health monitoring and worker health in adverse working conditions. [PERSON] et al.