# A Model for Working Environment Monitoring in Smart Manufacturing
## 1 Introduction
According to [PERSON] and [PERSON] [11], these factors may be classified as: traditional (physical, chemical, and biological), current (repetitive work, manual material handling, and psychosocial hazards), and new (interaction with robots). The development of technology introduces new hazards and risks in the workspace, but the progress in smart technologies with new technological solutions can be effectively and promptly used for identifying and resolving risky conditions. In addition, condition monitoring in smart manufacturing settings can be used for implementing predictive maintenance of the production system, leading to increased system safety, availability, and reliability [12; 13], reduced unplanned downtime [14], as well as to the intelligent and flexible configurability and evolution of control architectures [15]. Condition monitoring through the collection of large amount of heterogeneous data in smart manufacturing enables more appropriate decision making and management of the whole enterprise, leading to intelligent condition monitoring systems [16]. While inquiring into occupational exposure to vibration in manufacturing environments, [PERSON] et al. [17] pointed out the importance of reliable measurements of working conditions, followed by documenting and tracking measured values in order to increase understanding and create knowledge about them. According to [PERSON] et al. [18] the monitoring and measurement of workplace environmental factors should be minimal, but sufficient to detect the potential risks for workers. Sensor-based systems integrated with information systems enable capturing large-scale and fine-grained data for effective real-time monitoring of equipment and manufacturing conditions [14]. The use of wireless technologies, especially wireless sensor networks (WSN) in industry is mostly focused on the operation, monitoring, and maintenance of processes and equipment, while aspects of the use in mobile sensing applications for monitoring and improving worker safety and health conditions is neglected [19]. According to [PERSON] et al. [20] various physical, chemical, or spatial characteristics of real objects and the working context within smart working environments can be sensed and measured easily, reliably, and at low cost by using recent sensor technologies. In addition, the use of contemporary technologies, such as body area networks, cloud computing, and smart clothing, big data, and business intelligence, or embedded and pervasive computing, allow the improvement of working condition monitoring and the quality of health services within manufacturing environments [10]. The use of sensor-based systems with fixed and mobile sensors for monitoring in industrial environments means the need for human involvement in monitoring is reduced, which minimizes the possibility of getting imprecise monitoring data and the exposure of workers to additional risks [21]. In modern times, the extensive application of modern technologies in industrial systems is extremely important [22; 23]. A variety of emerging wireless technologies [24] can be used in industrial systems, e.g., ZigBee [25], LoRaWAN [26], and Bluetooth low energy (BLE) [27]. The usage of low-cost open-source hardware (OSHW) and single board computers (SBC), such as Raspberry Pi [28], is also common in research papers and projects. Together with wireless technologies, open source hardware, and single board computers, the utilization of messaging protocols such as message queuing telemetry transport (MQTT) is equally integrated into the IoT, such as in the systems for supporting intelligent transport [29]. Additionally, ICT can enhance the experience of disabled persons, e.g., visually impaired ones in industrial facilities [30]. However, the implementation of ICT technologies in industrial environments is faced with various challenges, such as security, interoperability, alignment to the internal strategy, and the limitations of the technology to be implemented [31].