## Literature Review
### IoT-Based Sensor for Monitoring System
The collected health, environmental, medical, location, and meteorological data were used to classify individuals as possibly infected or uninfected. The results showed that the proposed system could be used to detect infected individualsas well as to send a warning alert to the nearest governments and healthcare clinics to prevent further outbreaks. Finally, [PERSON] et al. developed an IoT-based sensor utilizing Arduino and Raspberry Pi to enhance eHealth care [59]. The performance of the proposed sensor was compared with that of a personal computer. The results confirmed that the proposed IoT-based sensor was suitable for scalable eHealth systems. Several studies have been conducted in the manufacturing industry and showed significant advantages from IoT based sensors in improving working conditions, preventing erroneous designs, providing fault diagnosis and quality prediction, and helping managers with better decision making. [PERSON] et al. developed an IoT-based sensor to measure the air quality inside a factory [11]. Temperature, humidity, CO\({}_{2}\) level, dust, and odor sensor data were collected and transmitted via wireless communication. Based on the experimental results, the proposed system is robust enough, able to accurately measure the environmental condition in the factory in real-time, and is expected to help managers maintain an optimum working environment for the workers inside the factory. [PERSON] et al. proposed an environmental monitoring system based on low-cost IoT sensors for preventing errors during the design phase in additive manufacturing [12]. The sensors were used to gather temperature and humidity data. The study revealed that knowledge of environmental conditions could help prevent errors during the design phase in additive manufacturing. [PERSON] et al. utilized IoT sensors to collect data for the fault diagnosis of mine hoisting equipment [13]. The study revealed that IoT sensors can help provide complete diagnosis data as well as improve diagnosis results. [PERSON] et al. proposed a framework by utilizing IoT and machine learning to predict the quality of a product and optimize operation control [14]. Metal casting was used as a real-case implementation of the proposed system. The proposed system was able to effectively predict the quality of the metal casting and efficiently improve the operation control. Finally, [PERSON] et al. proposed the integration of sensors and the SCADA system for implementation of the fourth industrial revolution framework [15]. Experimental results confirmed the feasibility of the proposed system, which is expected to help managers during the migration of legacy systems to the Industry 4.0 framework. The number of IoT-based sensors and other related components is increasing significantly. The adoption of IoT in manufacturing enables the transition from traditional to modern digitalized manufacturing. As the number of devices collecting sensor data in manufacturing increases, the potential for new types of applications that can handle the input of large amounts of sensor data such as big data technology also increases. [PERSON] et al. developed a conceptual framework by integrating big data technology in IoT, which is expected to support critical decision making [60]. By utilizing big data processing, the enormous amount of data collected by many heterogeneous sources (sensor devices) can be handled and presented in an efficient manner, thus they can assist managers with better decision making.