# Applications of the Open-Source Hardware Arduino Platform in the Mining Industry: A Review
## 2 Arduino in Field Monitoring Systems
### Atmospheric Monitoring
Some studies have detected anomalies from the measurement of dangerous gases. [PERSON] and [PERSON] [28] established an integrated system capable of gas monitoring, miner tracking, and cloud computing for the Hassan Kishore coal mine in Punjab, Pakistan (Figure 3). DTH11, MQ135, MQ9, and MQ4 sensors were connected to an Arduino Mega with a BLE shield to measure the temperature and humidity, CO\({}_{2}\), CO, and CH\({}_{4}\), respectively. Some stationary nodes for collecting data were connected to router nodes, and the collected data were transmitted to a base station capable of a global internet connection (Figure 4). In addition, by using a mobile node connected through BLE, a miner could send an emergency signal, and the location could be tracked. In this study, a \"mine warning index\" (MWI) was proposed for comprehensively analyzing the measured values, and abnormal events were detected using K-means clustering. As each stationary node had its own built-in algorithm, event detection was possible even if the node was disconnected from the network. [PERSON] et al. [43] constructed an electronic nose by arranging various gas detection sensors (MQ2, 3, 6-9) into one chamber. The sensors were connected to an Arduino UNO. The electronic nose inhaled and exhaled the gas using two vacuum pumps. After the measured values were transmitted through ZigBee communication, the types of gas were classified using principal component analysis (PCA) and linear discriminant analysis. As a result of classifying butane, CH\({}_{4}\), CO, and mixed gases using the measured values, the system demonstrated a classification accuracy of approximately 97%. As even one type of sensor can be sensitive to multiple gases, this analysis was expected to help in identifying harmful gases. Although the study was conducted for application in coal mines, it was only tested in the laboratory. [PERSON] and [PERSON] [44] also used PCA to identify the most influential gases among CH\({}_{4}\), CO, sulfur dioxide (SO\({}_{2}\)), and hydrogen sulfide (H\({}_{2}\)S) in an actual underground coal mine (Shangwan Coal Mine, Erdos, China). The PCA results were used as variables of an artificial neural network (ANN), to predict future air pollution. To obtain the data necessary for PCA analysis, some sensors (DHT11, MQ4, MQ9, MG811, MQ136, and MiCs2714) were connected to the Arduino UNO (Figure 5), and the data were transferred to the Azure machine learning (AML) platform using a ZigBee communication module (Figure 6). Although Arduino does not directly perform PCA or ANN analysis, an IoT system based on Arduino can interact with an AML platform in real time. To detect flammable and toxic gases, such as H\({}_{2}\), CH\({}_{4}\), and CO, [PERSON] et al. [45] used an ANN-based pattern recognition technique. An array of ZnO-based sensors was fabricated in the laboratory and was used to measure gas concentrations. A feed-forward back-propagation neural network was used for the classification of the gases at critical concentrations. The optimized ANN was implemented in an Arduino Due based on the Atmel (SAM3X8E ARM Cortex-M3 CPU) 32-bit ARM core microcontroller. The results of this experiment were verified under laboratory conditions. These studies monitored air quality and produced new information through additional analysis of the data. These results can help prepare for  
Figure 3: A brief description of an early warning Internet of Things (IoT)-based system in mines [29]. risks, by providing more useful information to mineworkers. However, most of them only interpret information regarding air quality and do not provide information on how to manage possible dangers. Figure 4: Layout of stationary nodes (SNs), router nodes (RNs), gateway, and moving anchor (MN) in the Hassan Kishore coal mine for enhanced safety [28]. Figure 5: (**a**) Front pictorial aspect of Arduino UNO, (**b**) back-side pictorial aspect of Arduino UNO, (**c**) 2.4 GHz ZigBee shield for Arduino, and (**d**) visual aspect of MQ4, MQ9, MG811, MiCs2714, and DHT11 [44].