# Applications of the Open-Source Hardware Arduino Platform in the Mining Industry: A Review
## 2 Arduino in Field Monitoring Systems
### Atmospheric Monitoring
The measured value was transmitted to a personal computer through the ZigBee module. However, this study was again conducted only in the laboratory. Although the types of sensors and communication modules are different, similar studies [36; 37] in the mining industry are gradually increasing. In underground or abandoned mines, it is generally difficult to supply power through electric wires; thus, these wireless sensor-based systems need to be operated with batteries. [PERSON] et al. [38] analyzed the battery life when sensors such as MQ4, MQ7, MQ9, and DHT22 were connected to an Arduino UNO. In addition, they proposed a method for optimizing the battery life by performing experiments, so that the system could be operated for as long as possible. In general, similar types of sensors are used to measure hazardous gases in mines, but new sensors were proposed and verified by [PERSON] et al. [39]. To detect hazardous gas more efficiently in mines, a nanogas-sensing device was proposed, based on a sol-gel method of depositing gold particles on ZnO, WO\({}_{3}\), TiO\({}_{2}\), and CuO thin films. As a result of experiments on CH\({}_{4}\), H\({}_{2}\), and N\({}_{2}\) gases, it was reported that the selectivity and sensitivity were improved as compared to existing sensors. The measured values were acquired wirelessly through the Wi-Fi module of the Node MCU (ESP8266), as programmed via the Arduino IDE. If applied to actual mines in the future, the results of this study will improve the accuracy of gas detection in mines. Most air quality monitoring studies have been conducted inside underground mines, but some studies have been conducted to measure air quality outside a mine. [PERSON] et al. [40] built a monitoring system using the Arduino Mega and a Wi-Fi module (ESP8266) to measure sewer gas discharged through a pipeline. DHT11 and DHT22 sensors were used to measure the temperature and humidity, and gases such as LPG, CH\({}_{4}\), CO\({}_{2}\), and CO were measured using MQ2, MQ4, MQ135, and MQ7 sensors, respectively. The measurement results are displayed on an LCD and transmitted to the IBM Bluemix, and execution of IBM's Open Cloud Architecture via Wi-Fi. [PERSON] and [PERSON] [41] developed a monitoring system for gases emitted from an environment polluted by mining. MQ2 sensors (for flammable gas), MQ7 sensors (for CO), MQ8 sensors (for hydrogen (H\({}_{2}\))), and MQ9 sensors (for LPG) were connected to an Arduino UNO, and LM35 sensors were used for temperature measurement. When a measured value reached a dangerous level, an SMS message was transmitted to a mobile phone through a global system for mobile communication (GSM) module. In practice, the main technology applied to gas monitoring outside a mine is not much different from that used in studies in underground mines. However, because communication outside the mine is simpler, it is possible to directly connect to a server, or to send a message from the monitoring system without the construction of a WSN. The studies discussed above considered approaches that transmit and monitor data in real time. However, it is difficult for mineworkers to respond immediately to dangerous situations based simply on monitoring the current conditions. Therefore, to respond immediately to an emergency, it is necessary to predict and prepare for situations. Some studies are designed to predict or respond to dangerous situations in advance through additional analysis based on monitoring data. [PERSON] and [PERSON] [42] connected MQ2 and MQ7 gas sensors to an Arduino UNO and installed them as local sensor nodes at different levels of a mine. Each sensor node was connected to an Arduino Mega-based main controlling system and transmitted data to the internet using a GSM module. When a gas sensor value exceeded a threshold, an alarm was triggered through a buzzer, and the gas level was predicted by applying a regression algorithm. The prediction results provided feedback to the system, including gas level fluctuations and predicted the gas inhalations of workers.