# Development of Digital Device Using ZigBee for Environmental Monitoring in Underground Mines
## 6 Conclusions  
This study developed a low-power, low-cost module for monitoring vibration and dust density in an underground mine using an XBee module, accelerometer, and dust sensor. The manufactured module was installed along the drift of Samdo Mine located in Dogye-eup, Samcheok-si, Gangwon-do, and the vibration and dust density were measured before and after blasting. Therefore, we could collect data on changes in vibration and dust density, even at a distance of 400 m or more from the blasting point, and it was confirmed that data from multiple points could be simultaneously acquired even in an underground environment where connecting to an external communication network is difficult. After blasting, the dust density increased in all the modules. However, depending on the location where the module was installed, the dust density trend varied with time. In some modules, the stability of communication deteriorated slightly. To solve this problem, maintaining more-stable power or increasing the number of router modules is required. In addition, increasing the number of coordinators or adjusting the sampling rate may be considered for establishing a stable network. However, because this choice is directly related to the cost, various attempts are required to make the optimal choice. Additionally, we must consider installing a large-capacity battery or improving the battery efficiency of the module. To build a stable network, we must secure an LOS between the modules. Therefore, if 3D modeling and LOS analysis of underground mines are performed in advance, modules can be installed at more-effective points. If light detection and ranging (LiDAR) is used in the field, obstacles can be analyzed in real-time, and appropriate installation points can be identified. In a future study, we intend to analyze the optimal module-installation points for a complete network using LOS analysis. In addition, we aim to visualize the monitoring results in real-time through a dashboard to more intuitively understand the environment in an underground mine. If the acquired data are learned through machine learning technology and dangerous areas are identified in advance, this can help prevent potential risks in underground mines and improve the working environment. Because this study can be constructed with low power and cost, it can be applied to relatively small and poor underground mines and used as basic research for a safe environment.  
Conceptualization, S.-M.K.; Formal analysis, W.-H.L.; Methodology, S.-M.K.; Software, W.-H.L. and S.-M.K.; Writing--original draft, W.-H.L., H.K. and C.-H.L.; Writing--review and editing, S.-M.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by (1) Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (2020R1G1A1010789), and (2) Energy and Mineral Resources Development Association of Korea (EMRD) grant funded by the Korean government (MOTIE) (Data science based oil/gas exploration consortium).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DPM & diesel particulate matter \\ GPRS & general packet radio service \\ GSM & global system for mobile communications \\ IoT & Internet of Things \\ LiDAR & light detection and ranging \\ LOS & line of sight \\ MEMS & micro-electromechanical system \\ NFC & near-field communication \\ RF & radio frequency \\ WSN & wireless sensor network \\ \end{tabular}