## 2 Study Area  
To apply the system developed in this study, the Taeyoung EMC Samdo mine (Figure 1a), located in Dogye-eup, Samcheok-si, Gangwon-do, was selected as the study area, and a preliminary experiment was conducted within at Samcheok Campus, Kangwon National University. A digital surface model (DSM) representing the elevation of the study area is shown in Figure 1b. Locations with high and low altitudes are marked in red and green, respectively. To acquire topographical information, 30 photos of the target area were taken with a Mavic 3 DJI drone, and a DSM was produced using pix4 Dmapper software. The orthoimage through ArcGIS Pro of ESRI, which was produced using pix4 Dmapper, is shown in Figure 1c. Areas not captured by drones were visualized using the ArcGIS Pro basemap. The result of 3D visualization using ESRI ArcScene for the easy understanding of the topography of the target area is shown in Figure 1d. The study area is surrounded by high-altitude mountainous terrain except for the haulage road. The Samdo mine, established in April 1993, mainly produces limestone for iron and steel production. The upper limestone layer of the Pungchon formation in the Samdo mine is mined by applying the board-and-pillar method. Approximately 1.5 million tons of limestone are mined annually at the Samdo mine, and haulage trucks are operated daily; therefore, haulage roads and mining environments should be kept safe. The system developed in this study was applied along a haulage road from the office to the mine adit.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sub-Field**} & \multirow{2}{*}{**References**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Real-Time**} & \multirow{2}{*}{**Positioning**} & \multirow{2}{*}{**Aim of Study**} \\  & & & **Sharing** & & & \\ \hline \multirow{6}{*}{\begin{tabular}{c} Road conditions \\ \end{tabular} } & [PERSON] and [PERSON] [20] & 2022 & No & No & \begin{tabular}{c} Road condition monitoring using the acceleration \\ measurement function of the black box \\ \end{tabular} \\ \cline{2-5}  & [PERSON] et al. [21] & 2022 & No & No & \begin{tabular}{c} Road condition monitoring using smartphone \\ built-in motion sensors \\ \end{tabular} \\ \cline{2-5}  & [PERSON] et al. [22] & 2022 & No & No & \begin{tabular}{c} Road condition monitoring using smartphone \\ built-in motion sensors \\ \end{tabular} \\ \cline{2-5}  & [PERSON] et al. [23] & 2022 & No & Yes & \begin{tabular}{c} Road condition monitoring using smartphone \\ built-in motion sensors \\ \end{tabular} \\ \cline{2-5}  & [PERSON] et al. [24] & 2021 & No & Yes & \begin{tabular}{c} Road condition monitoring using smartphone \\ built-in motion sensors \\ \end{tabular} \\ \cline{2-5}  & [PERSON] et al. [25] & 2020 & No & No & \begin{tabular}{c} Road vibration and noise monitoring using a \\ smartphone for an ambulance \\ \end{tabular} \\ \hline \multirow{6}{*}{Air quality} & [PERSON] et al. [28] & 2021 & Yes & No & \begin{tabular}{c} Air quality monitoring using PM\({}_{2.5}\) and CO\({}_{2}\) \\ sensors with a smartphone application \\ \end{tabular} \\ \cline{2-5}  & [PERSON] et al. [29] & 2022 & Yes & No & \begin{tabular}{c} Air quality monitoring using various sensors with \\ a smartphone application \\ \end{tabular} \\ \cline{2-5}  & [PERSON] et al. [30] & 2022 & Yes & No & \begin{tabular}{c} Gas leakage detection and alarm system with a \\ smartphone application \\ \end{tabular} \\ \cline{2-5}  & [PERSON] et al. [31] & 2022 & Yes & No & \
\begin{tabular}{c} Industrial monitoring using temperature, \\ humidity, and smoke sensors \\ \end{tabular} \\ \cline{2-5}  & [PERSON] et al. [32] & 2022 & Yes & Yes &
\begin{tabular}{c} Air quality monitoring using various sensors and \\ GPS for vehicles \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of IoT applications to monitor road conditions and air quality.