## 9 Conclusions  
In this paper, we introduced an initial effort towards an IoT-based early warning, event detection, and information sharing integrated platform that has the potential to provide comprehensive safety and improved services in underground coal mines. Our system integrates underground mine environment monitoring, miner location tracking, and cloud services through the application program interfaces for data analytics. This system has been successfully implemented at the Hassan Kishore coal mine, an operating underground coal mine. Arduino sensor modules were selected to measure five parameters i.e., temperature, humidity, CO\({}_{2}\), CO, and CH\({}_{4}\) at different locations in the underground mine with more than 95% and greater than 99% of accuracy and efficiency, respectively. The results obtained from the system implementation clearly indicated the status of the mine in terms of a \"_Mine Warning Index (MWI)_\". The contingency table of _MWI_ is based on the threshold limit values of gases, temperature, and humidity. Implementation in actual conditions confirmed some of the claims in a recent literature survey that covered toxic gases, seamless integration, and event detection, namely that a simple temporal statistical based ambient intelligence approach is far better than conventional qualitative decision-making. K-means clustering with Euclidean distance measurements and silhouette values are efficient in detecting abnormal events in an underground coal mine environment. Attributed and spatio-temporal correlated event detection algorithms provide early warnings to prevent disasters by analyzing sensor data. The RSS range-based centroid algorithm is efficient for tracking miners, but the accuracy is low. A web-based lightweight remote monitoring and control system with a REST style is efficient and consumes less memory when providing access to underground sensors.  
To test the effectiveness of the proposed integrated system, a number of artificial events were created and the system was tested under various actual underground coal mine conditions. These tests  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **Our System** & [25] & [15] \\ \hline Open-source & ✓ & ✓ & \(\times\) \\ \hline Cost-effective & ✓ & \(\times\) & N/A \\ \hline Cloud computing & ✓ & \(\times\) & \(\times\) \\ \hline Multi-parameters and multiple aspects of safety & ✓ & partial & Single aspect of safety \\ \hline Easy user interface & ✓ & ✓ & \(\times\) \\ \hline Real application & ✓ & \(\times\) & \(\times\) \\ \hline Energy efficient & N/A & ✓ & N/A \\ \hline \hline \end{tabular}  
* Applicable, \(\times\) not applicable, N/A not available.  
\end{table}
Table 10: Distinguishing features of our proposed system in comparison to some other studies.  
showed that the system is robust. However, there are a few challenges which demand the consideration for full-fledged implementation of this system in underground coal mines. These challenges are the standardization of processing, the unforgiving environment of the mine, management of multisource data, data privacy, the complex nature of IoT-based platforms, and autonomous sensing. To detect outliers using a K-means method, the cluster centers needs to be defined prior to use. Overall, this system was helpful for solving the problems of accessibility, serviceability, interoperability, and flexibility for developing an \"Internet of Things\" for coal mines.  
The authors would like to thank the anonymous reviewers for their constructive comments and suggestions on improving this paper.  
[PERSON] conceived the idea, provided the materials and helped with the technical aspects of system design. [PERSON] carried out the implementation, collected, managed and run different analysis on the related data, and wrote manuscript.  
The authors declare no conflict of interest.