Droppable Wireless Mesh Network for Intelligent Mine Rescue System
Patrick Duane, Sihua Shao, Mostafa Hassanalian, Vasilis Androulakis, Hassan Khaniani, Pedram Roghanchi · 2023
In the event of a mine emergency, the inherently unstable environment poses significant risks and challenges to rescue efforts. The use of intelligent systems employing robots to aid in mine rescues has emerged as a promising approach. However, the communication infrastructure within mines is often compromised or insufficient to handle the network traffic demands of these systems following an incident. Consequently, there is a need for a temporary, deployable communication network capable of supporting both time-sensitive environmental monitoring (e.g., toxic or flammable gases) and high-throughput data transmission, such as video or 3D mapping. In this paper, we propose a wireless mesh network (WMN) featuring droppable nodes as a solution to address this challenge. We assess the signal-to-noise ratio required for a stable and continuous stream of 3D LiDAR mapping data and evaluate its feasibility in a mine setting using collected data from a mine tunnel. Furthermore, we employ the OMNeT++ network simulator in conjunction with the experimental data to investigate the potential of a larger network to fulfill the requirements for LiDAR streaming. Our findings indicate that a WMN within a mine environment has the potential to support high-volume data transmission, such as video and LiDAR streams, thus enhancing the effectiveness of intelligent mine rescue systems.