Optimal sequential node deployment in underground mines with partial map discovery
Patrick Duane, Avery Christie, Sihua Shao, Vasilis Androulakis, Hassan Khaniani, Mostafa Hassanalian, Pedram Roghanchi · Tunnelling and Underground Space Technology · 2025
In the event of a mine emergency, the inherently unstable environment poses significant risks and challenges to rescue operations. Intelligent robotic systems have emerged as a promising solution to aid in such rescues, yet their effectiveness is often limited by compromised or insufficient communication infrastructure. To address this, there is a critical need for a temporary, deployable wireless communication network that can support both time-sensitive environmental monitoring (e.g., toxic or flammable gases) and high-throughput data transmission, such as video and 3D mapping. Unlike above-ground environments, radio wave propagation in underground tunnels is highly affected by multipath and waveguide effects. This research proposes a sequential mesh node deployment strategy based on a ray-tracing propagation model that accounts for these unique characteristics. The objective is to maximize end-to-end throughput across the network without prior knowledge of the post-disaster map. A simplified path loss approximation is also introduced as a computationally efficient alternative to full ray-tracing. Both models are integrated into the OMNeT++ simulation framework and compared to a baseline binary coverage method. Results show that throughput-aware node placement significantly improves spatial average throughput, and that the approximation method offers a high-performance, real-time deployment solution with reduced computational overhead.