MinerRouter : Effective Message Routing using Contact-graphs and Location Prediction in Underground Mine

Location-based distributed communication in underground mines has been a hard problem to solve due to unreliable centralized architecture such as leaky feeder systems, high attenuation, and the unavailability of GPS signals. Delay Tolerant Networks (DTN) enable decentralized message routing using the store-carry-forward method that can help in creating situational awareness needed to handle emergency and disaster scenarios. The ability to predict where the DTN nodes (miner) might have been at/are headed to (with respect to the mine regions and pillars) at different times, combined with contact-based routing and intelligent handling of buffer, can be used for better delivery of messages. To this end, we propose a hybrid approach, called MinerRouter, that uses Random Forest (RF) and Graph Autoencoder (GAE) - Long Short Term Memory (LSTM) model to exploit the short- and long-mobility patterns of miners, respectively for faster message/content dissemination. Our simulations show that MinerRouter outperforms Opportunistic RF (RF), Opportunistic Contact Graph Routing (O-CGR), MaxProp, SemiBlind, and Blind routing protocols in terms of the delivery ratio of messages received, message latency, buffer occupancy Rate, communication overhead costs, and hop count.