A Reliable Lora based Tracking and Monitoring System for Underground Mines

The mining industry directly employs more than 426,000 workers across the Canada and contributed $97 billion to Canada’s GDP in 2017. However, mining workers are exposed to five-fold higher occupational hazards than the industrial average. Reliable underground communication is essential to alleviate incidents and escalate rescue operations. However, wireless communications in mines is a big challenge. Electromagnetic wave propagation is very poor in mines due to irregular confined shapes and rough walls. A recent wireless standard LoRa (Long Range) is promising in mine environments, due it’s, ultra-low power consumption, long range and deep penetration capabilities. This project aims to develop a unique and comprehensive monitoring and control system for underground mines using LoRa. It intends to develop a LoRa based tracking system that will use different range based techniques to estimate distance and apply advanced Machine Learning (ML) algorithms such as particle filtering, recursive neural networks or Kalman filtering on the estimated fingerprinting result to improve the accuracy. In addition, it will develop a medium access control (MAC) protocol to collect different mine data timely and ensure the Quality of Service (QoS) requirements. The outcome of this work will lead to significant improvements in miner safety and productivity.

Faculty Supervisor:

Xavier Fernando


Ahasanun Nessa


PBE Canada


Engineering - computer / electrical


Mining and quarrying


Ryerson University



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