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Cross Layer Optimizations Of Integrated Networks In Underground Mines

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posted on 23.05.2021, 11:39 by Unknown Author
Establishing reliable communication is a difficult task in underground mines due to the extreme environmental conditions. This work proposes novel approaches and architectures to optimize the reliability, scalability and power efficiency of the communication networks in the environment of underground mines. Our research considers an integrated network architecture with three main elements that represent the common path of information flow from the surface of the mine to remote points deep in the underground. These three main elements are as follows: the backbone networks, the wireless channel of the confined spaces and the Wireless Sensors Networks (WSN). The objectives of enhancing the network reliability, scalability and power efficiency are globally considered for the entire integrated network. As a first step, the backbone network of the mine is optimized with intelligent algorithms for maximum stability, scalability and power efficiency. These objectives were achieved by introducing our novel Prediction-based Adaptive Equalization Algorithm (PAEA) and The Power-aware Adaptive Charging Schedule Algorithm (PACSA). Furthermore, this research introduced a novel wireless channel model to characterize the performance of the wireless systems in underground mines. The new proposed model, called “Mine Segmenting Wireless Channels Model”, is utilized by the wireless network as an added layer of intelligent network capacity. Lastly, with power efficiency and conservation in mind, the performance of the WSN in the underground is optimized for power efficiency, scalability and rapid development of applications. These objectives are achieved by introducing our novel Resources-Aware Sleep Scheduling Algorithm (RASSA) for the wireless sensor networks to provide an integrated platform, where new applications in mines can be rapidly developed to suit the operational requirements of the mine.





Doctor of Philosophy


Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type


Thesis Advisor

Wisam F. Farjow Kaamran Raahemifar Xavier N. Fernando