True Rays Model Integration in OMNeT++ for IIoT Network Planning in Underground Mines
This work develops a new module in the OMNeT++ simulator incorporating and validating the True Rays propagation model for underground environments. The integration enables the planning and evaluation of Industrial Internet of Things deployments of wireless sensor networks (WSNs) in mining scenarios. The tool can read the mine blueprints and deploy nodes at specified locations. It also defined the environment and network communication parameters for network coverage and performance analysis, allowing an understanding of how the WSN behaves in the underground mine before its deployment. The simulation predicts variables such as received power and path losses, optimizing sensing coverage and connectivity while reducing the need for site surveys. A large number of simulations using blueprints from real mines show that the signal propagation measurements are consistent with the theoretical models proposed in the state of the art. Experimental results were compared with a simulation WSN using LoRa, the maximum range obtained in simulation was 47 m, the measurements under real conditions in the underground only achieved 25 m of reach, but were limited at a threshold of -100 dBm to ensure connectivity and reliability of transmission among the nodes. As a result, the simulations and measurements performed better at the 433 MHz frequency, and the main contribution is a simulation tool simplifying planning IIoT networks in mining tunnels.
Authors
- Sandra Céspedes (ORCID: https://orcid.org/0000-0003-1686-2644)
- Eduardo Avendaño Fernández (ORCID: https://orcid.org/0000-0003-0910-8539)
- Óscar Javier Montañez-Sogamoso (ORCID: https://orcid.org/0000-0001-6363-3508)
- Cristian Suancha (ORCID: https://orcid.org/0009-0003-8420-3445)
- Fabian Medina (ORCID: https://orcid.org/0009-0005-5997-9828)
Institutions
- Pedagogical and Technological University of Colombia (CO)
- Concordia University (CA)
Publication Details
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1109/lacap63752.2024.10876249
- Primary Topic
- Advanced Image Fusion Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00