Open Source System for Monitoring Wireless Outdoor Networks in Mining

Description

The S11D mining complex in Brazil, situated in Pará state, extracts 20 million tons of iron each quarter. Connecting via a standard 802.11b/g/n wireless network is crucial for mine operations across vast distances. A local team employs a network monitoring tool called Ekahau Site Survey to guarantee the proper functioning of the network. However, due to the harsh terrain and dangerous nature of S11D operations, this tool fails to gather data from all points of interest, leading to interpolated maps that may not accurately represent the network's overall quality. In this work, we propose a platform that can be attached to mobile machines during operations to automatically collect network parameters such as channelization, RSSI, latency, packet loss, and bandwidth without human intervention. Using this network data, we generate an RSSI map using Kriging, which the local team can use. Comparison tests conducted in the laboratory and the field demonstrate that the platform performs similarly to Ekahau in capturing network parameters, ensuring its use in day-to-day operations for mapping.

Steps to reproduce

1. The gps_data.py script collects wireless network monitoring data at specific geographic coordinates. For this study, considering the plant, data were collected at 33 points of interest, with four distinct files generated per location, resulting in a total of 132 files containing detailed network parameter information. Additionally, a single result file was produced to summarize the overall monitoring status. 2. The gps_output_compiler.py script receives a directory of monitoring output data as input and compiles information based on the validity status indicated in the results file. The output is a .csv file that lists the latitude, longitude, altitude, and RSSI values for each valid data point (plant_coordinates.csv and stacker_coordinates.csv). 3. The distances.py script normalizes and transforms geographical coordinates into distance values relative to a reference point, generating a three-column .csv file (distance_based_coordinates.csv and distance_based_coordinates_stacker.csv). The columns are labeled x and y, representing the latitude and longitude coordinates, respectively, while the z column corresponds to the RSSI values. 4. The csvMerger.py script concatenates the plant and stacker coordinate files (distance_based_coordinates.csv and distance_based_coordinates_stacker.csv), merging them into distance_based_coordinates_all.csv. This combined file is used as a single input for generating the Kriging maps. 5. The kriging.py script processes the input files and applies the Kriging method to generate two maps: kriging_plant.png, based on distance_based_coordinates.csv, and kriging_stacker_plant.png, based on distance_based_coordinates_all.csv.

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