Toward Safer Mines: A Robust Wireless Sensor Network Placement for Real-World Underground Conditions
Bibliographic record
Abstract
Wireless sensor networks (WSN) are essential for monitoring operations, environmental conditions, and ensuring worker safety in underground mining environments. A key challenge in deploying these networks lies in achieving sufficient sensor coverage and reliable connectivity to enable early incident detection and timely response. While existing approaches primarily focus on deployment efficiency, this paper introduces a safety-driven optimization model that accounts for the complete underground mine structure and its specific operational conditions. The proposed algorithm minimizes the number of deployed nodes—reducing capital expenditure (CAPEX)—while meeting occupational safety standards by ensuring sensor coverage at critical points of interest and maintaining network connectivity. Compared to existing models, our method reduces the number of required nodes by up to 82%, while satisfying both k-coverage and k-connectivity requirements. Unlike conventional strategies based on idealized assumptions, our approach is tailored to the real-world conditions of Colombian underground mines. Simulation results demonstrate that achieving connectivity demands more nodes than coverage when both are evaluated under equivalent conditions. The algorithm was validated in two prototype mines, achieving <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> = 2 for both coverage and connectivity—ensuring that each point of interest is monitored by at least two sensors and connected via two link-disjoint communication paths. This redundancy enhances fault tolerance by providing backup for both sensing and communication. Finally, we introduce two new metrics for evaluating k-coverage and k-connectivity, designed to assess the robustness of WSN in underground mining and tunnel environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".