Enhanced Distance Estimation in Wireless Sensor Networks Using an Extended Path Loss Model for Underground Environments
Bibliographic record
Abstract
Accurate distance estimation is a cornerstone for reliable localization in Wireless Sensor Networks (WSNs), particularly in the challenging conditions of underground mining environments. Traditional localization approaches relying on logarithmic path loss models often fail to adequately capture the unique propagation characteristics of these settings, leading to inaccuracies. This study introduces an enhanced distance estimation framework leveraging an extended path loss model tailored to underground environments. The proposed model incorporates non-linear attenuation dynamics through additional parameters, enabling it to reflect the complex propagation phenomena inherent to mining tunnels. Using real-world Received Signal Strength (RSS) data collected in a controlled mining environment, we optimized the model parameters via non-linear fitting and quantitatively validated its performance against the classical logarithmic model. Results show that the extended model significantly reduces the Normalized Root Mean Square Error (NRMSE) by over 30 % on average, achieving a tighter distribution of errors with over 90 % of simulations yielding NRMSE values below 0.11. This work demonstrates that precise localization requires context-specific propagation models to capture the unique dynamics of underground environments. By providing a robust framework for distance estimation, our work paves the way for improved reliability in WSN applications critical to safety and operational efficiency in underground mining.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".