Surface-level path loss modeling for sensor networks in flat and irregular terrain
Bibliographic record
Abstract
Many wireless sensor network applications require sensor nodes to be deployed on the ground or other surfaces. However, there has been little effort to characterize the large- and small-scale path loss for surface-level radio communications. We present a comprehensive measurement of path loss and fading characteriztics for surface-level sensor nodes in the 400 MHz band in both flat and irregular outdoor terrain in an effort to improve the understanding of surface-level sensor network communications performance and to increase the accuracy of sensor network modeling and simulation. Based on our measurement results, we characterize the spatial small-scale area fading effects as a Rician distribution with a distance-dependent K-factor. We also propose a new semi-empirical path loss model for outdoor surface-level wireless sensor networks called the Surface-Level Irregular Terrain (SLIT) model. We verify our model by comparing measurement results with predicted values obtained from high-resolution digital elevation model (DEM) data and computer simulation for the 400 MHz and 2.4 GHz band. Finally, we discuss the impact of the SLIT model and demonstrate through simulation the effects when SLIT is used as the path loss model for existing sensor network protocols.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".