Probabilistic Estimation of Location Error in Wireless Ad Hoc Networks
Bibliographic record
Abstract
Many ad hoc network applications rely on nodes having accurate knowledge of their geographic locations. However, inherent in all localization systems is a degree of error in computed positions, which can compromise the accuracy and efficiency of location dependent applications and protocols. We propose a method in which nodes estimate the amount of error present in their derived positions with a certain probability. A linear scaling function is applied to the the calculated theoretical lower bound on localization variance, given by the Cramér-Rao Lower Bound (CRLB), so that it closely matches the variance of a given localization system. Probabilistic methods then use this variance function to estimate localization error. Coefficients for the scaling function were derived through simulations, and the proposed method was used to successfully estimate localization error in a multitude of randomly generated network topologies. Once known, estimates can be integrated into location dependent schemes to improve on their robustness to localization error.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".