Fast WiFi access point localization and autonomous crowdsourcing
Bibliographic record
Abstract
The locations of WiFi access points (APs) are important for WiFi positioning, especially when a propagation model is used. However, AP localization is usually challenging in a new environment because it is hard to obtain parameters for the propagation model, such as the path-loss exponent without the presence of surveyed database. This paper introduces a novel crowdsourcing method for automatic AP localization and propagation parameters (PPs) estimation based on the navigation solution from the Trusted Portable Navigator (T-PN). The estimation for PPs and AP locations based on non-linear weighted least squares (LSQ) is carried out automatically when enough measurements are collected, and the results are recorded in the database for future use. The fast estimation method calculates the propagation parameters autonomously and adaptively to account for the dynamic indoor environment. The autonomous system will also reduce the labour and time costs for the pre-survey and maintenance of databases, as the crowdsourcing is always done in background processes on devices. The accuracy of AP localization is also estimated and recorded in the database, providing an important indicator when using the AP localization results. The performance of the proposed system is evaluated by both simulations and field tests, and the result shows that the average AP localization errors are less than 6 meters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".