Linear Regression Algorithm against Device Diversity for Indoor WLAN Localization System
Bibliographic record
Abstract
In recent years, received signal strength (RSS) based indoor localization system using WLAN has attracted considerable attention. However, signal strength variations across diverse devices becomes a major problem in this system, especially in the crowdsourcing based localization system. In this paper, the linear regression algorithm is proposed to solve this problem automatically. First of all, the problem of device diversity and the adverse effects caused by this problem are analyzed. Then the intrinsic relationship between different RSS values collected by different devices is mined by the linear regression algorithm. The problem of device diversity will be handled by this algorithm. In crowdsourcing systems, when the major problem is eliminated, a unique radio-map can be created in the offline phase and the user's location can be estimated by a localization algorithm in the online phase. Experimental results show that the proposed method results in a higher reliability and localization accuracy.
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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 | 0.001 | 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".