New Machine Learning Hybrid Models to Lower Position Errors for Bluetooth-Based Indoor Localizations
Bibliographic record
Abstract
In recent times, there has been a significant development of critical IoT-based applications in the field of indoor localization (e.g., locating an asset or person). The utilization of Machine Learning (ML) algorithms to enhance accuracy in the presence of interference has generated considerable interest among researchers in this domain. This research paper introduces two hybrid models, namely the Asymmetric and Symmetric Line-Shifting hybrid models, which integrate novel line shifting algorithms with ML classifiers and regressors. The primary objective of these models is to reduce positioning errors. By shifting learned and predicted Reference Points using ML algorithms, the models calculate centroids that closely align with the targets' locations. Through real-world analysis, it was demonstrated that the new models achieved lower Root Mean Square Error (RMSE) and improved R-squared values compared to those obtained using the Constant Line Shifting algorithm (e.g., 52% and 29%, respectively). Moreover, both models outperformed the version without Reference Point shifting in terms of RMSE and R-squared values (e.g., 76% and 273%, respectively). Consequently, the proposed hybrid models are deemed accurate and reliable for many indoor localization applications.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".