Multiple Model BLE-based Tracking via Validation of RSSI Fluctuations under Different Conditions
Bibliographic record
Abstract
Of particular interest to this paper is indoor positioning via integration of information fusion, localization, and tracking technologies with Internet of Things (IoT) devices equipped with sensing, processing, and Bluetooth Low Energy (BLE) communication capabilities. In particular, the objective is development of advanced signal processing and machine learning solutions to micro-locate and track a person within a delimited physical space (e.g. building) using BLE locating infrastructure installed within this space. In this regard and as the first step, the paper focuses on evaluation and validation of RSSI fluctuations under different environmental conditions. Therefore, the first goal of the paper is to implement a Location-Based Services (LBS) platform consisting of two main sub-systems, i.e., acquisition sub-system, and the Fusion Centre (FC). The second goal of the paper is to test and validate effects of different parameters on the RSSI values and on tracking performance. Based on real experiments, the implemented LBS platform shows potential capabilities for incorporation of different fusion frameworks and providing accurate tracking results.
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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".