A Deployable Solution for Indoor Tracking of Workers in Construction Sites through Bluetooth Low Energy Technology
Why this work is in the frame
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Bibliographic record
Abstract
Real-Time Locating System (RTLS) using Bluetooth Low Energy (BLE) technology is becoming common to assist construction managers in making rational decisions pertinent to productivity monitoring and safety management on construction sites. However, there are still several challenges in deploying BLE-based RTLS on job sites. This paper proposes an RTLS explicitly designed for construction by satisfying requirements for widespread on-site adoption, including cost efficiency, scalability, and accuracy. The main contributions of this study are (i) substituting commonly used BLE receivers with BLE beacons; (ii) proposing a modular infrastructure placement strategy; (iii) developing localization algorithms using triangulation technique; (iv) post-processing the worker’s estimated locations. The experimental results show a localization error of 0.56 (m) and 0.64 (m) in a middle-size indoor space when the target is dynamic and static, respectively. This level of accuracy is an improvement compared to that reported in the literature and can be considered appropriate for most worker tracking applications on construction job sites. Moreover, replacing traditional BLE receivers that are smartphones or devices that require electrical wiring with battery-powered BLE beacons, and using the modular infrastructure placement strategy improved the RTLS scalability and efficiency in implementation cost and power consumption. The impact of environmental conditions, such as the weather availability of metal and construction equipment, on the developed RTLS’s performance, must be studied in future works.
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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 | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it