A Deployable Solution for Indoor Tracking of Workers in Construction Sites through Bluetooth Low Energy Technology
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".