Experimental Study of Wireless Sensor Networks forIndoor Construction Operations
Bibliographic record
Abstract
Emerging wireless sensor networks (WSN) technology offers a great potential in supporting current project management practices. Deploying wireless sensor networks on construction sites can lead to significant time and cost savings by providing accurate and near-real-time data to project management personnel. Continuous monitoring of labor usage, materials placement and equipment performance provides valuable data for assessing progress of construction operations and assists in improving safety and security on job sites. Construction activities take place in outdoor and indoor environments, while Global Positioning System (GPS) is ideal solution for tracking outdoor activities; it is not applicable for indoor application due to the lack of line-of-sight to satellites signals. Therefore, GPS-less means of tracking is required in indoor environments. While several research efforts had been attempted to develop indoor positioning systems utilizing various wireless technologies, there is no clear understanding of which wireless technology performs better in indoor construction environment. This research aims to experiment and test wireless technologies to aid the selection of wireless sensor networks configuration in support of current practice of progress tracking at construction on job sites. This paper describes experimental study conducted to determine the effectiveness of wireless technologies for dynamic indoor resource position tracking. The experiments investigate the challenges of wireless technologies applications in indoor environments, in particular, Wireless Local Area Networks (WLAN), Bluetooth, Zigbee and Synapse SNAP. A total of 21 experiments were carried out and 1752 data sets were analyzed. The results showed that Synapse SNAP out-performed all other technologies. The findings of this study are expected to provide a reference for future research on selection of indoor positioning technologies.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".