Models For Locating RFID Nodes
Bibliographic record
Abstract
Localization of randomly distributed wireless sensor nodes is a significant and fundamental problem in a broad range of emerging civil engineering applications. Densely deployed in physical environments, they are envisioned to form ad hoc communication networks and provide the sensed data without relying on a fixed communications infrastructure. To establish ad hoc communication networks among wireless sensor nodes, it is useful and sometimes necessary to determine sensors positions in static and dynamic sensor arrays. As well, the location of sensor nodes becomes of immediate use if construction resources, such as materials and components, are to be tracked. Tracking the location of construction resources enables effortless progress monitoring and supports real-time construction state sensing. This paper compares several models for localizing RFID nodes on construction job sites. They range from those based on triangulation with reference to transmission space maps, to roving RFID reader and tag systems using multiple proximity constraints, to approaches for processing uncertainty and imprecision in proximity measurements. They are compared qualitatively on the basis of cost, flexibility, scalability, computational complexity, ability to manage uncertainty and imprecision, and ability to handle dynamic sensor arrays. Results of field experiments and simulations are also presented where applicable.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".