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Record W1964089333 · doi:10.1108/14714171211215967

RFID deployment protocols for indoor construction

2012· article· en· W1964089333 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueConstruction Innovation · 2012
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsSoftware deploymentComputer scienceRadio-frequency identificationSystem deploymentIdentification (biology)Systems engineeringRange (aeronautics)Real-time computingProtocol (science)Computer securityEngineeringSoftware engineering

Abstract

fetched live from OpenAlex

Purpose Location awareness is essential to decisions pertinent to tracking and progress reporting, as well as to safety in construction projects. However, these applications have been mostly limited to the outdoor environment, where satellites for positioning information are in view. Recent studies on indoor location sensing systems are now overcoming this limitation and offering significant potential on construction practices, and radio frequency identification (RFID) is the most widely utilised technology for such application. The purpose of this paper is to address a wide range of protocols that are vital for RFID deployment for indoor construction. The paper identifies deployment settings to provide data acquisition with higher accuracy for indoor location sensing in construction. Design/methodology/approach A computational platform was designed to assess and evaluate the most suitable condition related to deployment of reference tags in construction. In this platform, a number of protocols and parameters are presented and their performance is evaluated. The evaluation scenarios were performed on a construction facility in Montreal, as well as in a controlled lab environment. The computational platform used for the study comprises the use of passive reference RFID tags and K Nearest Neighbour algorithm (K‐NN) for course‐grained detection of target's location and its classification into pre‐defined zone areas. Findings The studies resulted in a number of observations, findings, and lessons learned for RFID deployment in construction. The results indicate that: the speed of the reader is in direct relationship with the detection error rate; zone configuration effectiveness is in direct relationship with the deployed RFID read‐range; error rate on the controlled environment is significantly lower than rates in construction site; and stationary reader performs better than moving reader. Originality/value The paper's findings are expected to be of considerable value to researchers and practitioners involved in the utilisation of RFID technology in construction. The paper provides a set of helpful protocols for the deployment of passive RFIDs for automated onsite management of construction operations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.788
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.272
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it