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Record W2911913033 · doi:10.1109/lcomm.2019.2895819

An Efficient Dynamic Anti-Collision Protocol for Mobile RFID Tags Identification

2019· article· en· W2911913033 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.

Bibliographic record

VenueIEEE Communications Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsUniversity of Ottawa
FundersNational Key Research and Development Program of ChinaChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsComputer scienceCollisionRadio-frequency identificationIdentification (biology)Protocol (science)Reading (process)Computer networkCollision problemEmbedded systemReal-time computingComputer security

Abstract

fetched live from OpenAlex

Traditional anti-collision protocols for RFID tags identification assume that the tags and reader are stationary, i.e., no tag is coming in or leaving during tags identification. If not, the reader cannot ensure that the tags are all identified. However, in the mobile RFID system, the reader or tags or all of them are moving continuously, and the tags dynamically enter and leave the reading area. Therefore, traditional protocols are not suitable for the mobile RFID system. This letter presents a dynamic anti-collision protocol, called dynamic collision tree protocol, to be used in the mobile RFID identification system to identify RFID tags that dynamically move into and leave the reading area.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.318
Teacher spread0.305 · 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