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Record W1557908248 · doi:10.1002/wcm.2316

Integration of an IEEE 802.15.4 RFID network with mobile readers with a 802.11 WLAN

2012· article· en· W1557908248 on OpenAlexaff
Haleh Khojasteh, Jelena Mišić, Vojislav B. Mišić

Bibliographic record

VenueWireless Communications and Mobile Computing · 2012
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceComputer networkIEEE 802.11w-2009IEEE 802.11Wireless lanTelecommunicationsWireless

Abstract

fetched live from OpenAlex

In this paper, we propose a coexistence scheme that allows radio-frequency identification readers or sinks to collect data from radio-frequency identification tags or sensors that use IEEE 802.15.4 wireless personal area network standard and send it to the access point via an IEEE 802.11 wireless local area network. The proposed scheme uses time scheduling and bridging via readers to allow for simultaneous operation of heterogeneous wireless networks that operate in the industrial, scientific, and medical frequency band at 2.4GHz. A simple sleep management approach is developed that allows tags to reduce their power consumption as well as collision rate. To provide timely reaction to sudden changes in tag population or reader availability, we devise a scheme where the scheduling parameters are adjustable and readers are mobile. We evaluate the performance of our solution in four scenarios and confirm the flexibility of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.027
GPT teacher head0.286
Teacher spread0.259 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

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