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Record W1966000693 · doi:10.1109/glocom.2014.7036779

Location information dissemination scheme for RFID-based distributed localization systems

2014· article· en· W1966000693 on OpenAlexafffund
Lobna M. Eslim, Hossam S. Hassanein, Walid M. Ibrahim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceDisseminationScheme (mathematics)Leverage (statistics)Overhead (engineering)Computer networkDistributed computingMobile deviceContext (archaeology)Information DisseminationWorld Wide WebTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

The availability of location information is essential for context- and location-aware services, which are typically provided by a large number of applications. RFID systems are extensively utilized to provide localization service typically through a centralized and coordinated approach. In this paper, we propose a distributed location information dissemination scheme using heterogeneous uncoordinated mobile RFID readers with the support of inexpensive "memory spots". In the proposed scheme, mobile RFID readers localize passive RFID-tagged objects and leverage the available memory spots in a given smart environment to disseminate location information. Mobile RFID readers use such memory spots to store tag locations and queries enabling exchange location information without the need for direct communication among each other. We study the behavior of the proposed scheme and compare its performance with a typical pull dissemination strategy through extensive simulations using ns-3. Our results indicate that the proposed scheme outperforms the typical pull dissemination strategy in terms of localization delay and average overhead under different dynamicity settings.

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 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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.475

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 science0.0000.000
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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations2
Published2014
Admission routes2
Has abstractyes

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