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Record W2042109723 · doi:10.1109/wivec.2014.6953220

Demonstration of multi-channel medium access control protocol in vehicular power line communication (VPLC) using OMNeT++

2014· article· en· W2042109723 on OpenAlexaff
Zhengguo Sheng, Morgan Roff, Roberto P. Antonioli, Victor C. M. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceComputer networkChannel (broadcasting)Protocol (science)Power-line communicationNode (physics)MultiplexingAccess methodKey (lock)Channel access methodAccess controlMedia access controlRandom accessHidden node problemPower (physics)WirelessTelecommunicationsWireless networkEngineeringComputer security

Abstract

fetched live from OpenAlex

In-vehicle communication is becoming more important as the need to ensure reliable and efficient communications with the increasing number of x-by-wire applications. In order to well maintain in-vehicle communications, it is necessary to evolve protocols for managing communications and, in particular, for granting bus access for real-time applications. In this paper, we describe a demonstration of the multi-channel random access protocol that we have developed for VPLC using OMNeT++. The proposed solution uses a combination of time and frequency multiplexing and consists of two key features: (i) multiple access channels to prioritize transmissions and (ii) a distributed collision resolution algorithm that allows each node to compete for the use of its selected channel. The live demonstration illustrates the contention resolution procedure of in-vehicle communications with different service classes, shows simulation results to validate the advantages of the proposed protocol and provides useful guidelines for developing a robust contention-based protocol for vehicular power line communication systems.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.059
GPT teacher head0.333
Teacher spread0.275 · 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.

Study designSimulation or modeling
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

Citations1
Published2014
Admission routes1
Has abstractyes

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