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Record W2622223872 · doi:10.1109/wts.2017.7943531

Decentralized congestion control algorithm for vehicular networks using oscillating transmission power

2017· article· en· W2622223872 on OpenAlexafffund
Jordan T. Willis, Arunita Jaekel, Ikjot Saini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceSoftware deploymentTransmission (telecommunications)Vehicular ad hoc networkWirelessChannel (broadcasting)Network packetComputer networkIntelligent transportation systemNetwork congestionPower controlIEEE 802.11pPower (physics)Vehicular communication systemsControl channelReal-time computingWireless ad hoc networkTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Wireless Access in Vehicular Environments (WAVE) is a vehicle to vehicle (V2V) communications technology, which can be used to facilitate the deployment of safety applications for intelligent transport systems. Currently, the channel which is responsible for the awareness of other vehicles suffers from congestion even at moderate vehicular traffic densities. In this paper, we propose a novel method for adjusting the transmission power in a pattern which alternates between high and low powered transmissions. The proposed approach can be used both with and without existing rate control algorithms for V2V communication. Our simulation results demonstrate the advantages of the proposed approach in terms of both awareness levels and packet error rates.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.245
Teacher spread0.232 · 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 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

Citations27
Published2017
Admission routes2
Has abstractyes

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