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Record W2383378078

Routing Protocol Based on Location for Vehicular ad Hoc Networks

2011· article· en· W2383378078 on OpenAlexvenueno aff
Yi Wu

Bibliographic record

VenueMicrocomputer applications · 2011
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceComputer networkRetransmissionVehicular ad hoc networkOptimized Link State Routing ProtocolWireless Routing ProtocolRouting protocolZone Routing ProtocolDynamic Source RoutingWireless ad hoc networkAd hoc wireless distribution serviceEnhanced Interior Gateway Routing ProtocolNetwork packetTelecommunicationsWireless
DOInot available

Abstract

fetched live from OpenAlex

How can timely and stable transmission of data packets from source to destination that are the most important performance of routing protocol in vehicular ad hoc network(VANET).Because of the vehicle moves quickly,network segmentation phenomenon is serious in vehicular ad hoc network(VANET),that the network structure changes rapidly,network routing maintenance costs increase.A kind of rapid and stable network layer packet transmission routing protocol of VANET,based on the vehicle on running route,vehicle speed direction and the density of road vehicles is presented.This protocol is the improvement of DSR mechanism.The simulation results show that the presented protocol can achieve higher successful retransmission ratio,fewer routing hops and throughput and lower transmission delay.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.014
GPT teacher head0.222
Teacher spread0.209 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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