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Record W2066100235 · doi:10.1109/icdim.2013.6693996

Reliable communication protocol for inter-vehicular network

2013· article· en· W2066100235 on OpenAlexaff
Yasir Malik, Stefan D. Bruda, Bessam Abdulrazak, Usman Tariq

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversité de SherbrookeBishop's University
Fundersnot available
KeywordsVehicular ad hoc networkComputer scienceComputer networkBroadcast radiationWireless ad hoc networkReliability (semiconductor)Protocol (science)Vehicular communication systemsAtomic broadcastBroadcast communication networkBroadcasting (networking)Computer securityTelecommunicationsWireless

Abstract

fetched live from OpenAlex

This paper presents the reliable broadcast protocol for Inter-Vehicular networks. Traffic fatalities are one of the leading causes of death in the world. Vehicular communication technology such as (vehicular ad hoc networks) has emerged as one of promising technology in improving the safety of drivers, passengers, and pedestrians on the road. In vehicular ad hoc networks majority of the messages are broadcasted to announce the state of a vehicle to it neighbors (e.g. sending emergency warning messages, transmitting state information, etc.). However broadcast in vehicular ad hoc networks cause a broadcast storm problem. In this paper, we present new broadcast protocol that address the broadcast problem and improves the reliability of receiving broadcast messages in vehicular ad hoc network. Simulation results shows the efficiency of proposed protocol.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.015

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 communication0.0010.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.012
GPT teacher head0.242
Teacher spread0.229 · 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 designNot applicable
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

Citations4
Published2013
Admission routes1
Has abstractyes

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