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Record W1819264509 · doi:10.1002/ett.2909

PBMP: priority‐based multi‐path packet routing for vehicular ad hoc network system in city environment

2014· article· en· W1819264509 on OpenAlexaff
Wei Kuang Lai, Chih-Kun Tai, Tin‐Yu Wu, Alagan Anpalagan, Jian Zhi Chen

Bibliographic record

VenueTransactions on Emerging Telecommunications Technologies · 2014
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceComputer networkEqual-cost multi-path routingNetwork packetRouting (electronic design automation)Path (computing)Intersection (aeronautics)Geographic routingLink-state routing protocolSource routingRouting protocolDistributed computingEngineeringTransport engineering

Abstract

fetched live from OpenAlex

Abstract In vehicular ad hoc networks, efficient routing mechanisms are needed for data transmission to adapt to rapidly changing topology. Therefore, in this paper, we propose a priority‐based multi‐path packet routing strategy based on geographic routing. The goal is to achieve high delivery ratio and low end‐to‐end delay. By collecting real‐time traffic flow information, we derive a connectivity probability function to select road intersections efficiently for transmission and to reduce the path's sensitivity to vehicle movements. In addition, the data loads are also referred to assist with intersection selections and avoid packet congestions. Furthermore, packets are classified with three priority levels for different transmission strategies. To enhance reliability, multiple paths are constructed to achieve high probability of robust receptions. Simulation results show that priority‐based multi‐path has significant performance improvement in comparison with other existing geographic routing protocols. Copyright © 2014 John Wiley & Sons, Ltd.

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: Methods · Consensus signal: none
Teacher disagreement score0.785
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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
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

Citations6
Published2014
Admission routes1
Has abstractyes

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