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Record W2145505680 · doi:10.1109/vetecs.2009.5073289

A Continuous Communication Path Availability Analysis in Vehicular Ad Hoc Networks

2009· article· en· W2145505680 on OpenAlexaff
Mehdi Khabazian, Mustafa Mehmet Ali

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsConcordia University
Fundersnot available
KeywordsUnavailabilityComputer scienceWireless ad hoc networkPath (computing)Computer networkNode (physics)Markov processTopology (electrical circuits)Network topologyTransmission (telecommunications)Range (aeronautics)Mobility modelDistributed computingMathematicsTelecommunicationsEngineeringStatisticsWireless

Abstract

fetched live from OpenAlex

In this paper, we study the statistical properties of continuous communication path availability in vehicular ad hoc networks at the steady state. We assume a network of highways with arbitrary topology. The nodes arrive at the network through one of the traffic entry points following a Poisson process and move along a path according to a mobility model. We model the number of clusters in the node population along a path as a Markovian birth-death process. This model enables us to determine the distribution of the number of clusters and continuous communication path availability and unavailability times as functions of mobility and traffic arrival parameters in the path. The numerical results show that the mean path availability duration varies logarithmically with the transmission range. In practice, an adaptive transmission range may be needed to have very high path availability.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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