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Record W1915092061 · doi:10.1155/2015/307031

Effective Transmission Coverage Area-Based Link Dynamics Characterization of VANET in Highway Scenario

2015· article· en· W1915092061 on OpenAlexaff
Chunfeng Liu, Oliver Yang, Gen Li, Yantai Shu

Bibliographic record

VenueInternational Journal of Distributed Sensor Networks · 2015
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Ottawa
FundersNational Natural Science Foundation of China
KeywordsComputer scienceLink (geometry)Queueing theoryVehicular ad hoc networkTransmission (telecommunications)Routing (electronic design automation)Link budgetMobility modelComputer networkTelecommunicationsWirelessWireless ad hoc network

Abstract

fetched live from OpenAlex

This paper uses the concept of effective transmission coverage area as a model for the derivation of analytic expressions in order to characterize the dynamic statistics of link lifetime, new link arrival rate, new link interarrival time, link breakage interarrival time, and so forth. Extensive simulations have been undertaken to verify the derived analytical expressions via generated mobility traces. Results demonstrate that the proposed analytical model can characterize the dynamic statistics well. Furthermore, the mathematical results of expected link life and expected new link arrival rate are provided to be used in analyzing the network connectivity. Combining with queueing theory, the upper bound connectivity of a VANET is obtained. This work thus provides a fundamental guideline on designing new mobility models, new routing protocols, and the corresponding performance analysis in VANET.

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)0.002
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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