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Record W2023858199 · doi:10.1109/icc.2013.6654769

iCAR: Intersection-based connectivity aware routing in vehicular ad hoc networks

2013· article· en· W2023858199 on OpenAlexaff
Nizar Alsharif, Sandra Céspedes, Xuemin Shen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer networkComputer scienceNetwork packetVehicular ad hoc networkRouting protocolIntersection (aeronautics)Wireless ad hoc networkOverhead (engineering)Routing (electronic design automation)TelecommunicationsWirelessEngineeringTransport engineering

Abstract

fetched live from OpenAlex

In this paper, we propose an intersection-based connectivity-aware routing protocol (iCAR) for vehicular ad hoc networks (VANETs) to enable infotainment and interactive applications, as well as multi-hop Internet access in urban environments. iCAR is a novel protocol that takes into consideration real-time vehicular traffic information and the experienced packet delivery delay per road, in order to improve the routing performance by dynamically selecting roads with a guaranteed connectivity and a reduced delivery delay. This is achieved by deploying a microscopic view of vehicles location to proactively estimate roads connectivity and the minimum link lifetime per road. Detailed analysis and simulation-based evaluations show that iCAR significantly improves the network performance in terms of packet delivery ratio and end-to-end delay with a negligible cost of communication overhead.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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

Citations77
Published2013
Admission routes1
Has abstractyes

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