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Record W1978052349 · doi:10.1109/icc.2012.6363838

Reliability of cluster-based multichannel MAC protocols in VANETs

2012· article· en· W1978052349 on OpenAlexaff
Khalid Abdel Hafeez, Lian Zhao, Zaiyi Liao, Bobby Ngok-Wah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer networkComputer scienceWireless ad hoc networkReliability (semiconductor)Vehicular ad hoc networkMedia access controlDistributed coordination functionAccess controlChannel (broadcasting)Cluster analysisControl channelWireless networkWirelessIEEE 802.11TelecommunicationsTelecommunications link

Abstract

fetched live from OpenAlex

The IEEE 802.11p or Wireless Access in Vehicular Environment (WAVE) has been adopted as a main technology for vehicular ad hoc networks (VANETs). Its Medium Access Control (MAC) protocol is based on the Distributed Coordination Function (DCF) of the IEEE 802.11 which has low performance and high collision rate especially when using a single channel. Therefore, many clustering-based multi-channel MAC protocols have been proposed to limit channel contention, provide fair channel access within the cluster, increase the network capacity by the spatial reuse of network resources and control the network topology more effectively. Most of these protocols did not study the optimized cluster parameters such as average cluster size, communication range within the cluster and between cluster heads, and the life time of a path. In this paper, we analyze the reliability and connectivity of a typical cluster-based MAC protocol in terms of these parameters.

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)0.004
metaresearch head score (Gemma)0.017
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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
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 science0.0010.001
Research integrity0.0010.001
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 head0.250
Teacher spread0.237 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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