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Record W2104791563 · doi:10.1109/ccece.2011.6030633

Performance evaluation for multicast transmissions in VANET

2011· article· en· W2104791563 on OpenAlexaff
Aslinda Hassan, Mohamed H. Ahmed, Md. Azizur Rahman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsSt. John’s Health Sciences CentreMemorial University of Newfoundland
Fundersnot available
KeywordsComputer networkComputer scienceMulticastUnicastGeocastVehicular ad hoc networkProtocol Independent MulticastXcastDistance Vector Multicast Routing ProtocolSource-specific multicastWireless ad hoc networkPragmatic General MulticastMobile ad hoc networkDistributed computingRouting protocolOptimized Link State Routing ProtocolNetwork packetWirelessTelecommunications

Abstract

fetched live from OpenAlex

Vehicular ad hoc network (VANET) is a network that is formed when vehicles with wireless transceiver have the need to communicate with each other. Although VANET shares some similarities with mobile ad hoc network (MANET), the dynamic nature of VANET has posed a challenge in designing routing protocols. This paper shows the performance evaluation between unicast and multicast routing protocols implemented in a vehicular environment that is based on Manhattan grid model for transmission between one sender and multiple receivers. Unlike multicast transmission in geocast routing, the multiple receivers for the paper scenario are not located in a specific geographic region. Performance is evaluated in term of average end-to-end delay, throughput, packet delivery ratio and routing overhead. The results reveal a consistent performance for multicast protocols as the number of receiving nodes increases during the transmission.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.070
GPT teacher head0.284
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2011
Admission routes1
Has abstractyes

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