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Record W3196932028 · doi:10.5539/cis.v14n4p20

Non-Delay Tolerant Non-Overlay Routing Protocols Performance Evaluation for VANET

2021· article· en· W3196932028 on OpenAlexvenueno aff
Mahmoud Ali Al Shugran

Bibliographic record

VenueComputer and Information Science · 2021
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceComputer networkRouting protocolVehicular ad hoc networkLink-state routing protocolOptimized Link State Routing ProtocolWireless ad hoc networkZone Routing ProtocolInterior gateway protocolNetwork packetDistributed computingWirelessTelecommunications

Abstract

fetched live from OpenAlex

A Vehicular Ad hoc Network (VANET) is a distinctive situation of wireless ad hoc networks. The designing of the routing protocol considers a critical role in communication in VANET. VANET has specific features compared to other types of wireless ad hoc networks that impose special characteristics for designing of efficient routing protocols.The challenging factor in designing efficient routing protocols for VANET is the high movement of vehicles that incurs a rapid change in the network topology that causes frequent link breakage. This paper presents and evaluates different position-based routing protocols associated with VANETs. The evaluation aiming to determine appropriate specifications for optimal routing protocols’ features achieving best performance within different environmental conditions. The performance comparison is carried out in terms of Packet Delivery Rate (PDR), Void Problem Occurrence Rate (VPOR), and Average Hops Count (AHC).

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)0.004
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.257
Teacher spread0.244 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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