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Record W3196526716 · doi:10.5539/mas.v15n5p12

Beacon-Based Non-Delay Tolerant Routing Protocols for VANET: A Systematic Comparison

2021· article· en· W3196526716 on OpenAlexvenueno aff
Mahmoud Ali Al Shugran

Bibliographic record

VenueModern Applied Science · 2021
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceComputer networkRouting protocolVehicular ad hoc networkProtocol (science)Routing (electronic design automation)Wireless ad hoc networkLink-state routing protocolWireless Routing ProtocolDistributed computingWirelessTelecommunicationsMedicine

Abstract

fetched live from OpenAlex

A Vehicular ad hoc network (VANET) is a sort of wireless ad hoc network which are used to provide communications between nodes. The frequent topology change is considered a unique feature of VANET nature due to the high movement of its participating vehicles. Thus, the design of a routing protocol that could cope with VANET characteristics is very challenging. Position-based routing protocols are the most suitable approaches for VANET. In this paper researcher broadly discussed Beacon-based Non-Delay Tolerant Geographical-based routing protocols for VANET. The main concern is to discuss the characteristics disadvantages of those protocols. Finally, several research directions relevant to the focus of this survey are identified that define preferred features of the appropriate routing protocol that can cope with VANET challenges.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0010.001
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 head0.018
GPT teacher head0.266
Teacher spread0.248 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2021
Admission routes1
Has abstractyes

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