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Record W2601943225 · doi:10.1504/ijnvo.2017.10004173

Theoretical framework of link stability-based forwarding strategy in vehicular ad hoc networks

2017· article· en· W2601943225 on OpenAlexaff
Mohammad M. Kadhum, Tat‐Chee Wan, Sabri M. Hanshi

Bibliographic record

VenueInternational Journal of Networking and Virtual Organisations · 2017
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceComputer networkVehicular ad hoc networkPacket forwardingRouting protocolNetwork packetRouting (electronic design automation)Link (geometry)Wireless ad hoc networkOptimized Link State Routing ProtocolDistributed computingWirelessTelecommunications

Abstract

fetched live from OpenAlex

The nature of vehicular ad hoc networks (VANET) brings challenges that prompt researchers to address relevant problems. One of serious challenges is to design a robust routing protocol that is capable of tackling the frequent link disruptions which are caused by vehicle mobility. Geographic forwarding strategy-based routing protocols are found more suitable in highly dynamic network such VANET. Link expiry time-based (LET) routing is being utilised to minimise link breakages in effectively mobile networks. However, it may cause some issues such as increasing delay and routing loops due to non-optimal selection of next-hop. This paper proposes a relative velocity based geographic forwarding (RVGF) strategy which aims to enhance the link stability with respect to geographic information. Relative velocity model is introduced where speed and direction of vehicles are utilised to improve forwarding decisions in VANET. RVGF can help in minimising the probability of link breakage and ensuring high packet delivery.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.262
Teacher spread0.247 · 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
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
Published2017
Admission routes1
Has abstractyes

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