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Reliable broadcasting in VANETs using Physical-Layer Network Coding

2012· article· en· W1975096130 on OpenAlexafffund
Eugène David Ngangue Ndih, Soumaya Cherkaoui

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversité de Sherbrooke
FundersNational Research Council Canada
KeywordsComputer networkComputer scienceNetwork packetTime division multiple accessThroughputLinear network codingBroadcasting (networking)Media access controlBandwidth (computing)Real-time computingWirelessTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we present a PNC-based MAC protocol for VANETs (VPNC-MAC) which makes use of the PNC to ensure efficiency and reliability of periodic beacon transmissions in VANETs. The VPNC-MAC protocol basically consists on two phases: a setup phase and the heartbeat packet exchange phase. During the setup stage, VPNC-MAC uses a location-based OFDMA signaling technique to guarantee a quick and non bandwidth consuming setup. In addition, the packet exchange phase in VPNC-MAC contains two periods of variable and adjustable length. The fist period is a guaranteed time slot period, called the VPNC-MAC session, in which the nodes exchange their packets using PNC. The second period is a contention period reserved to nodes unable to transmit during the VPNC-MAC session. The simulation results shows that the VPNC-MAC protocol outperforms the optimal CSMA (ideal TDMA) in terms of capacity of transmissions supported within a fixed duration, and overall packet reception rate.

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)0.001
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.026
GPT teacher head0.247
Teacher spread0.221 · 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

Citations7
Published2012
Admission routes2
Has abstractyes

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