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Record W2046372691 · doi:10.1109/giis.2014.6934257

A quality of service model for IEEE 802.11p communication protocol in a smart city

2014· article· en· W2046372691 on OpenAlexaff
Yamen Y. Nasrallah, Irfan Al‐Anbagi, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIEEE 802.11pComputer networkComputer scienceQuality of serviceNetwork packetSmart gridLatency (audio)IEEE 802Low latency (capital markets)WirelessBase stationSmart cityThroughputVehicular ad hoc networkEmbedded systemTelecommunicationsWireless ad hoc networkEngineeringInternet of Things

Abstract

fetched live from OpenAlex

A reliable and time-efficient communication system between vehicles and infrastructure is an indispensable issue in the development of a smart city. The vehicles must have the ability to transmit and receive urgent messages with low latency. Infrastructures such as hospitals and police stations should be equipped with base stations capable of communicating in near real-time fashion with vehicles. In addition to that, in a smart grid scenario, this is especially important between the power charging station and the Electric Vehicles (EVs), Nowadays the best standard that is designed to operate in a vehicular environment is the IEEE 802.11p standard. It is a contention-based Medium Access Control (MAC) protocol that provides Quality of Service (QoS) for Wireless Access in the Vehicular Environment (WAVE). In this paper we propose a low-latency version of this protocol by introducing a finite buffer at the MAC level that momentarily stocks the packets coming from the application layer. Our proposed architecture is modeled with a Markov chain analytical method. Our results show an enhancement in the performance in terms of the end-to-end delay and an improvement in the throughput.

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: none
Teacher disagreement score0.766
Threshold uncertainty score0.435

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 science0.0000.000
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 head0.048
GPT teacher head0.309
Teacher spread0.261 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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