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Record W2114966563 · doi:10.1145/2656346.2656352

Vehicular social systems

2014· article· en· W2114966563 on OpenAlexaff
Saida Maaroufi, Samuel Pierre

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceVehicular ad hoc networkWireless ad hoc networkField (mathematics)PublicationRouting (electronic design automation)Routing protocolComputer networkComputer securityWirelessTelecommunicationsBusiness

Abstract

fetched live from OpenAlex

Online Social Networks (OSNs) are taking the world by storm and are urging the development of a new type of ad hoc networks that is Vehicular Social Networks (VSNs). The inclusion of social networks within vehicles has attracted researchers to devise social aware routing protocols and architectures in order to build vehicular social communities and to facilitate human interaction between commuters on the road. However, several issues defy the purpose of VSNs. This paper presents an analysis of the relevant work published in this growing new field, as well as an extension of our research work that tackles the problem of data dissemination in VSNs. We first summarize vehicular social architectures. Then we examine the main social properties of VANET drivers that contribute in the design of socially aware routing algorithms. Moreover, we discuss the open research challenges including privacy concerns. Finally, we present SocialDrive, an online social aware publish/subscribe strategy for vehicles transiting in platoons. We evaluate its performance and we show that it improves the quality of real time communication between commuters in dense networks.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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