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Record W2461609381 · doi:10.1109/tmc.2016.2591527

Characterizing the Instantaneous Connectivity of Large-Scale Urban Vehicular Networks

2016· article· en· W2461609381 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Mobile Computing · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Mobility and Location-Based Analysis
Canadian institutionsConcordia University
FundersSeventh Framework ProgrammeResearch Executive Agency
KeywordsNetwork topologyComputer scienceVehicular ad hoc networkContext (archaeology)Topology (electrical circuits)NavigabilitySoftware deploymentComputer networkReliability (semiconductor)Block (permutation group theory)Distributed computingWireless ad hoc networkTelecommunicationsWirelessEngineeringPower (physics)

Abstract

fetched live from OpenAlex

Understanding of the network topology is a basic building block towards the design of efficient networking solutions. In the context of vehicular networks, such a step is especially crucial due to the highly dynamic nature of vehicles that can lead to strong instantaneous variations in the structure of the network. This notwithstanding, and despite the soon-to-come real-world deployment of vehicle-to-vehicle communication technologies, we still lack a clear understanding of vehicular network topological properties. In this paper, we present a complex network analysis of the instantaneous topology of a realistic vehicular network in Cologne, Germany. Our study unveils a poorly connected topology, with very limited availability, reliability, and navigability. We also examine the vehicular network topology in a second scenario, i.e., Zurich, Switzerland. The comparative analysis shows how simplistic mobility models can lead to unrealistic overly connected topologies.

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.

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 categoriesScience and technology studies
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 score0.583
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.012
GPT teacher head0.265
Teacher spread0.253 · 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