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Record W2147422444 · doi:10.1109/mcom.2011.5762817

IP mobility management for vehicular communication networks: challenges and solutions

2011· article· en· W2147422444 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Communications Magazine · 2011
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Waterloo
FundersFundación para el futuro de ColombiaUniversity of WaterlooUniversidad ICESI
KeywordsComputer scienceProtocol stackComputer networkMobility managementMobility modelSoftware deploymentVehicular ad hoc networkProtocol (science)Internet ProtocolThe InternetTelecommunicationsWirelessWireless ad hoc networkWireless sensor network

Abstract

fetched live from OpenAlex

Vehicular communication networks have emerged as a promising platform for the deployment of safety and infotainment applications. The stack of protocols for vehicular networks will potentially include Network Mobility Basic Support (NEMO BS) to enable IP mobility for infotainment and Internet-based applications. However, the protocol has performance limitations in highly dynamic scenarios, and several route optimization mechanisms have been proposed to overcome these limitations. This article addresses the problem of IP mobility and its specific requirements in vehicular scenarios. A qualitative comparison among the existent IP mobility solutions that optimize NEMO BS in vehicular networks is provided. Their improvements with respect to the current standard, their weaknesses, and their fulfillment of the specific requirements are also identified. In addition, the article describes some of the open research challenges related to IP mobility in vehicular scenarios.

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 categoriesMeta-epidemiology (narrow)
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 score0.841
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.059
GPT teacher head0.248
Teacher spread0.189 · 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