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Record W2148522665 · doi:10.1109/glocom.2010.5684128

A Novel Network Mobility Management Scheme for Vehicular Networks

2010· article· en· W2148522665 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer networkMobility managementComputer scienceVehicular ad hoc networkWireless networkMobility modelWirelessNetwork topologyRadio resource managementTelecommunicationsWireless ad hoc network

Abstract

fetched live from OpenAlex

In recent years, vehicular networks have been widely studied because these networks become an important part of wireless metropolitan networks. This type of wireless network is different from other conventional wireless mobile networks since the mobile nodes in the networks usually have high speed, sufficient power and dynamic topology. In vehicular networks, vehicles can connect with access points, which are deployed on the roadside, to communicate with their correspondent nodes through Internet. However, the radio range of antennas which are installed in vehicles and access points are limited. As a result, to maintain connections during the trips, vehicles have to switch their access points frequently. The question of how to design an efficient mobility management solution for vehicular networks is still very important in such mobile environment. In this paper, a novel mobility management scheme for vehicular networks is proposed. Network mobility solution is adopted for vehicular networks and intra-cluster communications are used to improve the quality of the mobility.

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 score0.788
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 science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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