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Record W2036241097 · doi:10.1109/icc.2013.6654718

A fast location-based handoff scheme for vehicular networks

2013· article· en· W2036241097 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
KeywordsHandoverComputer scienceComputer networkScheme (mathematics)Latency (audio)Real-time computingVehicular ad hoc networkLow latency (capital markets)WirelessWireless ad hoc networkTelecommunications

Abstract

fetched live from OpenAlex

IEEE 802.11 is an economical and efficient standard that has been applied to vehicular networks. The long handoff latency of the traditional handoff scheme for IEEE 802.11, however, becomes an important issue for seamless connections in vehicular environments, as more handoffs may be triggered due to higher mobility of vehicles. This paper presents a new fast location-based handoff scheme particularly designed for vehicular networks. With the position and direction of a vehicle and locations of its surrounding access points (APs), our algorithm can accurately predict the next AP that the vehicle may visit. Time spent on scanning APs in handoff procedures is therefore significantly saved. The AP selection scheme can also reduce the total number of handoffs by selecting APs on the vehicle's advancing path. Finally, simulation results demonstrate that the proposed scheme attains low prediction error rates, low layer-2 handoff latency and the reduced number of unnecessary handoffs.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.905

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 head0.006
GPT teacher head0.189
Teacher spread0.184 · 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