MétaCan
Menu
Back to cohort
Record W2002365903 · doi:10.1109/35.1000224

DiffServ resource allocation for fast handoff in wireless mobile internet

2002· article· en· W2002365903 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 Communications Magazine · 2002
Typearticle
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsUniversity of Waterloo
FundersLangley Research Center
KeywordsComputer scienceComputer networkHandoverQuality of serviceWireless networkResource allocationCall Admission ControlMobile QoSWirelessMobile IPThe InternetService (business)Service providerTelecommunications

Abstract

fetched live from OpenAlex

The next-generation wireless networks are evolving toward a versatile IP-based network that can provide various real-time multimedia services to mobile users. Two major challenges in establishing such a wireless mobile Internet are support of fast handoff and provision of quality of service (QoS) over IP-based wireless access networks. In this article, a DiffServ resource allocation architecture is proposed for the evolving wireless mobile Internet. The registration-domain-based scheme supports fast handoff by significantly reducing mobility management signaling. The registration domain is integrated with the DiffServ mechanism and provisions QoS guarantee for each service class by domain-based admission control. Furthermore, an adaptive assured service is presented for the stream class of traffic, where resource allocation is adjusted according to the network condition in order to minimize handoff call dropping and new call blocking probabilities.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score1.000

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 science0.0010.000
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 head0.025
GPT teacher head0.246
Teacher spread0.221 · 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