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Record W2102685832 · doi:10.1109/vetecs.2009.5073552

Fair and Efficient Scheduling for Telemedicine Traffic Transmission over Wireless Cellular Networks

2009· article· en· W2102685832 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
TopicAdvanced Wireless Network Optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTelemedicineComputer networkComputer scienceQuality of serviceWirelessScheduling (production processes)Wireless networkBandwidth (computing)TelecommunicationsEngineeringHealth care

Abstract

fetched live from OpenAlex

Telemedicine traffic transmission over wireless cellular networks has gained in importance during the last few years. Due to the fact that this type of traffic carries critical information regarding the patients' condition, it is vitally important that multimedia telemedicine traffic has highest transmission priority in comparison to all other types of traffic in the cellular network. However, the need for expedited and correct transmission of telemedicine traffic calls for a guaranteed bandwidth to telemedicine users. This creates a tradeoff between the satisfaction of the very strict quality of service requirements of telemedicine traffic and the loss of the guaranteed bandwidth in the numerous cases when it is left unused, due to the infrequent nature of telemedicine traffic. In this paper, we propose a fair scheduling mechanism for telemedicine traffic transmission over wireless cellular networks. The mechanism achieves high channel bandwidth utilization while offering full priority to telemedicine traffic.

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 score0.726
Threshold uncertainty score0.682

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 head0.004
GPT teacher head0.204
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

Quick stats

Citations11
Published2009
Admission routes1
Has abstractyes

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