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Record W2045203293 · doi:10.1002/wcm.1098

Selectivity function scheduler for IEEE 802.11e HCCA access mode

2011· article· en· W2045203293 on OpenAlexaff
Najah Abu Ali, Ashraf Ali Bourawy, Hossam S. Hassanein

Bibliographic record

VenueWireless Communications and Mobile Computing · 2011
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer sciencePollingScheduling (production processes)Computer networkIEEE 802Distributed coordination functionQuality of serviceNetwork packetRandom accessReal-time computingThroughputWirelessIEEE 802.11Telecommunications

Abstract

fetched live from OpenAlex

SUMMARY In this paper, we present a scheduling algorithm that enhances the performance of the standard IEEE 802.11e scheduler for the Hybrid Coordination Function Controlled Channel Access mode. The main contribution in designing the proposed scheduler is the ability to accommodate multiple streams with different levels of Quality of Service requirements concurrently running on the same station. This is achieved by dynamically calculating the Transmission Opportunities of each active traffic stream (TS) and the appropriate Service Interval of each active station. The proposed algorithm optimizes the utilization of the scarce bandwidth resources by only polling active stations. The algorithm incorporates a selectivity function to assign polling priorities to the active streams only based on their diverse requirements and their link‐attainable transmission rates. The performance of the proposed Selectivity Function Scheduler (SFS) scheme is evaluated against the standard scheduler. Simulation results show that the SFS outperforms the standard scheduler in terms of enhancing streams' throughput, reducing packet dropping ratio and maintaining high fairness amongst the admitted TS. Copyright © 2011 John Wiley & Sons, Ltd.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.842

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.083
GPT teacher head0.338
Teacher spread0.255 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2011
Admission routes1
Has abstractyes

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