MétaCan
Menu
Back to cohort
Record W2133381163 · doi:10.1109/iscc.2009.5202316

A Selectivity Function Scheduler for IEEE 802.11e

2009· article· en· W2133381163 on OpenAlexaff
Ashraf Ali Bourawy, Najah AbuAli, Hossam S. Hassanein

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceIEEE 802PollingConstant bitrateNetwork packetScheduling (production processes)QueueComputer networkChannel (broadcasting)Distributed coordination functionThroughputReal-time computingVariable bitrateIEEE 802.11WirelessBit rateQuality of serviceMathematical optimizationOperating system

Abstract

fetched live from OpenAlex

The IEEE 802.11e standard defines a reference scheduler that allocates transmission opportunities (TXOP) to traffic streams based on mean data rate and minimum physical rate. However, the reference scheduler is only efficient for streams with strict constant bit rate (CBR) characteristics. This paper presents a scheduling scheme named as the selectivity function scheduler (SFS) for the IEEE 802.11e hybrid coordination function (HCF) controlled channel access (HCCA). The SFS enhances the procedure of computing the number of variable sized packets by not only considering the new arrivals, but also accounting for the packets remaining in the queue due to channel conditions. SFS incorporates a selectivity function that assigns polling priorities to already admitted streams based on their actual requirements. The performance of the proposed SFS is evaluated and compared with the reference scheduler defined by the standard. Simulation results show that the SFS outperforms the standard scheduler in terms of enhancing streams throughput, reducing the packet dropping ratio, and maintaining high fairness among different traffic streams.

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)0.000
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.277

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 head0.019
GPT teacher head0.271
Teacher spread0.252 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicWireless Networks and ProtocolsFrench-language works237,207