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Record W2038375224 · doi:10.1109/wowmom.2014.6919016

Multihop backpressure scheduling for IEEE 802.11s multiradio cognitive wireless mesh networks

2014· article· en· W2038375224 on OpenAlexaff
Claude Roy, Jean‐Charles Grégoire

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsWireless mesh networkComputer scienceComputer networkSwitched meshCognitive radioIEEE 802.11sShared meshService setWireless broadbandQuality of serviceScheduling (production processes)Mesh networkingDistributed computingOrder One Network ProtocolWirelessWireless networkTelecommunicationsWi-Fi arrayEngineering

Abstract

fetched live from OpenAlex

Wireless mesh networks (WMN) are efficient and low cost solutions for the deployment of broadband access in various environments. While the capacity and the bandwidth availability of single radio WMNs may severely limit QoS, multiradio cognitive WMNs (CWMN) can provide better QoS mechanisms. This paper describes a multihop backpressure scheduling algorithm which ensures that the available bandwidth is properly shared considering the type of traffic to forward, the distance of the clients from the portal and the fluctuating conditions of the links. Performance evaluation in our simulated environment shows that, while preventing bandwidth wastage, our new scheduling algorithm achieves nearly perfect fairness.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.274
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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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