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Record W2152075021 · doi:10.1109/glocomw.2010.5700108

Fault-tolerant and scalable channel assignment for multi-radio multi-channel IEEE 802.11a-based wireless mesh networks

2010· article· en· W2152075021 on OpenAlexaff
Aizaz U. Chaudhry, Roshdy H. M. Hafez, Osama Aboul‐Magd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceWireless mesh networkComputer networkScalabilityMesh networkingDistributed computingWireless networkSwitched meshChannel (broadcasting)Bandwidth (computing)Order One Network ProtocolWirelessFault toleranceTelecommunications

Abstract

fetched live from OpenAlex

Wireless Mesh Networks aim to provide high-bandwidth broadband connections to a large community and thus, should be able to accommodate a large number of users accessing the Internet. Due to high estimated traffic volume in Wireless Mesh Networks, scalability and fault tolerance become important requirements in algorithm design. We propose a Failure Recovery Mechanism (FRM) for the channel assignment algorithm, TICA (Topology-controlled Interference-aware Channel-assignment Algorithm), whose goal is to provide automatic and fast failure recovery. We also investigate the performance of TICA with respect to scalability and show that TICA performs well in large-scale networks.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.255
Teacher spread0.229 · 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

Citations9
Published2010
Admission routes1
Has abstractyes

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