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Record W2134124253 · doi:10.1109/wcnc.2009.4917728

Flow Starvation Mitigation for Wireless Mesh Networks

2009· article· en· W2134124253 on OpenAlexafffund
Keivan Ronasi, Sathish Gopalakrishnan, Vincent W. S. Wong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWireless mesh networkComputer networkComputer scienceMesh networkingScalabilityOrder One Network ProtocolDefault gatewayThroughputWireless networkShared meshSwitched meshWirelessDistributed computingTelecommunications

Abstract

fetched live from OpenAlex

Wireless mesh networks can provide scalable highspeed Internet access at a low cost. Fair channel access among different nodes in the wireless mesh network, however, is an important consideration that needs technological solutions before mesh networks can be widely deployed. Lack of fairness significantly decreases the throughput of nodes that are more than one hop away from mesh gateways. We propose an analytical model and use simulation studies to establish the existence of starvation in mesh networks even when we can ameliorate problems due to exposed terminals. Motivated by the inability of standard medium access control (MAC) protocols to limit starvation, we propose a modification to the MAC protocol to alleviate flow starvation. Our proposed algorithm improves the channel usage of short-term flows with nodes that are multiple hops from the gateway by a factor of 7 in some cases with a penalty of 20% reduction in total throughput across all nodes. Our proposed algorithm also has a better performance than two other schemes in terms of a higher fairness index.

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 score0.955
Threshold uncertainty score0.394

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 communication0.0000.001
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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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

Citations17
Published2009
Admission routes2
Has abstractyes

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