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Record W2096638896 · doi:10.1109/cnsr.2007.60

Structural Unfairness in 802.11-basedWireless Mesh Networks

2007· article· en· W2096638896 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceLimitingComputer networkNetwork topologyWireless mesh networkSoftware deploymentHop (telecommunications)WirelessMesh networkingDistributed computingWireless networkDefault gatewayResidualTelecommunicationsAlgorithmEngineering

Abstract

fetched live from OpenAlex

The use of multi-hop wireless networks based on 802.11 technology is extensive and growing, owing to their ease of deployment and low cost. However, such networks exhibit poor fairness, starving nodes that are too many hops distant from the gateway. The best current solution to this problem is source rate limiting. While effective in many topologies, this fails to completely address the fairness problem. In this paper we investigate this problem of residual unfairness in multi-hop wireless networks that use source-rate limiting. We identify the five necessary conditions for its occurrence, showing that elimination of any of these conditions is sufficient to remove the remaining unfairness. For cases where the conditions are unavoidable, we present two simple changes that can ameliorate the problem, providing on average 30% improvement for the least-rate flow.

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.

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: Empirical · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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

Quick stats

Citations9
Published2007
Admission routes1
Has abstractyes

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