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Record W2106527934 · doi:10.1109/wcnc.2011.5779286

Algorithms for bandwidth efficient multicast routing in multi-channel multi-radio wireless mesh networks

2011· article· en· W2106527934 on OpenAlexaff
Hoang Lan Nguyen, Uyen Trang Nguyen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsYork University
Fundersnot available
KeywordsComputer networkComputer scienceMulticastProtocol Independent MulticastDistributed computingWireless mesh networkDistance Vector Multicast Routing ProtocolDynamic Source RoutingLink-state routing protocolSource-specific multicastXcastSteiner tree problemStatic routingRouting (electronic design automation)Routing protocolWireless networkWirelessMathematicsTelecommunicationsMathematical optimization

Abstract

fetched live from OpenAlex

Traditional multicast routing algorithms such as shortest path tree (SPT) and Steiner tree (MST) do not consider the wireless broadcast advantage or the underlying channel assignments in a multi-channel multi-radio (MCMR) wireless mesh network (WMN). We propose multicast routing algorithms that take into account the above factors in order to minimize the amount of network bandwidth consumed by a routing tree. Experimental results show that routing trees constructed by the proposed algorithms outperform traditional trees such as SPTs, MSTs and minimum number of forwarders trees (MFTs) with respect to packet delivery ratio, throughput and end-to-end delay.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.056
GPT teacher head0.270
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations12
Published2011
Admission routes1
Has abstractyes

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