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Record W2716509290 · doi:10.1109/ccece.2017.7946683

IMRP: Interference-aware multicast routing for Wireless Mesh Networks

2017· article· en· W2716509290 on OpenAlexaff
Behzad Farmani, Muhammad Jaseemuddin, Omar Batarfi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer networkComputer scienceMulticastProtocol Independent MulticastDistance Vector Multicast Routing ProtocolDistributed computingWireless mesh networkXcastSource-specific multicastMetricsRouting (electronic design automation)Routing protocolWireless networkWirelessStatic routingTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we propose an interference-aware multicast routing scheme for a single-radio Wireless Mesh Networks carrying multiple multicast flows. We focus on multicast routing over WMNs with stationary nodes, such as community wireless networks. We present a new metric that is based on three components: Path Interference Cost (Ip), Broadcast Benefit Factor (BF), and Hop Count (HC). We proposed a formula to compute Link Interference Cost that is aggregated into Ip. The goal of our metric is to choose a join path to multicast tree for a new receiver node that causes lower intra-flow and inter-flow interferences. The benefit of the proposed metric is to improve flow throughput. We also proposed a route discovery scheme for discovering the join path, computing metric, and making route selection. We evaluated the performance of our scheme through simulation under variety of network conditions.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.031
GPT teacher head0.283
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 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

Citations1
Published2017
Admission routes1
Has abstractyes

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