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Record W2135959573 · doi:10.1109/wcnc.2011.5779216

Best path to best gateway scheme for multichannel multi-interface wireless mesh networks

2011· article· en· W2135959573 on OpenAlexaff
Mustapha Boushaba, Abdelhakim Hafid

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsComputer networkComputer scienceInterior gateway protocolGateway (web page)Wireless mesh networkDefault gatewayInternetworkingPath (computing)Gateway addressNetwork packetThe InternetRouting protocolRouterMetricsBorder Gateway ProtocolShortest path problemDistributed computingH.248WirelessWireless networkTelecommunicationsRouting tableLink-state routing protocol

Abstract

fetched live from OpenAlex

This paper addresses the problem of optimal gateways selection and route selection to Internet in backbone wireless mesh networks (WMNs) where each mesh router (MR) is equipped with multiple radio interfaces and a subset of nodes serve as gateways to Internet. Several schemes have been proposed to route packets in WMNs or to select appropriate gateways to connect clients to Internet. However, most of these schemes consider packet loss, interferences, load at gateways, or ETX (Expected Transmission Count) as routing metrics; only a few schemes consider two some of these metrics at the same time. In this paper, we propose an efficient gateway and path selection scheme, called BP2BG (Best Path to Best Gateway), that takes into account load at gateways, ETX and interferences in order to select best path to best gateway. Simulation results show that BP2BG can significantly improve the overall network performance compared to schemes using either ETX, nearest gateway (i.e., shortest path to gateway), load at gateways or interferences as metrics for path and gateway selection.

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 categoriesMeta-epidemiology (narrow)
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 score0.959
Threshold uncertainty score1.000

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 science0.0020.001
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 head0.045
GPT teacher head0.271
Teacher spread0.226 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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