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Record W2108490126 · doi:10.1145/1506270.1506387

Topology designs with controlled interference for multi-radio wireless mesh networks

2008· article· en· W2108490126 on OpenAlexaff
K. L. Eddie Law, Adam Kohn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWireless mesh networkComputer networkComputer scienceOrder One Network ProtocolMesh networkingWireless networkWirelessNetwork topologyShared meshSwitched meshRadio resource managementService setChannel (broadcasting)Interference (communication)Hazy Sighted Link State Routing ProtocolRouting protocolWi-Fi arrayDistributed computingRouting (electronic design automation)TelecommunicationsDynamic Source Routing

Abstract

fetched live from OpenAlex

Wireless access technologies have been maturing and becoming the natural selections to establish infrastructures of wireless mesh networks (WMNs). IEEE 802.11 wireless local area networks (WLANs) have recently been the popular choices to carry out experiments on wireless mesh networks. Numerous research directions are being investigated regarding, for example, signal interference, MAC protocol designs, channel assignments, path routing and metric selections, and transport layer performance analysis, etc. In this paper, we examine the relationship among topologies of wireless mesh networks, channel assignment issues, and number of radio tranceivers in the meshing stations. Path establishment protocol between source and destination stations for multi-radio multi-channel wireless mesh networks is implemented for experiments. Different frequency channels are used for a multi-hop path setup operation. Setup durations for multi-hop connections are measured and reported in the paper.

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 score0.968
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.044
GPT teacher head0.264
Teacher spread0.220 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

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