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Record W2093788399 · doi:10.1109/bsc.2010.5472987

Experiments of multi-channel 802.11 wireless mesh networks with TCP proxies

2010· article· en· W2093788399 on OpenAlexaff
Adam Kohn, K. L. Eddie Law

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer networkWireless mesh networkComputer scienceWireless networkTestbedWirelessOrder One Network ProtocolRadio resource managementIEEE 802.11sChannel (broadcasting)Distributed computingTelecommunications

Abstract

fetched live from OpenAlex

IEEE 802.11 wireless access technology is a possible candidate for constructing wireless mesh networks. However, multi-hop 802.11 wireless networks suffer heavy co-channel interference. In this paper, the 802.11-based networks are extended to operate using multi-radio multi-channel designs to inhibit the interference effects. Using a partially overlapped channel scenario and an orthogonal channel scenario, it has been confirmed that the introduction of multiple channels is capable of improving network performance. Despite these gains, TCP performance degrades exponentially with hop counts; therefore, wireless mesh networks may further be improved by adding an n-hop proxy service. In terms of hop counts, these proxies break long connections into relatively shorter connections with tighter transport layer control. A trade-off between the number of proxies and the length of proxies has become evident through testbed evaluation. With respect to this trade-off, the queuing delays at proxies and the amount of collisions over the lossy wireless links signify the need for a suitable protocol to control the efficient usage of multiple channels and proxies.

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: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score0.595

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 communication0.0000.000
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 head0.021
GPT teacher head0.268
Teacher spread0.247 · 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
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

Citations3
Published2010
Admission routes1
Has abstractyes

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