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Record W1586020639 · doi:10.1002/wcm.2244

Applications of network coding to improve TCP performance over wireless mesh networks: a survey

2012· article· en· W1586020639 on OpenAlexaff
Nasim Arianpoo, Paria Jokar, Victor C. M. Leung

Bibliographic record

VenueWireless Communications and Mobile Computing · 2012
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of British Columbia
FundersDivision of Computer and Network Systems
KeywordsComputer scienceComputer networkWireless mesh networkZeta-TCPTransmission Control ProtocolTCP tuningTCP delayed acknowledgmentLinear network codingNetwork packetNetwork performanceWireless networkWirelessTelecommunications

Abstract

fetched live from OpenAlex

ABSTRACT There is growing interest in the use of wireless mesh network (WMN) as a last‐mile option for Internet access. Despite the many benefits of WMNs, the performance of Internet access may not be ideal. One of the main issues is the interaction of transmission control protocol (TCP) with the underlying network. The poor performance of TCP over multi‐hop networks is well‐documented, and extensive research exists, which addresses TCPs foible and enhance TCP performance for multi‐hop environments. This paper provides a thorough survey of TCP performance issues over WMNs and the available solutions to address these issues. Among the existing methods, we focused on network coding (NC) and the ways that TCP interacts with network coded systems. NC is a technique that encodes the received packets in each node before forwarding them towards the destination. The use of NC in the transport layer to address performance issues raised by wireless access is a recent research topic. This paper presents a detailed study of TCP interaction with NC. Some open research areas in this field are suggested. Copyright © 2012 John Wiley & Sons, Ltd.

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)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.004
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 head0.028
GPT teacher head0.291
Teacher spread0.263 · 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
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
Published2012
Admission routes1
Has abstractyes

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