Applications of network coding to improve TCP performance over wireless mesh networks: a survey
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".