Persistent SCTP timeout policy by using cross-layer mechanism
Bibliographic record
Abstract
To meet various contingencies of operating wireless networks including for example the change of the state of the channel, cross-layer techniques facilitate the sharing of information between the OSI model layers and can be apply to all various protocols levels, if there are interactions for which the overall performance of the studied system can be improved. Reliable transport protocols use the retransmission timeout management mechanism (RTO-MM) when a bad state of the wireless channel occurs, which temporarily blocks the transmission of data. In this paper, we suggest a new policy of timeout applied to the Stream Control Transmission Protocol (SCTP) called persistent timeout policy. This policy is based on the use of the channel status provided by the 802.11 link layer, through the cross-layer mechanism. The principle of this timeout is that when a bad state of the wireless channel blocks the sending of data, SCTP continuously observes the evolution of this state to detect the next favorable change before sending its segments. We evaluate the following two timeout policies (persistent and traditional RTO-MM) of SCTP in an ad hoc network, and also in comparison with the Transmission Control Protocol (TCP). Section I of this paper presents an overview of the SCTP protocol. Section II presents the principle of the persistent timeout policy. Section III presents the simulation results that are used to compare the two timeout policies of the two reliable transport protocols SCTP and TCP.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".