Cross layer optimization for routing based on link layer delay analysis
Bibliographic record
Abstract
The choice of a suitable path for packet transmission represents a fundamental issue to any routing protocol. The principle of choosing the shortest path is also no longer a good option for route selection since many other aspects may influence communication performance. In that sense, the link quality of the path is more important than the length of the path in a wireless network because of the unstable conditions of the channel. Expected Transmission Count (ETX) is a widely-used routing metric, which servers into the selection of the path with fewer transmissions for a successful packet delivery. However, other factors should be also considered for route selection, such as the re-transmission delay. In this paper, a comprehensive analysis on the effect of the link layer delay to the transmission throughput is presented and discussed. Given the analysis outcome, this work proposes a routing metric based on the link layer delay for IEEE 802.11. This metric allows to determine the delay of each hop along the path to evaluate the quality of the path as whole. Experimental simulation results reveal that the proposed routing metric achieves better results when compared to two other known approaches.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".