MP-DSR: a QoS-aware multi-path dynamic source routing protocol for wireless ad-hoc networks
Bibliographic record
Abstract
Routing in wireless ad-hoc networks has received significant attention in the literature due to the fact that the dynamic behavior of these networks poses many technical challenges on the design of an effective routing scheme. Though on-demand routing approaches have been shown to perform well, they generally lack the support for quality-of-service (QoS) with respect to data transmission. In order to select a subset of end-to-end paths to provide increased stability and reliability of routes, a new QoS metric, end-to-end reliability, is defined and emphasized. We present a distributed multi-path dynamic source routing protocol (MP-DSR)for wireless ad-hoc networks to improve QoS support with respect to end-to-end reliability. Our protocol forwards outgoing packets along multiple paths that are subject to a particular end-to-end reliability requirement. A simulation study is performed to demonstrate the effectiveness of our proposed protocol, particularly the fact that MP-DSR achieves a higher rate of successful packet delivery than existing best-effort ad-hoc routing protocols, such as the dynamic source routing (DSR).
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".