Improving quality-of-service in ad hoc wireless networks with adaptive multi-path routing
Bibliographic record
Abstract
The objective of this paper is to propose a mechanism for adaptive computation of multiple paths to transmit a large volume of data packets from a source s to a destination d in ad hoc wireless networks. We consider two aspects in this framework. The first aspect is to perform preemptive route re-discoveries before the occurrence of route errors while transmitting a large volume of data from s to d. Consequently, this helps find out dynamically a series of multiple paths in the temporal domain to complete the data transfer. The second aspect is to select multiple paths in the spatial domain for data transfer at any instant of time and to distribute the data packets in sequential blocks over those paths in order to reduce congestion and end-to-end delay. The performance of this approach has been evaluated to show the improvement in the quality of service. It has been observed that the mechanism allows any source to transmit a large volume of data to a destination without degradation of performance due to route errors. Additionally, it would help reduce significantly the end-to-end delay and the number of route-rediscoveries needed in this process.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".