Bibliographic record
Abstract
On-demand routing reduces the control overhead in mobile ad hoc networks, but it has the major drawback of introducing latency between route-request arrival and the determination of a valid route. This paper addresses the issue of minimizing the delay in on-demand routing protocols through optimizing the Time-to-Live (TTL) interval for route caching. An analytical framework is introduced to compute the expected routing delay when a source node or an intermediate node has a cached route with any given TTL value. Furthermore, numerical methods are proposed to determine the optimal TTL of a newly cached route. We present simulation results that support the validity of our analysis. Using the proposed analytical framework, we study how the routing delay is affected by route length, route-request frequency, and the frequency of topology variation. We show that the proposed optimal route-cache TTL strategy can significantly reduce the routing delay over systems that either does not use route-cache or keeps route-cache indefinitely long. We further show that the performance gain of optimizing the route-cache TTL increases with increasing traffic pattern localization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".