Optimizing route-cache lifetime in ad hoc networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it