Proactive caching for Producer mobility management in Named Data Networks
Why this work is in the frame
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Bibliographic record
Abstract
Named Data Networks (NDNs) offer a promising paradigm for the future Internet to cope with the growing demand for data and the shifts in applications. One of the main challenges in NDNs is how to support a seamless operation during mobility. In this paper, we propose a proactive caching scheme (named ProCacheMob) to support Producer mobility that exploits location predictors and data requests patterns to cache data before handover occurs. In essence, ProCacheMob adopts the predicted future Interests, that will be sent to the mobile Producers, and caches their data contents ahead of handover. Thus, avoids Interest retransmission that increases the Consumer's delay and decreases the network efficiency during Producer's mobility. ProCacheMob is simulated in ndnSIM and evaluated against mainstream NDN mobility solutions. The simulation results show how the scheme is successful to avoid packets drops and decreases the delay experienced by Consumers by 52% compared to other schemes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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