Supporting Consumer Mobility Using Proactive Caching in Named Data Networks
Why this work is in the frame
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Bibliographic record
Abstract
Mobility management in Named Data Networks (NDNs) is one of the main challenges of seamless operation in the future Internet. Techniques used in existing proposals for Consumer mobility are either reactive or semi-proactive, which try to reduce data access time, but yet retransmissions are required. We propose a fully proactive optimal scheme (OpCCMob) that adopts location and data patterns forecasts to proactively support Consumers movements in the network. In essence, the scheme will optimally cache the predicted content close to the Consumer such that it will be satisfied before handover and avoid Interests retransmissions. A mathematical formulation of the problem is provided such that it bounds the overhead on the network and minimizes the delay of fetching the data. OpCCMob is implemented in ndnSIM and used as a benchmark to evaluate mainstream NDN mobility schemes under various practical scenarios. The results of different experiments show that the delay can be maintained during Consumers movements using control messages as an overhead. Moreover, a sensitivity analysis is conducted to measure the robustness of proactive schemes during imperfect predictions.
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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 | 0.001 | 0.001 |
| 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