Performance modeling of network coding in epidemic routing
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
Epidemic routing has been proposed to reduce the data transmission delay in opportunistic networks, in which data can be either replicated or network coded along the opportunistic multiple paths. In this paper, we introduce an analytical framework to study the performance of network coding based epidemic routing, in comparison with replication based epidemic routing. With extensive simulations, we show that our model successfully characterizes these two protocols and demonstrates the superiority of network coding in opportunistic networks when bandwidth and node buffers are limited. We then propose a priority variant of the network coding based protocol, which has the salient feature that the destination can decode a high priority sub-set of the data much earlier than it can decode any data without the priority scheme. Our analytical results provide insights into how network coding based epidemic routing with priority can reduce the data transmission delay while inducing low overhead.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 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