Optimal Forwarding in Opportunistic Delay Tolerant Networks with Meeting\n Rate Estimations
Bibliographic record
Abstract
Data transfer in opportunistic Delay Tolerant Networks (DTNs) must rely on\nunscheduled sporadic meetings between nodes. The main challenge in these\nnetworks is to develop a mechanism based on which nodes can learn to make\nnearly optimal forwarding decision rules despite having no a-priori knowledge\nof the network topology. The forwarding mechanism should ideally result in a\nhigh delivery probability, low average latency and efficient usage of the\nnetwork resources. In this paper, we propose both centralized and decentralized\nsingle-copy message forwarding algorithms that, under relatively strong\nassumptions about the networks behaviour, minimize the expected latencies from\nany node in the network to a particular destination. After proving the\noptimality of our proposed algorithms, we develop a decentralized algorithm\nthat involves a recursive maximum likelihood procedure to estimate the meeting\nrates. We confirm the improvement that our proposed algorithms make in the\nsystem performance through numerical simulations on datasets from synthetic and\nreal-world opportunistic networks.\n
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".