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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".