Efficient Network Coded Data Transmissions in Disruption Tolerant Networks
Bibliographic record
Abstract
Most routing protocols in disruption tolerant networks (DTN) use redundant transmissions to explore the diversities in routing paths in order to reduce data transmission delay. However, mobile nodes in DTN usually have limited energy and may prefer fewer transmissions for longer lifetime. Hence, it is vital to carefully balance the tradeoff between data transmission delay and the amount of transmissions among mobile nodes. In this paper, we consider the problem to route a batch of data packets in DTN. By making an analogy between the routing protocol and low-density erasure codes, we investigate the information-theoretical optimal number of data transmissions in delivering data. With such insights, we propose E-NCP, an efficient protocol in DTNs based on network coding, that reduces data transmissions significantly, while increasing data transmission delay only slightly as compared to the protocol with the best performance. With extensive theoretical analysis and simulations, we show that network coding facilitates a better tradeoff between resource usage and protocol performance, and that our protocol offers unique advantages over replication-based protocols.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".