A Message Transfer Framework for Enhanced Reliability in Delay-Tolerant Networks
Bibliographic record
Abstract
Delay-tolerant networks (DTNs) can tolerate disruption on end-to-end paths by taking advantage of temporal links emerging between nodes as nodes move in the network. Intermediate nodes store messages before forwarding opportunities become available. A series of encounters (i.e., coming within mutual transmission range) among different nodes will eventually deliver the message to the desired destination. The message delivery performance in a DTN (such as delivery ratio and end-to-end delay) highly depends on the time elapsed between encounters and the time two nodes remain in each others communication range once a contact is established. As messages are forwarded opportunistically among nodes, it is important to have sufficient contact opportunities in the network for faster, more reliable delivery of messages. We propose a simple yet efficient method for improving the performance of a DTN by increasing the contact duration of encountered nodes (i.e., mobile devices). Our proposed sticky transfer framework and protocol enable nodes in DTNs to collect neighbors’ information, evaluate their movement patterns and amounts of data to transfer in order to make decisions of whether to “stick” with a neighbor to complete the necessary data transfers. The sticky transfer framework can be combined with any DTN routing protocol to improve its performance. We evaluate ourframework through simulations and measure several network performance metrics. Simulation results show that the proposed framework can improve the message delivery ratio, end-to-end delay, overhead ratio, buffer occupancy, number of disrupted message transmissions and so on. It can be well adopted for challenged scenarios where larger messages sizes need to be delivered with application deadline constraints. Furthermore, performance of the DTN improved (upto 43%) at higher node densities and (up to 49%) under increased mobility conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".