Practical routing in delay-tolerant networks
Bibliographic record
Abstract
Delay-tolerant networks (DTNs) have the potential to connect devices and areas of the world that are under-served by current networks. A critical challenge for DTNs is determining routes through the network without ever having an end-to-end connection, or even knowing which "routers" will be connected at any given time. Prior approaches have focused either on epidemic message replication or on knowledge of the connectivity schedule. The epidemic approach of replicating messages to all nodes is expensive and does not appear to scale well with increasing load. It can, however, operate without any prior network configuration. The alternatives, by requiring a priori connectivity knowledge, appear infeasible for a self-configuring network.In this paper we present a practical routing protocol that only uses observed information about the network. We designed a metric that estimates how long a message will have to wait before it can be transferred to the next hop. The topology is distributed using a link-state routing protocol, where the link-state packets are "flooded" using epidemic routing. The routing is recomputed when connections are established. Messages are exchanged if the topology suggests that a connected node is "closer" than the current node.We demonstrate through simulation that our protocol provides performance similar to that of schemes that have global knowledge of the network topology, yet without requiring that knowledge. Further, it requires a significantly smaller quantity of buffer, suggesting that our approach will scale with the number of messages in the network, where replication approaches may not.
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".