On the Role of Delay Tolerant Networks and Contact Graph Routing in Direct-to-Satellite IoT
Bibliographic record
Abstract
This paper explores the integration of Delay-Tolerant Networking (DTN) and Contact Graph Routing (CGR) within Direct-to-Satellite Internet of Things (DtS-IoT) networks, utilizing the FLoRaSat discrete-event simulator based on Omnet++, By incorporating a DTN model and the CGR algorithm, the study evaluates the efficacy of these technologies in optimizing data routing and handling across emerging Low-Earth Orbit (LEO) satellite networks. The research delves into various satellite fleet configurations, including Star and Delta constellations, across different numbers of orbital planes and with the integration of opportunistic Inter-Satellite Links (ISLs). Results using the FLoRaSat simulator demonstrate that the DTN store-carry-and-forward approach over ISLs, enhanced by CGR, significantly reduces end-to-end delivery delays. Specifically, the implementation achieves an average end-to-end delivery delay as low as 10 minutes in 4-plane Star constellations with 24 satellites and immediate forwarding in 8-plane Delta constellations of equivalent size, underscoring the potential of DTN and CGR to improve the efficiency and reliability of emerging DtS-IoT.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".