Performance evaluation and analysis of delay tolerant networking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wireless opportunistic connections are the primary mechanism for transferring data between disconnected nodes, such as vehicles, remote sensors, or village kiosks DieselNet, SeNDT, KioskNet in a delay tolerant network (DTN). Opportunistic connections may last from seconds, as in the case of a rapid drive-by, to several minutes. During this short connection window, it is important to maximize data transfer between DTN nodes. We use microbenchmarks to study the wireless transfer performance of the DTN reference implementation, which is the most widely used DTN implementation today implementing. Existing DTN deployments utilize low-cost, low-power devices that tend to have slow CPUs KioskNet. Based on these characteristics, we hypothesize about the effect of control parameters on opportunistic data transfer. We test our hypotheses through a series of experiments and show that the principal performance bottleneck is the CPU. We also found that the choice of DTN bundle size affects performance by a factor of up to 60.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 it