Relative interpersonal-influence-aware routing in buffer constrained Delay-Tolerant Networks
Bibliographic record
Abstract
In Delay-Tolerant Networks, the existence of social selfishness results in different levels of willingness to forward the packet from acquaintances and strangers. The traditional social selfishness is defined solely by the social ties between node pairs to indicate the preference for packet delivery. However, the absolute value of social ties cannot accurately model the competition among friends when the storage resources become scarce. Thus, if the social selfishness exists in the buffer management, the traditional social ties cannot reflect the willingness for saving the packet when buffer overflows. To address this issue, we take the relay's social ties with all other nodes into account and introduce the definition of interpersonal influence as a metric for the willingness to save packets in buffer management. Furthermore, based on the interpersonal influence we estimate the overall delivery probability and further propose a multi-copy routing protocol to reduce the occurrence of packet dropping. Simulation result based on real trace INFOCOM06 demonstrates the efficiency of our scheme.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".