Improving Dominating Set Routing Performance via Node Mobility Model
Bibliographic record
Abstract
To support seamless communications for roaming users over heterogeneous wireless networks, mobile ad-hoc networks (MANETs) are receiving a special interest in the research community due to its infrastructure-less nature. MANETs can help to achieve a seamless service for users roaming over areas with no coverage from other wireless networks. As a result, MANETs are a key access network component of the super node system that employs the delay tolerant network (DTN) architecture to address user mobility issues. Within the super node system, a dominating-set routing technique is proposed to improve message delivery over MANETs and to achieve better resource utilization. The performance of the dominating set routing technique depends on estimation accuracy of the probability of a future contact between nodes. This paper studies how node mobility can be modeled and used to better estimate the probability of a contact. We derive a distribution for the node-to-node inter-meeting time, and present numerical results to demonstrate that it can be used to improve the dominating-set routing technique performance.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".