Exploiting Node Localization for Performance Improvement of Vehicular Delay-Tolerant Networks
Why this work is in the frame
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Bibliographic record
Abstract
Vehicular Delay-Tolerant Networks (VDTNs) are characterized by high node mobility, intermittent connectivity, and short contact durations. Such factors cause incomplete transmissions and the waste of link capacity. To address these issues, this paper explores the use of node localization in VDTNs. The exchange of signaling information related to nodes' real-time location, current trajectory, velocity, and transmit range allows a Contact Prediction Algorithm to estimate contact durations. This information can be used in conjunction with additional signaling information (e.g. link data rate), to determine the maximum number of bytes that can be transmitted during contact opportunities. A Contact Duration Scheduling Policy can use this information to prevent incomplete transmissions, while increasing the number of successfully relayed bundles and improving data link utilization. Through a simulation study, we investigate the benefits of introducing the concept of node localization, and evaluate the performance of the proposed Contact Prediction Algorithm and Contact Duration Scheduling Policy. We demonstrate the gains introduced by this approach in comparison with an environment where VDTN nodes have no access to localization information.
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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 | 0.000 |
| 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