Modeling and Delay Analysis of Intermittently Connected Roadside Communication Networks
Why this work is in the frame
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Bibliographic record
Abstract
Vehicular networks outline a challenging terrestrial application of the emerging delay-tolerant networking (DTN) paradigm where wireless links experience frequent disruptions. Thus, continuous end-to-end paths are unguaranteed. Under such conditions, mobile vehicles present opportunistic relaying capabilities that promote network connectivity, particularly between stationary and isolated roadside units. In this context, we investigate a challenging information-delivery-delay minimization problem. Information is encapsulated into bundles buffered at the source, and vehicles opportunistically transport them to the destination. Consequently, bundles undergo both queueing and transit delays. We propose a probabilistic bundle release scheme (PBRS) under which a roadside unit performs typical Internet-like forwarding where a single bundle is only released to an arriving relatively high-speed vehicle. This ensures a minimized bundle transit. In contrast, under a greedy bundle release scheme (GBRS), a bundle is released to any arriving vehicle, regardless of its speed. Two queueing models are developed to characterize a roadside unit and evaluate its performance under both schemes. A simulation framework is set up to validate these models. Results indicate the inefficiency of the typical Internet packet-like release mechanism as it incurs excessive bundle queueing delays. A bulk bundle release (BBR) extension is proposed as an effective solution. We show that GBRS-BBR outperforms PBRS-BBR.
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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 | 0.001 | 0.002 |
| 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