Modeling and Delay Analysis of Intermittently Connected Roadside Communication Networks
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".