Vehicular opportunistic communication under the microscope
Bibliographic record
Abstract
We consider the problem of providing vehicular Internet access using roadside 802.11 access points. We build on previous work in this area [18, 8, 5, 11] with an extensive experimental analysis of protocol operation at a level of detail not previously explored. We report on data gathered with four capture devices from nearly 50 experimental runs conducted with vehicles on a rural highway. Our three primary contributions are: (1) We experimentally demonstrate that, on average, current protocols only achieve 50% of the overall throughput possible in this scenario. In particular, even with a streamlined connection setup procedure that does not use DHCP, high losses early in a vehicular connection are responsible for the loss of nearly 25% of overall throughput, 15% of the time. (2) We quantify the effects of ten problems caused by the mechanics of existing protocols that are responsible for this throughput loss; and (3) We recommend best practices for using vehicular opportunistic connections. Moreover, we show that overall throughput could be significantly improved if environmental information was made available to the 802.11 MAC and to TCP. The central messagein this paper is that wireless conditions in the vicinity of a roadside access point are predictable, and by exploiting this information, vehicular opportunistic access can be greatly improved.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".