Strategic Sensing in Vehicular Networks Using Known Mobility Information
Bibliographic record
Abstract
In this paper, we study the problem of sensing targets in the context of vehicular networks. First, we define targets to be the vehicles moving on the road and sensors to be the roadside cameras. Then, we study the effect of predicted mobility on reducing the number of times each camera is activated in order to guarantee the coverage of targets. We formulate the sensing problem as an integer linear program using an opportunistic scheduler. Afterward, we extend the formulation and propose a novel strategic scheduler for coverage, which utilizes the predicted mobility information. We then extend this method to a fully distributed version and propose an approximation algorithm by exchanging messages among the sensors. Using a Markovian and a car-following availability models, we show by simulations that the number of activated sensors is significantly reduced by utilizing predicted mobility information. After that, we analyze both schedulers to quantify the gain of utilizing mobility information in sensing. We adopt an independent node mobility model due to its tractability. The analysis is composed of two main components; calculation of mobility gain in terms of sensing cost and probability of feasibility. Our analysis and simulations demonstrate the gain of mobility in sensing targets in terms of higher probability of feasibility and lower sensing cost.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".