Crowd Sensing in Vehicular Networks Using Uncertain Mobility Information
Bibliographic record
Abstract
In this paper, we quantify the impact of uncertainty in predicted mobility information on the sensing cost of the mobile sensing problem (i.e. sensing of target areas) in vehicular sensor networks. We focus on vehicular sensor networks in which the communication channel is the bottleneck of the system, and sensing the environment is meant to be provided with a minimum load on the communication channel. First, we formulate an opportunistic scheduler, which does not utilize predicted mobility information in the sensing procedure. Then, we formulate a strategic scheduler, which utilizes predicted mobility information in the sensing procedure. After that, we propose two types of noise models that capture the uncertainty in knowing mobility information. We quantify the impact of uncertainty in knowing mobility information on the sensing cost of the strategic scheduler using an independent coverage model. We find that the strategic scheduler outperforms the noise-free opportunistic scheduler even when the strategic scheduler utilizes noisy mobility information up to a certain threshold of noise. This threshold is determined in the paper, and is referred to as the breaking point of the strategic scheduler. Simulation results of the breaking point closely matches our analysis, which demonstrates the correctness of the used analysis methodology. Finally, we use an estimation model for the mobility information between two points on the road, and we propose an algorithm to keep the scheduling scheme below the breaking point.
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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 | 0.003 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".