CESense: Cost-Effective Urban Environment Sensing in Vehicular Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
In vehicular sensor networks, vehicles can act as mobile sensors to monitor the dynamic features of the physical world such as traffic flow, air quality, and temperature. However, the conventional full-coverage sensing approach is neither realizable nor cost-effective since the sensor-equipped vehicles are unevenly distributed and the environmental data are spatio-temporally correlated. To this end, we propose a cost-effective urban environment sensing solution (CESense), that exploits the sensing data correlations to improve the sensing accuracy and efficiency. CESense gathers data only at some specific areas of the whole sensing space and reliably infers the status of unsensed areas. Particularly, CESense uses a probabilistic matrix factorization model to reveal the latent features that impact the environmental status. Then, an appropriate set of sensing areas can be selected by fully taking advantage of these latent features and the sensing resource distribution patterns. In addition, to be adaptive to the dynamic environment, a checkpoint mechanism is designed to supervise the data gathering progress. Extensive experiments, which are based on the real taxicab mobility traces and air quality data collected in Beijing city, demonstrate that CESense can significantly improve the accuracy and efficiency of vehicular sensing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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