Reliability-Driven Vehicular Crowd-Sensing: A Case Study for Localization in Public Transportation
Bibliographic record
Abstract
This paper proposes a new framework for GPS-less identification of location of public transportation vehicles by using machine intelligence algorithms by exploiting the vehicular crowd-sensing concept. Since data trustworthiness is vital when data is crowd- solicited via various non-dedicated sensors, assessment and quantification of the trustworthiness of participating sensors plays a key role in the accuracy of the acquired information. To this end, we propose two trustworthiness-aware recruitment schemes for the non-dedicated sensors in a vehicular crowd-sensing environment: Reliability-driven naive recruitment (RDNR) and Reliability-driven exclusive recruitment (RDER). The former determines to use the data of a mobile device with a probability equal to the reliability of the device whereas the latter excludes the participating devices whose reliability scores are below a certain threshold. The data acquired from the recruited participant pool then undergoes an unsupervised machine learning module that is hosted in the cloud. We evaluate the performance of RDNR and RDER in comparison to each other and a non-restrictive recruitment mechanism which does not consider reliability of participants at all. Through simulations, we show that over 85% and 98% accuracy can be achieved in the worst and best cases, respectively while consuming less energy than GPS-based localization approaches.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".