Reliability-Driven Task Assignment in Vehicular Crowdsourcing: A Matching Game
Bibliographic record
Abstract
Vehicular crowdsourcing is an emerging mobile sensing-based paradigm in which a service platform manages the allocation of data collection and processing tasks to vehicles according to their itinerary, expected response time, and optimal reward. However, selfish or malicious vehicles may take advantage of the platform to maximize their profit or even attack the deployed sensing applications and compromise their decisions by transmitting unreliable data, which can degrade the quality of delivered services and negatively affect the platform reward and reputation. In this paper, we design a task assignment mechanism for vehicular crowdsourcing based on vehicles' reliability to protect the platform from such threats. First, a beta reputation system is adopted for probabilistic reliability assessment according to vehicles' behavior. Then, a new multi-dimensional task assignment problem, which proves to be NP-complete, is modeled as a many-to-one matching game between the platform and vehicles by defining reliability and reward-based preference functions. Finally, a distributed matching algorithm that aims at maximizing the platform reward by assigning the tasks to reliable participants is presented. The mechanism also achieves privacy-preservability by enabling vehicles to engage in the task allocation process without necessarily unveiling their sensitive information. Results show that the proposed mechanism increases the quality of crowdsourcing services by assigning the tasks to reliable and benign vehicles, and adheres to the scalability requirements of the system.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".