Privacy-Aware Online Task Assignment Framework for Mobile Crowdsensing
Bibliographic record
Abstract
Mobile crowdsensing is a new sensing paradigm exploiting potential of crowds to collect data, which has various advantages over traditional sensor networks such as low cost, high coverage, and high mobility. Privacy preservation is a crucial issue in mobile crowdsensing because worker privacy might be exposed if workers share their location information to service platform or other workers. In this paper, we assume workers can determine their own privacy preservation levels and they do not need to upload their location information to the platform or share to other workers for sensing behavior coordination. Moreover, workers move to task locations to collect sensing data in a distributed manner. We accordingly propose a privacy-aware online task assignment framework to achieve high task coverage. In this framework, spatial task-application information in previous cycles is used to estimate worker density and an incentive pricing mechanism is designed to guide workers to collect sensing data in low-worker-density areas. We present detailed mechanism design. Extensive simulation results show that our proposed solution has much better performance than the baseline mechanism.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".