A Truthful Location-Protected Mobile Crowdsensing Framework with User Mobility
Bibliographic record
Abstract
Mobile Crowdsensing (MCS) is a promising paradigm for the large-scale sensing. In this paper, we aim to build a truthful framework of MCS taking user mobility and incentive into account. The proposed framework is composed of two challenging problems, path planning and incentive mechanism design. In path planning, every user as a worker independently plans a tour to carry out tasks based on its own strategy. In incentive mechanism design, the platform leverages a mechanism to select the winners and determine the payments. To solve these two problems, a heuristic bidirectional searching algorithm is proposed for path planning and an incentive mechanism is designed. The proposed mechanism is proved to be computationally efficient, individually rational, and truthful. Finally, the simulation results show that our proposed heuristic algorithm outperforms the baseline algorithms and approaches the optimal solution in path planning. Our proposed mechanism has a total payment smaller than that of a Vickrey-Clarke-Groves (VCG) mechanism.
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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 | 0.000 | 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 | 0.001 |
| 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".