A Reverse Auction Based Incentive Mechanism for Mobile Crowdsensing
Bibliographic record
Abstract
Incentive mechanism design is a critical issue in mobile crowdsensing and a lot of work has been carried out. However, existing mechanisms in this area generally lack of consideration of individual worker/candidate's (potential) contribution to the system when recruiting new workers or when detaining existing workers. In this paper, we design a reverse auction based incentive mechanism. The design objective is to maximally reduce the system maintenance cost (including auction cost and recruitment cost) by optimizing the composition of workers in the system. For this purpose, in the recruiting process, candidates are queried in the descending order of their potential contributions to the system, while in the detaining process, likelydropping-out workers are rewarded with inner lottery whose amount is adjusted based on their usefulness to the system. In the auction process, prices are calculated based on workers' bids and also their usefulness to the system. We present detailed mechanism design. Simulation results show that our mechanism outperforms existing work.
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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 | 0.000 |
| 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".