Profit maximization in mobile crowdsourcing: A truthful auction mechanism
Bibliographic record
Abstract
In mobile crowdsourcing systems, smartphones can collectively monitor the surrounding environment and share data with the platform of the system. The platform manages the system and encourages smartphone users to contribute to the crowdsourcing system. To enable such sensing system, incentive mechanisms are necessary to motivate users to share the sensing capabilities of their smartphones. In this paper, we propose ProMoT, which is a Profit Maximizing Truthful auction mechanism for mobile crowdsourcing systems. In the proposed auction mechanism, the platform acts as an auctioneer. The smartphone users act as the sellers and submit their bids to the platform. The platform selects a subset of smartphone users and assigns the tasks to them. ProMoT aims to maximize the profit of the platform while providing satisfying rewards to the smartphone users. ProMoT consists of a winner determination algorithm, which is an approximate but close-to-optimal algorithm based on a greedy mechanism, and a payment scheme, which determines the payment to users. Both are computationally efficient with polynomial time complexity. We prove that ProMoT motivates smartphone users to rationally participate and truthfully reveals their bids. Simulation results show that ProMoT increases the profit of the platform in comparison with an existing scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".