Multi-Leader Multi-Follower Game-based Incentive Scheme for Socially-Aware Mobile Crowdsensing
Bibliographic record
Abstract
As the paradigm of crowdsensing involves the data collection from users, the issue of designing reward to incentivize the users is fundamentally important to be addressed, thereby effectively enhancing the participation. In this paper, we revisit this issue in the context of socially-aware crowdsensing which integrates crowdsensing into social networks. For example, in crowdsensing-based healthcare services, the accuracy of diet recommendation for a certain user can be promoted by exploiting the nutritional information contributed and shared by the socially-connected friends of him/her taking similar types of food. To be more general and practical, we study the incentive schemes in presence of multiple crowdsensing service providers and multiple users. Understanding the behaviors of users and service providers in socially-aware crowdsensing is of paramount importance for incentive schemes. Considering this, we propose a multi-leader and multi-follower Stackelberg game approach to model the strategic interactions among service providers and users, where the social influence of users and the strategic interconnections of service providers are jointly and formally integrated into the game modeling. Through backward induction methods, we theoretically validate the existence and uniqueness of the Stackelberg equilibrium. Simulations are conducted to evaluate game equilibrium properties, and the results are presented to assess and demonstrate the performance effectiveness of the proposed game model.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".