On Coalitional and Non-Coalitional Games in the Design of User Incentives for Dependable Mobile Crowdsensing Services
Bibliographic record
Abstract
In the Era of massive connectivity, crowdsensed data services are becoming attractive so to acquire sensory data from smart mobile devices that are recruited for cooperative collection of sensed data through embedded sensors. Despite its benefits to offer sensing as a service by taking advantage of the non-dedicated nature MCS systems, it remains certain challenges such as user privacy and data trustworthiness, which could affect the intention of an individual to participate in the sensing phenomena. Furthermore, in the presence of physical tampering or falsification of sensor readings, trustworthiness of crowd-sensed data arises as a big concern. Game theoretic solutions are popular to address the utility maximization needs of participants, and the trustworthiness objectives of the MCS platforms. We revisit coalitional game formation and subgame perfect equilibrium-based concepts to motivate more users for truthful participation, and present a comparative study of these game models from the standpoint of platform utility, user utility and data trustworthiness. Our case studies show that the subgame perfect equilibrium-based model can provide higher user utility and up to 30% higher platform utility in comparison to the coalitional recruitment model in the presence of a large adversary population in an MCS campaign. Furthermore, regardless of the adversary population, repeated subgame-based model results in lower disinformation probability in the system when compared to coalitional game-based user incentivization.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".