Cooperative-Rationality-Based Multiplatform Task Assignment Mechanisms for Mobile Crowdsensing
Bibliographic record
Abstract
Task assignment is a key issue in mobile crowdsensing (MCS). Most existing work in this area has focused on the task assignment for the single platform scenario, which can cause considerable waste of limited human resources or reduced task completion rate due to potential spatial mismatching between distributions of users and tasks. In this article, we study multiplatform cooperative task assignment. The design goal is to maximize the social welfare while ensuring cooperative and individual rationality. We formulate this problem, transform it to a maximum value flow problem, and prove its NP-hardness. We first propose a greedy-maximum-flow-based task matching (GMTA) mechanism for interplatform task matching. In GMTA, there are two phases in each time slot: 1) in the former phase, earliest-deadline-first-based intraplatform optimal task assignment is carried out at each individual platform and 2) in the second phase, greedy-maximum-flow-based task matching is carried out to perform interplatform cooperative task assignment for those overloaded tasks in the first phase. We then enhance GMTA by designing an iterative-maximum-flow-based task matching (IMTA) mechanism, which is to achieve enhanced social welfare at the cost of increased computational overhead. We deduce time complexities of both mechanisms, and prove that they satisfy cooperative and individual rationality. Extensive simulations are conducted and the simulation results demonstrate the effectiveness of our proposed mechanisms.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.004 |
| 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".