Profit-Oriented Task Allocation for Mobile Crowdsensing With Worker Dynamics: Cooperative Offline Solution and Predictive Online Solution
Bibliographic record
Abstract
Mobile crowdsensing (MCS) is a new paradigm of data collection with large-scale sensing. A group of mobile users are recruited as workers to move around in a specific region and carry out sensing tasks. A challenging problem of MCS is task allocation, especially when the MCS platform needs to assign tasks to selected workers among a large user pool and consider mixed spatial and temporal features, including locations and time windows of tasks, and trajectories and arrival time of workers. In this paper, we take into account these features and study the task allocation problem that assigns tasks to workers over time and guarantees the tasks are accomplished before their deadlines. We consider an offline scenario where the MCS platform is informed of all the information of tasks and workers in advance, and an online scenario where the platform does not know the information of workers before they enter the system. For the offline scenario, we provide a cooperative ant colony algorithm with swarm intelligence to approximate the optimal solution in large-scale cases. For the online scenario with incomplete information, we propose several online algorithms, among which the predictive online algorithm exploits historical records of workers and performs the best. Finally, we conduct simulations and evaluate the differences among the online solutions and offline solutions. The results show that the proposed online solutions can approach the offline optimal solution in small-scale cases, and its approximation obtained by the cooperative offline solution in large-scale cases.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".