An Efficient Partially Correlated Task Assignment Algorithm for Mobile Crowdsensing
Bibliographic record
Abstract
Task assignment is a critical issue in mobile crowd-sensing, which is aimed to maximize the number of completed tasks subject to budget constraints. However, existing work in this aspect did not consider the correlation between the tasks submitted by the same task requester. That is, tasks in the same subset from the same task requester are often correlated such that they are considered completed only when all of them are completed, and partial completion of them are useless. This requirement largely affects the performance of existing algorithms for the assignment of such partially correlated tasks. In this paper, we formulate the problem of maximizing the total number of completed tasks subject to such correlation and also budget constraints as an integer programming problem. We propose two greedy algorithms, one is requester happiness utility based algorithm and the other is minimum task remaining subset first algorithm. We present design details of both algorithms and deduce their computational complexities. Numerical results demonstrate that these two algorithms can significantly outperform the existing work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".