Location-Dependent Task Allocation for Mobile Crowdsensing With Clustering Effect
Bibliographic record
Abstract
Mobile crowdsensing (MCS) offers a promising paradigm for large-scale sensing with the rapid growth of mobile smart devices. Compared with traditional sensing methods, MCS is more effective and efficient in energy and cost. Task allocation is a key problem in MCS, which has a significant impact on the performance. It is challenging to design a generic solution to the task allocation problem because MCS applications typically consider distinct targets under specific constraints. However, there are many common interests such as data quality, budget, and energy consumption. In this paper, we analyze and formulate the task allocation problem from two perspectives, respectively. First, we focus on data quality and propose a genetic algorithm (GA) to maximize data quality. Then, we take the profit of workers into account and propose a detective algorithm (DA) to improve the profit. In the GA-based solution, only the platform is able to decide the task assignment. However, in the DA-based solution, the workers are allowed to determine and submit their task sets to the platform, which just needs to make a selection from these task sets. In addition, we consider the clustering effect of tasks and the influence caused by different geographic distributions of tasks. To evaluate the performance of the proposed solutions, extensive simulations are conducted. The results demonstrate that our proposed solutions outperform the baseline algorithm and there is a tradeoff between the data quality and the profit of workers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".