Efficient Task Allocation for Mobile Crowd Sensing Based on Evolutionary Computing
Bibliographic record
Abstract
Mobile crowd sensing (MCS) offers a promising paradigm for big data collection in a large scale. It leverages the power of mobile smart devices, and shows various advantages over traditional sensing networks, such as high energy efficiency, cost-effectiveness, and flexibility. A key problem in MCS is to efficiently allocate distributed tasks to mobile users (MUs) while addressing various constraints, e.g., in terms of the quality of sensed data and collection cost. In this paper, we further take into account the clustering effect of sensing tasks and propose an efficient approach to solve the NP-hard task allocation problem. In our solution, a variant genetic algorithm (GA) is utilized to maximize the task complete ratio and balance the sensed data among tasks while respecting the MUs' constraints. Considering the unique characteristics of the task allocation problem, we divide the crossover and mutation process of the GA into two steps. First, an available subset of tasks is selected for each MU. Then, a feasible travelling path is designed over this subset of tasks in the second step. The simulation results show that the proposed GA-based solution significantly outperforms the baseline solution in terms of task complete ratio and data balance.
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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 | 0.001 | 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 | 0.001 | 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".