Intelligent Task Allocation for Mobile Crowdsensing With Graph Attention Network and Deep Reinforcement Learning
Bibliographic record
Abstract
Mobile crowdsensing (MCS) leverages crowd intelligence, i.e., smart devices and their owners, to collect data in an intelligent and cost-efficient manner. One of the fundamental research problems in MCS is task allocation, where a group of smart device owners are recruited as workers to reach and sense specified targets. In task allocation, task publishers submit their data collection tasks with the constraints and budgets, while workers report their estimated costs and possible constraints associated with data collection. The task allocation problem aims at allocating tasks to workers to maximize profit from the gap between the compensation to workers and the available budget while satisfying constraints from both sides. As task allocation problems are often NP-hard, heuristic schemes are widely used to obtain time-efficient results. However, the performance of heuristic methods may vary significantly in different environments, especially for NP-hard problems. To address task allocation problems in MCS, in this paper, we integrate a carefully designed graph attention network (GAT) into deep reinforcement learning (DRL) and develop a GAT-based DRL method (GDRL) to solve an NP-hard task allocation problem. Compared with manually crafted heuristics, our approach features the flexibility and self-adaptability of DRL, enabling the solver to interact with and adjust to new environments and generalize its experience to different situations. Extensive numerical results show that our proposed method can achieve significantly better results than the reference schemes in various experiment settings.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".