Air-Ground Collaborative Mobile Crowdsensing by Predictive Multi-Agent Deep Reinforcement Learning
Bibliographic record
Abstract
Mobile crowdsensing (MCS) by human participants and unmanned aerial vehicles (UAVs) is an emerging air-ground collaborative data collection paradigm by navigating a group of UAVs to collaborate with human participants to provide large-scale and fine-grained sensing services. In this paper, we aim to optimize the trajectory design of UAVs by jointly considering the collected data volume, geographical sensing fairness, and limited energy reserve during the serving period. To achieve the long-term serving objective, we propose a human participants distribution prediction based multi-agent deep reinforcement learning method for efficient UAV navigation to collaborate with human participants in performing MCS tasks. Specifically, we first introduce a region division based human participant spatial distribution prediction method to help UAVs to collaborate with human participants by the predictive mobility flows. Then, we present the multi-agent proximal policy optimization (MAPPO) based method for efficient UAV navigation decision-making. Extensive simulations and trajectory visualization using the real-world mobility dataset in KAIST show that the proposed method consistently outperforms the state-of-the-art in terms of the energy efficiency when varying the number of UAVs and human participants.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".