Dynamic Scheduling for Quality of Information Maximization in Location-aware Opportunistic Mobile Crowdsensing
Bibliographic record
Abstract
The recent emergence of unmanned aerial vehicles (UAVs) technology has brought up enormous potential applications. However, operating the underlying UAV networks requires an accurate understanding of the dynamic 3D wireless spectrum conditions in real-time. To achieve this, Opportunistic mobile crowdsensing (OCS) has been considered as a cost-effective solution by recruiting mobile devices for spectrum information gathering. Due to a lack of sensing and transmission coordination, existing OCS approaches cannot gather spectrum information with sufficient quality in the data acquisition process, leading to deteriorated UAV network operation performance. To address this challenge, we propose a novel dynamic scheduling mechanism that maximizes the quality of information (QoI) in the location-aware OCS scheme. By jointly allocating sensing and transmission resources, this approach achieves QoI maximization by reducing the OCS communication and sensing overhead. The problem is formulated as a stochastic network optimization problem and uses the Lyapunov optimization theory for solution development. We propose a decentralized sensing scheduling and a centralized dynamic transmission scheduling mechanism to solve the sub-problems. To evaluate the performance of our proposed solution, we conduct extensive simulations and compare our results against other crowdsensing schemes. The simulation results demonstrate the convergence and performance of the proposed approach and show that it outperforms other crowdsensing schemes. The proposed approach maximizes the QoI by dynamically allocating resources while minimizing the communication and sensing overhead.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".