Centralized Task Allocation for Multiple UAVs in Time-Constraint Industrial IoT Operations
Bibliographic record
Abstract
The industrial Internet of Things (IoT) allows real-time monitoring and operational efficiency by enabling automated processes in industrial environments. In this paper, we propose a centralized task assignment framework for industrial IoT scenarios, focusing on light cargo delivery to specific locations via Unmanned Aerial Vehicles (UAVs). The task assignment problem is formulated as a Capacitated Vehicle Routing Problem (CVRP) with the objective of minimizing the maximum route distance among all UAVs. To solve CVRP, we employ a learning-based approach using an Attention Model (AM), which utilizes a deep learning framework with an encoder-decoder architecture to generate optimized UAV routes while satisfying the capacity constraints of UAVs. The AM is trained using policy gradient reinforcement learning to ensure that the solutions are both efficient and scalable. Numerical results demonstrate the effectiveness of the AM-based framework in delivering solutions that minimize the maximum tour length for deliveries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".