A conceptual framework for smart ports: Novel UAV-based pilotage protocol using flying aerial ad-hoc networks
Bibliographic record
Abstract
The increasing complexity of port operations, driven by growing container volumes and larger vessel sizes, has made efficiency a critical priority. Traditional vessel guidance to the quayside relies on pre-installed buoyage systems with geometrically colored landmarks, which are costly, safety-critical, and expose pilots to significant risks. In this paper, we propose a novel approach by introducing a Flying Aerial Ad-Hoc Network (FANET)-based vessel pilotage protocol as part of a smart port concept. This innovative system replaces the IALA buoyage system with UAV-guided vessel navigation, leveraging advanced sensing and communication infrastructure to enable precise berthing operations. Furthermore, it incorporates coordinated UAV charging processes to maintain operational continuity. Our work addresses a significant gap in existing FANET routing protocols, which often neglect the integration of UAV routing and mobility models tailored for specific pilotage tasks, such as charging coordination. By employing reinforcement learning (RL) techniques, the proposed methodology aims to optimize vessel guidance, planning, and scheduling, offering an intelligent port system capable of determining optimal trajectories. This novel approach not only enhances operational efficiency but also sets the foundation for modernizing maritime navigation and port management with cutting-edge UAV technologies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".