Digital Twin and Semantic-Aware Multi-Agent RL for Maritime Search and Rescue Operations
Bibliographic record
Abstract
Effective maritime search and rescue (SAR) requires fast, coordinated action from Internet of Maritime Things (IoMT) nodes operating under extreme communication, energy, and environmental constraints. Existing solutions treat semantic sensing, digital twin modeling, and decentralized control in isolation, limiting their responsiveness and scalability. We propose SEMADT-RL, a unified framework that integrates semantic-driven communication, predictive digital twin forecasting, and decentralized multi-agent deep reinforcement learning with graph attention networks (MADRL-GNN). The semantic layer enables lightweight, anomaly-triggered updates, significantly reducing bandwidth while preserving critical detection cues. The digital twin assimilates these updates using an extended Kalman filter to forecast survivor drift and node dynamics. These forecasts guide decentralized agents that collaboratively optimize mobility, processing, and transmission policies under dynamic and constrained maritime conditions. Simulation results demonstrate that SEMADT-RL achieves faster survivor detection, lower communication overhead, and higher energy efficiency than state-of-the-art baselines, providing a scalable solution for next-generation IoMT-assisted SAR operations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".