Age-Optimal Information Gathering in Linear Underwater Networks: A Deep Reinforcement Learning Approach
Bibliographic record
Abstract
In this paper, we consider an underwater linear network, where an autonomous underwater vehicle (AUV) gathers data from a set of underwater devices. The AUV monitors a set of physical processes, where the status of each process can be sensed by one or more devices and each device is capable of sensing one or more processes. The AUV needs to maintain freshness of its information status about the monitored processes. To quantify the freshness of the information at the AUV, we consider the concept of the age of information (AoI), which represents the amount of time elapsed since the most recently delivered update information was generated. A framework is proposed to optimize the AUV's linear movement trajectory and scheduling of process status updates with the objective of minimizing the normalized weighted sum of the average AoI of the monitored physical processes. The formulated optimization problem is a non-convex mixed integer problem, which cannot be solved by the standard optimization techniques. We develop a solution approach based on the technique of deep reinforcement learning (DRL). Specifically, we leverage an actor-critic DRL approach to find the optimum locations and stopping time of the data gathering points. Simulation results illustrate that the proposed framework maintains robustness under different scenarios and provides better performance when compared with baseline and$K$-means clustering approaches.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".