A Novel Harvesting-Aware RL-based Opportunistic Routing Protocol for Underwater Sensor Networks
Bibliographic record
Abstract
Underwater wireless sensor networks (UWSNs) have the potential to empower smart ocean applications. However, the widespread use of UWSN applications has been limited due to the many daunting challenges incurred for underwater wireless acoustic communication. Moreover, underwater wireless communication is energy-hungry, which confines UWSN deployment to small-scale due to the risks and costs of missions for at sea replacement of the nodes' battery. The energy harvesting capability of underwater sensor nodes is an important characteristic that has been overlooked in the literature. In this paper, we study the data routing process in UWSNs with energy harvesting capabilities. We proposed a novel opportunistic routing protocol, named RELOR, that is the first in the literature to consider the energy harvesting capability of underwater sensor nodes during routing decisions. RELOR implements a learning framework for the best selection of the forwarder nodes based on the observed environment conditions. We conduct extensive simulations to compare the performance of the proposed protocol to the state-of-the-art solution. Obtained results show that RELOR outperforms the related work in terms of packet delivery ratio, end-to-end latency, and nodes' energy consumption.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".