Underwater sensor networks: a new challenge for opportunistic routing protocols
Bibliographic record
Abstract
Opportunistic routing (OR) is a promising paradigm that selects the next-hop forwarder on the fly. OR has gained a lot of attention from the research community for its ability to increase the performance of wireless networks. In OR a potential group of nodes (candidates) is selected to help as the next-hop forwarder. Each candidate that receives the packet can continue forwarding the packet. In OR, by using a dynamic relay node to forward the packet, the transmission reliability and network throughput are increased. Underwater sensor networks (UWSNs) collect data from the environment and transfer them to the sonobuoys on the surface to send them to a center for further processing. Because of the acoustic channels common to UWSNs, they have low bandwidth, high error probability, and longer propagation delay compared to radio channels. These properties of UWSNs make them good potential candidates for using OR concepts to deliver packets to the destination. This article reviews and compares different OR protocols proposed for UWSNs. We classify the existing approaches in different categories, discuss representative examples for each class of protocols, and uncover the requirements considered by the different protocols, as well as the design requirements and limitations under which they operate. Finally, we discuss potential future research directions for UWSNs using the OR paradigm.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".