Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
Bibliographic record
Abstract
Much attention has been focused lately on the Opportunistic Routing technique (OR) that can overcome the restrictions of the harsh underwater environment and the unique structures of the Underwater Sensor Networks (UWSNs). OR enhances the performance of the UWSNs in both packet delivery ratio and energy saving. In our work; we propose a new routing protocol; called Energy Efficient Depth-based Opportunistic Routing with Void Avoidance for UWSNs (EEDOR-VA), to address the void area problem. EEDOR-VA is a reactive OR protocol that uses a hop count discovery procedure to update the hop count of the intermediate nodes between the source and the destination to form forwarding sets. EEDOR-VA forwarding sets can be selected with less or greater depth than the packet holder (i.e., source or intermediate node). It efficiently prevents all void/trapped nodes from being part of the forwarding sets and data transmission procedure; thereby saving network resources and delivering data packets at the lowest possible cost. The results of our extensive simulation study indicate that the EEDOR-VA protocol outperforms other protocols in terms of packet delivery ratio and 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".