Energy-Aware Routing and Data Transmission in Underwater Wireless Sensor Networks via Optimized Progressive Feedback Cascaded Visual Cosine CNN
Bibliographic record
Abstract
Underwater Wireless Sensor Networks (UWSNs) facilitate communication between deep-sea sensor nodes and surface sinks, which is essential for marine research, environmental monitoring, and surveillance. However, efficient and reliable communication is challenged by harsh underwater environments and limited resources. UWSNs suffer from high energy consumption, unstable links, and reduced transmission accuracy due to limited bandwidth, node mobility, and acoustic channel impairments. Traditional routing and transmission techniques often fail to strike a balance between energy efficiency and reliability, creating a strong need for intelligent, adaptive, and robust optimization methods. This paper proposes a novel energy-efficient framework that integrates the Fishier Mantis Optimization Algorithm (FMOA) for routing, the Progressive Feedback Cascaded Visual Cosine Convolutional Neural Network (PFCVCCNN) for data transmission, and the Sharpbelly Fish Optimization (SFO) for parameter fine-tuning. Simulation results show that the proposed framework achieves lower EC (2.5 J), higher PDR (94%), reduced PLR (6%), and superior TA (99.12%) compared to existing protocols. The integration of FMOA, PFCVCCNN, and SFO provides a scalable, adaptive, and reliable solution for energy-efficient communication in UWSNs, significantly enhancing network lifetime and data delivery performance under challenging underwater conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".