Synergy Optimized Routing Protocol for Multiobjective Optimization in Underwater Communication Networks
Bibliographic record
Abstract
Underwater communication systems face challenges, including limited bandwidth, high latency, and void areas. This article introduces synergy optimization routing protocol (SORP) for Internet of Underwater (IoU) sensor networks, emphasizing link scheduling to address localization, energy consumption, latency, network longevity, and void regions. Leveraging belief-desire-intention (BDI) and fuzzy logic, SORP offers adaptive responses to varying network conditions. Theoretical modeling using NetLogo enhances the understanding of SORP’s behavior. In evaluation, SORP consistently outperforms others. Demonstrating superior energy efficiency (3.0–3.9) compared to PPWURC, state prediction-based data collection (SPDC), balanced routing protocol based on machine learning (BRP-ML) (40–120), and energy efficient clustering routing protocol based on arithmetic progression (5–6.5), SORP proves its efficacy. Latency analysis reveals SORP consistently displaying the lowest values (2.0–2.8), surpassing packet hierarchy and void processing, SPDC, and BRP-ML (8.3–30.0). With a perfect packet delivery ratio (PDR) of 98%, SORP showcases exceptional reliability. Network lifetime analysis positions SORP as a durable option, lasting from 3985 to 4010 rounds. Validation through an underwater communication system demonstrates speeds of 5 Mb/s and above. Simulation testing reveals a transmission speed of 80 bps with latency of less than 4 s and 98% PDR. Theoretical predictions indicate significant improvements in real-time transmission, reducing latency to less than 1 s with a speed of 5 Mb/s. This research presents an innovative and practical approach to address underwater communication challenges, highlighting the efficiency and reliability of SORP in routing protocols for underwater sensor networks. The combination of theoretical modeling and real-time testing offers a comprehensive understanding, emphasizing the potential real-world impact of SORP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".