SMIC: Sink Mobility with Incremental Cooperative Routing Protocol for Underwater Wireless Sensor Networks
Bibliographic record
Abstract
The acoustic environment suffers from a number of impairments which effect transmitted data reliability and integrity leads toward low-quality routing. Integral part of cooperative routing is reliable data delivery with trade-off energy consumption is high, because of multiple transmissions. In order to overcome this problem and getting advantage of cooperation routing, we proposed a scheme Sink Mobility with Incremental Cooperative Routing (SMIC) which involves Mobile Sinks to reduce energy consumption and achieve reliable data transfer. In this paper, selection parameter for relay and destination node is node's depth, residual energy and link quality (Signal-to-Noise Ratio) to achieve quality routing. Energy efficiency is achieved by optimized mobility pattern of Mobile Sinks (MSs) and using Amplify and Forward (AF) incremental cooperative routing which helps in efficient utilization of resources by using them, when needed. The proposed work is validated via simulations which show the relatively improved performance of our proposed protocol in terms of the selected performance metrics.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".