DEAC: Depth and Energy Aware Cooperative Routing Protocol for Underwater Wireless Sensor Networks
Bibliographic record
Abstract
In Underwater Wireless Sensor Networks (UWSNs), reliability is one of the major concerns for large number of applications. The underwater environment is very harsh and noisy. Fading is common and unavoidable, therefore achieving reliable data transfer requires innovative routing solutions. This paper presents a energy efficient cooperative routing with varying Depth threshold (Dth) called Depth and Energy Aware Cooperative Routing Protocol for UWSNs (DEAC). DEAC utilizes the broadcast nature of sensor nodes by performing cooperative routing. Optimised value of Dth is selected for a source node and varied according to the number of alive neighbors of that source node. Potential destination node is selected from outside of Dth and a potential relay node is selected from inside. Destination and relay are selected on the basis of depth, residual energy and link quality between sensor nodes. Source node forwards a data packet to destination node from two ways, directly from source node to destination node and via relay to destination node. At destination, two data packets received from source node and relay node are combined using Maximum Ratio Combining Technique (MRC). Simulation results show that DEAC achieves better performance over some existing depth based routing protocols in terms of throughput, packet Acceptance ratio, packet drop 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 | 0.001 |
| 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".