EEORS: Energy Efficient Optimal Relay Selection Protocol for Underwater WSNs
Bibliographic record
Abstract
In this paper, an energy efficient optimal relay selection protocol is proposed for underwater wireless sensor networks (UWSNs). Sensor nodes are randomly deployed in a three dimensional underwater network and is partitioned into three zones based on depth. The mid zone contains the relay nodes. Nodes in the relay zone are assigned values on the bases of location and depth. Nodes residing in the center of the relay zone are assigned highest location values. These values decrease as the nodes become farther from the center. Values are also assigned to the relay nodes based on depth. The optimal relay is the one having the highest (maximum) location and depth values. If the optimal relay is within the transmission range of the source nodes in the bottom zone, they send the data packets to the optimal relay that further forwards them to the sink. Direct transmission from source to sink at the expense of more energy is accomplished when the optimal relay lies outside the transmission range of the source nodes. Nodes in the top zone send data directly to sink. Relay nodes in the mid zone send the data to sink either directly or through the optimal relay node. When the most optimal relay node dies, the second optimal node becomes the most optimal relay node. Simulation results show that the proposed scheme outperforms the counterpart scheme in terms of energy efficiency due to the selection of the optimal relay.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".