Performance Analysis of Amplify and Forward Technique in Region Based Cooperative Routing for Underwater Wireless Sensor Networks
Bibliographic record
Abstract
In this paper, we propose a region based cooperative routing protocol (RPCRP). This protocol performs analysis of amplify and forward technique over Rayleigh fading channels. The source node sends the sensed signal to the destination and available relay nodes. At the destination node, bit error rate (BER) is checked on the basis of which, either positive or negative acknowledgement (ACK or NACK) is sent to the source and relay nodes. If the positive feedback is received from the destination node, the relay nodes drop the packet. However, in case of negative feedback, the best relay node amplifies the signal. After the signal is amplified, it is forwarded to the destination node. Moreover, the mobile sinks (MSs) change their position after some time and cover the whole network are also deployed. The nodes that lie within the transmission range of MSs forward their data directly to the sink. Also, the mathematical equations for the total SNR gain and outage probability are verified by simulations. Results show that RBCRP outperforms incremental best ralay technique (IBRT) in terms of throughput and network lifetime. Also, the mathematical analysis for outage probability shows that RBCRP is 62 % more better than IBRT.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".