On Enhancing Network Reliability and Throughput for Critical-range based Applications in UWSNs
Bibliographic record
Abstract
Underwater Wireless Sensor Networks (UWSNs) support various applications like pollution monitoring, tsunami warnings, offshore exploration, tactical surveillance, etc. Distinctive features of UWSNs like low available bandwidth, large propagation delay, highly dynamic network topology, and high error probability pose many challenges for designing efficient and reliable communication protocols. In this paper, we propose an extension of IAMCTD (Improved Adaptive Mobility of Courier nodes in Threshold-optimized DBR protocol for UWSNs) that focuses on enhancing network reliability and throughput for critical-range based applications. Our scheme avoids control overhead that was present in IAMCTD for implementing changes in depth threshold. The movement pattern of courier nodes along with reducing communication burden on nodes increases throughput as well. Additionally, stability period is improved and node density per round remains comparatively high improving the overall network reliability. Based on the comprehensive simulations using MATLAB, we observe that our scheme improves the performance in terms of throughput and stability period. Moreover, comparatively higher network density per round is maintained and end-to-end delay is stabilized throughout the network lifetime.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".