Design of Algorithms and Protocols for Underwater Acoustic Wireless Sensor Networks
Bibliographic record
Abstract
Nowadays, with the recent advances of wireless underwater communication and acoustic sensor devices technology, we are witnessing a surge in the exploration and exploitation of the ocean’s abundant natural resources. Accordingly, to fulfill the requirements of the exploration of the ocean, researchers have focused their work toward the design of methods and algorithms for the underwater acoustic sensor networks (UASNs). Although considerable research effort has been devoted to the development of a variety of UASN-based applications, very limited work has addressed the algorithmic design and analysis for UASN. To this end, we propose to provide a comprehensive design, development, and analysis of algorithms and protocols for UASNs. We discuss each of the fundamental UASN building blocks, such as (i) underwater acoustic communication channel modeling, (ii) sustainable coverage and target detection, (iii) Medium Access Control (MAC-layer design and time synchronization, (iv) localization algorithms design, and (v) underwater routing protocol. Then, we illustrate the different protocols from each category and compare their benefits and drawbacks. Finally, we discuss a few potential directions for future research related to the design of future generations of UASNs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".