Why this work is in the frame
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Bibliographic record
Abstract
Oceans are a great unknown. To change this worryingly reality, underwater wireless sensor networks (UWSNs) have been proposed for the automated and real-time data collection from ocean, including the life and events beneath them. Currently, the underwater acoustic channel is the most viable technology for long-range underwater wireless communication, but its use impairs the data collection in UWSNs. It presents strong signal absorption and is severely affected by human-made and natural noise in the aquatic environment. Therefore, data collection in UWSNs is unreliable. In the recent years, opportunistic routing has been proposed to improve UWSN communication's reliability and, consequently, data delivery. However, not always the proposed opportunistic routing protocols will perform well, as the neighborhood configuration of a node might not be dense enough or at a maximum distance that would favor data communication. In this paper, we proposed the power control-based opportunistic routing protocol, named PCR, for reliable and energy-efficient data delivery in UWSNs. The proposed PCR protocol selects the most suitable transmission power level at each underwater sensor node, aimed at improving the packet delivery probability at each hop. To avoid the selection of high power transmission and the uncontrolled inclusion of neighboring nodes in the next-hop candidate set, which would drastically increase the energy consumption, the PCR protocol considers the energy waste that will occur in each neighboring underwater sensor node. Numerical results showed that PCR improves the packet delivery probability and reduces the energy waste for data delivery by adjusting the proper transmission power and selecting the suitable candidate set, leading to energy conservation when compared with related proposals presented in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it