On the Optimality of Opportunistic Routing Protocols for Underwater Sensor Networks
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Bibliographic record
Abstract
In the last decade, underwater wireless sensor networks (UWSNs) have attracted a lot of attention from the research community thanks to their wide range of applications that include seabed mining, military and environmental monitoring. With respect to terrestrial networks, UWSNs pose new research challenges such as the three-dimensional node deployment and the use of acoustic signals. Despite the large number of routing protocols that have been developed for UWSNs, there are very few analytical results that study their optimal configurations given the system's parameters (density of the nodes, frequency of transmission, etc.). In this paper, we make one of the first steps to cover this gap. We study an abstraction of an opportunistic routing protocol and derive its optimal working conditions based on the network characteristics. Specifically, we prove that using a depth threshold, i.e., the minimum length of one transmission hop to the surface, is crucial for the optimality of opportunistic protocols and we give a numerical method to compute it. Moreover, we show that there is a critical depth threshold above which no packet can be transmitted successfully to the surface sinks in large networks, which further highlights the importance of properly configuring the routing protocol. We discuss the implications of our results and validate them by means of stochastic simulations on NS3.
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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 | 0.000 |
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 it