Longevity-Oriented and Reliable Forwarding Percolation Routing in Underwater Acoustic Sensor Networks
Bibliographic record
Abstract
In underwater acoustic sensor networks (UANs), reliable packet delivery is critical in data collection and monitoring of the oceans. It primarily relies on the design of routing protocols to guarantee network durability and connectivity. However, utilizing routing design to achieve enhanced network longevity and reliable packet forwarding is challenging due to the complex underwater environment, unstable link connectivity, high transmission power, and high propagation latency. Thus, we propose a novel routing strategy called the longevity-oriented and reliable forwarding percolation (LRP) routing protocol. The goal of LRP is to ensure reliability by exploring multipath percolation-based routing and extend network longevity by energy control and residual energy optimization. Network reliability can be estimated using a built-in calculation model, and the source node controls energy and records the residual energy to extend the network lifetime. Utilizing an optimization of the network reliability requirement and residual energy, we develop a routing strategy to select the activated link set and node set for each hop in an energy-saving and reliable way. Moreover, a recursive approach is used to avoid the occurrence of void regions. Simulation results exhibit the effectiveness of the power control and routing strategy and demonstrate its superiority over the benchmarks in terms of network longevity and reliability.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".