Balancing between robustness and energy consumption in underwater acoustic sensor networks
Bibliographic record
Abstract
In recent years, underwater acoustic sensor networks (UWSNs) are envisioned for different potential applications, ranging from long-term marine environmental monitoring, industrial instrumentation control, to military surveillance and security. Compared to wireless sensor networks (WSNs), energy-efficient data transmission becomes more critical in UWSNs due to non-rechargeable batteries of sensor nodes with limited amount of energies in long-term marine monitoring applications. Besides, in underwater acoustic communications, transmitting and receiving power levels dominate the energy consumption during the data transfer. Data packet retransmission caused by network deployment error increases the energy consumption and reduce the network lifetime. In this paper, we investigate a two-dimensional deployment strategy of UWSNs with a square grid topology. We present a mathematical model to study the deployment error of UWSNs. Based on this model, a parameter, i.e., α, is introduced to balance the network robustness and the energy consumption of sensor nodes. α is defined as the ratio of the transmission range of a sensor node to the distance between two closest adjacent sensor nodes. We report optimal values of α corresponding to this balance for different sizes of UWSNs.
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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 | 0.000 | 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 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".