An AUV-Assisted Data Collection Approach for UASNs Based on Hybrid Clustering and Matrix Completion
Bibliographic record
Abstract
Underwater Acoustic Sensor Networks (UASNs) have emerged as pivotal contributors to various domains. Never-theless, UASNs grapple with intrinsic challenges, notably propa-gation delays, heightened energy consumption, and inconsistent transmissions, which cumulatively impair the fidelity of under-water communication. Given these challenges, the exigency for a mechanism that assures both energy efficiency and reliable data collection becomes paramount. In this paper, we propose a data acquisition framework with the support of Autonomous Underwater Vehicles (AUVs), utilizing the Hybrid Clustering and Matrix Completion (AHMC) methodology, aiming to enhance the efficiency of data collection in UASNs. Initially, our approach har-nesses a novel hybrid clustering strategy, melding the virtues of the fuzzy c mean (FCM) algorithm with the Firefly Optimization Method (FA) to bolster network performance. The core strategy delineates the formation of energy-optimized clusters through FCM, post which the Elbow method ascertains the optimal K-value. Successively, the FA algorithm becomes instrumental in pinpointing the most suitable cluster head (CH) for each cluster. Furthermore, we introduce an ant colony algorithm tailored for the UASN s, encapsulating factors like energy, transmission latency, and the comprehensive trajectory span of AUV s during their operational phase. This strategic inclusion refines the pheromone update model, seamlessly amalgamating multifaceted elements to chart an optimally efficient AUV pathway. To cul-minate, the intra-cluster data aggregation is honed via a matrix completion methodology. Simulation results attest to the superior performance of our AHMC paradigm, showcasing commendable metrics in energy efficiency, latency, and network lifetime.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".