Dynamic Slice-Based Privacy-Preserving Data Aggregation for UWSNs
Bibliographic record
Abstract
Underwater Wireless Sensor Networks (UWSNs) are integral to marine exploration yet confront significant security concerns. In contrast to terrestrial WSNs, underwater acoustic channels are characterized by their constrained bandwidth, considerable propagation delays, and a higher bit error rate. These factors facilitate adversaries’ ability to intercept network transmissions and acquire sensitive data. Furthermore, the constraints of energy resources in UWSNs pose additional challenges in harmonizing privacy preservation with energy conservation. This study introduces a novel Privacy-Preserving Data Fusion Algorithm (DSPDA) tailored for UWSNs. The DSPDA circumvents the shortcomings of conventional privacy algorithms by employing dynamic sharding to minimize transmission demands and augment data fusion precision, which adjusts the size of data shards relative to nodal distances, thereby safeguarding confidentiality. Furthermore, it enhances network-wide energy optimization by aligning the energy consumption of cluster heads with their respective child nodes, thus averting disproportionate energy depletion and potential network dysfunction. Our simulation results demonstrate that the DSPDA algorithm notably diminishes communication overhead by approximately 37% in comparison to the SMART algorithm and delivers a further 20% efficiency improvement over the EEHA algorithm.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".