Enhancing Source Location Privacy in UASNs: A Multi-Armed Bandit and Pseudopacket Scheduling Approach
Bibliographic record
Abstract
Underwater Acoustic Sensor Networks (UASNs) have garnered significant interest over recent decades, yet they continue to confront substantial challenges concerning security and privacy. The inherent openness of acoustic communication in such networks introduces profound risks to node privacy and overall network security. External attackers are capable of retracing data streams to identify the source node, thus jeopardizing the confidentiality of the data origin. Notably, the subtleties of passive attacks in UASNs render them more elusive compared to active attacks. Furthermore, conventional research often fails to reconcile security needs with energy efficiency. Addressing these concerns, this paper concentrates on mitigating passive attacks in UASNs while striving to harmonize security with network performance. We introduce a Source Location Privacy Scheme based on the Multi-Armed Bandit and Pseudo-packet Scheduling for UASNs (MP-SLP). The methodology commences with the sink node executing geographic data acquisition and neighbor node identification using a flood-based technique. Subsequently, through a Multi-Armed Bandit (MAB) framework, the source node designates an intermediate node for data packet transmission. The sink node then employs a location-aware opportunistic routing strategy to establish authentic routes that combine both randomness and reduced energy consumption for the transmission of actual packets. Alongside, branch nodes implement a pseudopacket scheduling tactic aimed at obstructing adversarial efforts to track source locations. Simulation results demonstrate that the proposed scheme proficiently moderates additional energy consumption, maintaining them within acceptable limits, while safeguarding location privacy.
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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 | 0.001 |
| 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".