A Comprehensive Survey of Security Schemes and Privacy-Preserving Techniques for the Internet of Underwater Things
Bibliographic record
Abstract
With the growing interest that is being shown in marine resources, the concept of the internet of things (IoT) has been extended to underwater scenarios, which has given rise to the internet of underwater things (IoUT). The IoUT encompasses a network of interconnected intelligent underwater devices that can be used to monitor underwater environments and support various applications, such as underwater exploration, disaster prevention, and environmental monitoring. Advances in underwater wireless communication and sensor technologies have propelled the IoUT concept forward. However, the IoUT faces significant challenges. The harsh and vast underwater environment make information sensing particularly difficult and lead to insufficient or inaccurate data being collected. Additionally, underwater conditions like pressure variation, hydrological characteristics, temperature changes, water currents, and topography hinder conventional communication models and make data transmission difficult and inefficient. Security in IoUT networks is a critical concern due to hardware limitations and seawater channel imperfections. Constrained sensor nodes and spatial-temporal uncertainty introduced by node mobility further complicate security provisioning. This survey paper addresses these challenges by offering a comprehensive overview of IoUT security. The investigation thoroughly examines both traditional and classic machine learning techniques, and focuses on deploying advanced technologies such as federated learning and digital twin. The study effectively addresses integration challenges and open issues, and provides a roadmap for future directions to play a pivotal role in formulating robust security mechanisms for IoUT networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".