Reliability Analysis of Low-Cost Node Designs for Internet of Underwater Things
Bibliographic record
Abstract
The reliability analysis has been focused on research in many domains. Reliability in Internet of Things (IoT) has been extensively addressed since IoT is one of the building blocks of many critical systems. Nevertheless, reliability analysis has not gained significant attention within the context of Internet of Underwater Things (IoUTs). This is critical since IoUT infrastructures are costly due to the high cost of underwater nodes and operations for node repair and replacement are challenging. Recent works have designed low-cost underwater sensor nodes for the experiments and validation of networking protocols for IoUTs. Such low-power, low-cost nodes can be attractive for IoUT heterogeneous deployments. Therefore, it is advantageous to perform a reliability analysis of low-cost underwater nodes designed from off-the-shelf components. This paper presents a model for the reliability analysis of low-cost underwater nodes. The proposed approach models the node’s reliability from the reliability of its individual components. As a case study, we conduct a reliability analysis of two low-cost node designs provided in the literature. The obtained results show that the reliability of low-cost underwater nodes can be diminished by the use of a series of many off-the-shelf components.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".