Minimising number of sensors in wireless sensor networks for structure health monitoring systems
Bibliographic record
Abstract
Sensor placement optimisation is an important problem in the field of structural health monitoring (SHM). Many researchers solve this problem by focusing only on the network requirements, without considering the civil engineering requirements. However, there are researchers that optimise the sensor placement considering network and civil requirements. Unfortunately, those researchers did not address minimising the number of sensors. As a result, in this research we study the problem of minimising the number of sensors for SHM in wireless sensors networks satisfying both civil and network requirements. The authors’ contribution in this work is showing the mathematical model of the mentioned problem. Then, solve the problem using different methods: exhaustive search, genetic algorithm (GA), and a numerical iterative algorithm that applies binary search (BS). The problem is solved using different number of sensors as well as different placements in many conducted experiments. The obtained results showed that minimising the number of sensors becomes more significant with big structures. Furthermore, the BS algorithm is the best to use to solve the problem for small buildings. However, for larger buildings, there is a trade‐off between the performance, and time complexity, where the BS gives optimal solution, but GA gives better time execution.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".