Reinforcement learning-based IoT sensor scheduling strategy for bridge structure health monitoring
Bibliographic record
Abstract
Internet of Things (IoT) based Bridge Structural Health Monitoring (BSHM) is a hot topic in the field of civil engineering and computer science, and has been widely concerned by academia and industry. The lifetime of the sensors is much less than that of the bridge, which is one of the main technical bottlenecks in BSHM. Therefore, how to effectively improve the network lifetime is the focus of current research. Based on reinforcement learning and Fisher information matrix, this paper proposed a node sleep scheduling strategy by using the learning automata model and confident information coverage (CIC) model to learn the optimal sensor sleep scheduling strategy through cooperative sensing among nodes. Fisher information matrix, which is widely used in civil engineering, was introduced to define the node sleep scheduling problem as a multi-objective optimization problem. With information validity as the modal assurance criterion, the network performance was measured by combining energy efficiency, network coverage requirements and network connectivity. While ensuring the network connectivity and coverage requirements, the system parameter identification error is minimized and the network life is maximized. Through the simulation of jiangbei Bridge in Guangdong, China, the effectiveness, energy efficiency and applicability of the proposed scheme are verified.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".