Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)
arXiv:2510.22517v3 Announce Type: replace-cross Abstract: Optimal sensor placement (OSP) is critical for efficient, accurate monitoring, control, and inference in complex physical systems. We propose a machine-learning-based feature attribution (FA) framework to identify OSP for target predictions. FA quantifies input contributions to a model output; however, it struggles with highly correlated input data often encountered in practical applications for OSP. To address this, we propose a Correlation-Assisted Attribution Framework (CAAF), which introduces a clustering step on the candidate senso
The increasing complexity of AI systems and physical infrastructure necessitates more efficient and robust data collection strategies, making optimal sensor placement a critical area of research.
Efficient sensor placement is fundamental for improving the accuracy and reliability of AI-driven predictive applications in diverse complex systems, from industrial control to smart cities and defence.
The proposed CAAF framework offers a more resilient, machine-learning-based approach to sensor placement, especially in scenarios with highly correlated sensor data, which was previously a challenge for feature attribution methods.
- · AI-driven predictive maintenance companies
- · Smart infrastructure developers
- · Defense and aerospace sectors
- · Machine learning researchers
- · Traditional, heuristic-based sensor placement methods
- · Systems with suboptimal sensor networks
Improved performance and decreased operational costs for systems reliant on sensor data.
Accelerated development of more reliable and autonomous AI applications across industrial and military domains.
Enhanced resilience of critical infrastructure against failures due to better predictive monitoring facilitated by optimal sensing.
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