Sensor Placement

Sensor placement optimization aims to strategically locate sensors within a system or environment to maximize data quality and minimize resource usage. Current research focuses on developing efficient algorithms, including greedy heuristics, Bayesian optimization, and deep learning models (e.g., transformers, convolutional neural processes), to determine optimal sensor locations under various constraints (e.g., budget, physical limitations, robustness to failures). These advancements are crucial for improving the accuracy and efficiency of various applications, such as environmental monitoring, structural health monitoring, and human activity recognition, by enabling more effective data collection and analysis with fewer sensors.