Optimal Sensor Placement

Optimal sensor placement aims to strategically position sensors to maximize data quality and minimize costs across diverse applications. Current research focuses on developing efficient algorithms, including evolutionary greedy algorithms, gradient-based methods leveraging Gaussian processes, and machine learning approaches like autoencoders, to solve this computationally challenging problem, often incorporating constraints like limited sensor availability or restricted placement zones. These advancements are improving data acquisition in various fields, from environmental monitoring (e.g., methane leak detection, wind pressure field reconstruction) to human activity recognition and autonomous systems, leading to more effective and cost-efficient data collection and analysis.