Active Sensing

Active sensing optimizes data acquisition by strategically controlling sensors to maximize information gain and minimize resource consumption. Current research emphasizes developing efficient algorithms, such as reinforcement learning and deep neural networks (including LSTMs), to guide sensor placement and data collection in diverse applications, from robotics and medical diagnosis to environmental monitoring and wireless communications. This approach promises significant improvements in efficiency and accuracy across various fields by intelligently focusing sensing efforts on the most informative data, leading to better decision-making and reduced costs.