Sensor Selection

Sensor selection focuses on optimizing the use of sensor networks by strategically choosing which sensors to activate or prioritize, aiming to maximize performance while minimizing resource consumption (energy, bandwidth, computation). Current research emphasizes efficient algorithms, including generative models like GFlowNets and reinforcement learning, to navigate the combinatorial complexity of sensor subset selection and dynamically adapt sensor usage based on data characteristics and task requirements. This research area is crucial for advancing resource-constrained applications like IoT devices, robotics, and autonomous systems, enabling more efficient and effective data acquisition and processing.