Deep Reinforcement Learning for Sensor Scheduling

Wake-up radio lets IoT nodes respond selectively to ID- or content-based requests, creating scheduling opportunities when sensor readings are correlated: careful request design can save energy and improve a gateway's estimate of system state.

This project designs a deep reinforcement learning algorithm that schedules sensors using knowledge of system state and statistics alongside battery levels and capabilities, potentially extending to different objective functions over the system state.