Tag
This paper proposes two hardware-agnostic dynamic scheduling strategies (a model-free reinforcement learning agent and an on-the-fly approximated prediction method) for managing task execution in batteryless IoT devices with unknown workloads, and evaluates them against existing approaches using a simulation framework with real-world solar data.