Hardware-agnostic RL and approximated prediction schedulers achieve strong task throughput and tunable survival balancing in batteryless IoT with unknown workloads, while static thresholds suffice for devices with larger energy buffers.
Pro-energy: A novel energy prediction model for solar and wind energy-harvesting wireless sensor networks,
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Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation
Hardware-agnostic RL and approximated prediction schedulers achieve strong task throughput and tunable survival balancing in batteryless IoT with unknown workloads, while static thresholds suffice for devices with larger energy buffers.