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In this paper, a reinforcement learning-based throughput on demand (ToD) provisioning dynamic power management method (RLTDPM) is proposed for sustaining perpetual operation and satisfying the ToD requirements for today's energy harvesting wireless sensor node (EHWSN). The RLTDPM monitors the environmental state of the EHWS and adjusts their operational duty cycle under criteria of energy neutrality to meet the demanded throughput. Outcomes of these observation-adjustment interactions are then evaluated by feedback/reward that represents how well the ToD requests are met; subsequently, the observation-adjustment-evaluation process, so-called reinforcement learning, continues. After the learning process, the RLTDPM is able to autonomously adjust the duty cycle for satisfying the ToD requirement, and in doing so, sustain the perpetual operation of the EHWSN. Simulations of the proposed RLTDPM on a wireless sensor node powered by a battery and solar cell for image sensing tasks were performed. Experimental results demonstrate that the achieved demanded throughput is improved 10.7% for the most stringent ToD requirement, while the residual battery energy of the RLTDPM is improved 7.4% compared with an existing DPM algorithm for EHWSN with image sensing purpose. INDEX TERMSReinforcement learning, wireless sensor node, energy harvesting, energy neutrality, dynamic power management, throughput on demand.

A reinforcement learning-based maximum power point tracking (RLMPPT) method is proposed for photovoltaic (PV) array. By utilizing the developed system model of PV array and configuring the environment for the reinforcement learning, the proposed RLMPPT method is able to observe the environment state of the PV array in the learning process and to autonomously adjust the perturbation to the operating voltage of the PV array in obtaining the best MPP. Simulations of the proposed RLMPPT for a PV array are conducted. Experimental results demonstrate that, in comparison to an existing MPPT method, the RLMPPT not only achieves better efficiency factor for both simulated weather data and real weather data but also adapts to the environment much fast with very short learning time.

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