Tumbler: Energy Efficient Task Scheduling for Dual-Channel Solar-Powered Sensor Nodes

Energy harvesting technology has been popularly adopted in embedded systems. However, unstable energy source results in unsteady operation. In this paper, we devise a long-term energy efficient task scheduling targeting for solar-powered sensor nodes. The proposed method exploits a reinforcement learning with a solar energy prediction method to maximize the energy efficiency, which finally enhances the long-term quality of services (QoS) of the sensor nodes. Experimental results show that the proposed scheduling improves the energy efficiency by 6.0%, on average and achieves the better QoS level by 54.0%, compared with a state-of the-art task scheduling algorithm.