A unified stochastic model for energy management in solar-powered embedded systems

Energy harvesting from environments such as solar energy are promising solutions to tackle energy sustainability in embedded systems. However, uncertainties in energy availability, non-ideal characteristics of harvesting circuits, energy storage (battery or supercapacitor), and application demand dynamics add more complexity in the system. We present a unified model based on discrete-time Finite State Markov Chain to capture the dynamicity and variations in both the energy supply from solar irradiance and the energy demand from the application. In this paper, we exploit the temporal and spatial characteristics of solar energy and propose a deterministic profile with stochastic process to reflect the fluctuation due to unexpected weather condition. Optimal policy to maximize expected total QoS is derived from the presented model using a probabilistic dynamic programming approach. Compared to a state-of-the-art deterministic energy management framework, our proposed approach outperforms in term of QoS and energy sustainability (with less shutdown time) of the system.