Power management in energy harvesting embedded systems

Energy harvesting (also known as energy scavenging) is the process of generating electrical energy from environmental energy sources. There exists a variety of different energy sources such as solar energy, kinetic energy, or thermal energy. The term has been frequently applied in the context of small autonomous devices such as wireless sensor nodes. This thesis addresses power management in energy harvesting embedded systems. As an example scenario, we focus on wireless sensor nodes which are powered by solar cells. We demonstrate that classical power management solutions have to be reconceived and/or new problems arise if perpetual operation of the system is required. In particular, we provide a set of algorithms and methods for different application scenarios, including real-time scheduling, application rate control as well as reward maximization. Goal is to optimize the performance of the application subject to given energy constraints. Compared to state-of-the-art approaches, our methods optimize the system performance or achieve the same performance as state-of-the-art approaches requiring, e.g., smaller solar cells and smaller batteries. Furthermore, we show how to dimension important system parameters like the minimum battery capacity or a sufficient prediction horizon. Our theoretical results are supported by simulations using long-term measurements of solar energy in an outdoor environment. Furthermore, to demonstrate the practical relevance of our approaches, we measured the implementation overhead of our algorithms on real sensor nodes.