Getting More Out of Energy-harvesting Systems: Energy Management under Time-varying Utility with PREAcT

Careful energy management is a prerequisite for long-term, unattended operation of solar-harvesting sensing systems. We observe that in many applications the utility of sensed data varies over time, but current energy-management algorithms do not exploit prior knowledge of these variations for making better decisions. This paper presents PREAcT, the first energy-management algorithm that exploits time-varying utility to optimize application performance. PREAcT'S design combines strategic long-term planning of future energy utilization with feedback control to compensate for deviations from the expected conditions. We implement Pre-act on a low-power microcontroller and compare it against the state of the art on multiple years of real-world data. Our results demonstrate that PREAcT is up to 53 % more effective in utilizing harvested solar energy and significantly more robust to uncertainties and inefficiencies of practical systems. These gains translate into an improvement of 28 % in the end-to-end performance of a real-world application we investigate when using PREAcT.