GRES: Guaranteed Remaining Energy Scheduling of Energy-harvesting Sensors by Quality Adaptation

Many battery-powered IoT sensor nodes rely on harvesting energy which must be assumed an unreliable source. Previous works have shown that a sensor node can adapt its power consumption to keep its battery's state of charge at a sufficient level to achieve perpetual operation by, e.g., dynamically adapting its duty cycle. In this paper, we show that it is also possible to reduce the energy consumed by the operation of a sensor node by controlling the quality of the processed and transmitted data. Moreover, whereas most state-of-the-art methods rely on forecasting energy harvesting, risking loss of service in case of wrong predictions, this paper presents an algorithm called Guaranteed Remaining Energy Scheduling (GRES) which dynamically controls the quality of processed and transmitted data of a sensor node at runtime based on the state of charge of the battery, and providing a guarantee of safe continuous operation at the expense of data quality despite fluctuations in expected harvested energy. In experiments, GRES is evaluated and compared to an approach computing ILP-generated quality schedules for one full day ahead based on the assumption of a perfect harvested energy prediction. It is shown that the latter approach is not only computationally and energy-wise expensive, but also can lead to power shutdowns in case of wrongly predicted harvesting profiles.