DynaES: Dynamic Energy Scheduling for Energy Harvesting Environmental Sensors

Low-cost sensors and IoT technologies have facilitated the deployment of environmental sensors to collect and analyze various factors, such as soil properties. Due to the lack of electric power networks in many deployment locations, these sensors rely on energy harvesting (EH) systems that use natural energy sources such as solar power. Specifically, solar-powered EH systems benefit from timely obtaining weather information for efficient future energy scheduling. However, obtaining weather information for EH environmental sensors is always challenging, as they are commonly deployed in harsh environments without network access. To address this problem, we present DynaES, a novel energy scheduling method for EH sensors without relying on online weather forecasts. DynaES comprises two components: a DC power gain estimator that predicts future power gain by individually estimating changes in environmental parameters and ensembling them, and a dynamic energy scheduler that distributes energy to sensors based on priority and adjusts sensing intervals and frequency. We evaluate DynaES via simulation-based studies on real-world datasets and compare its performance against state-of-the-art baselines. Evaluation results show that DynaES accurately predicts future energy gain with low estimation errors and enables $1.8 \times$ – $4 \times$ more frequent sensing operations with shorter sensing intervals while achieving longer operation hours without complete battery drains.