This article describes the development of an energy-adaptive, real-time sensing framework for battery-less devices which includes a lightweight machine learning-based energy predictor that can run on microcontroller devices.
The use of battery-less energy harvesting devices has been recognized as a promising solution for their low maintenance requirements and ability to work in harsh environments. However, these devices have to harvest energy from ambient energy sources and execute real-time sensing tasks periodically while satisfying data freshness constraints, which is especially challenging as the energy sources are often unreliable and intermittent. In this paper, the authors develop an energy-adaptive real-time sensing framework for battery-less devices. This framework includes a lightweight machine learning-based energy predictor that is capable of running on microcontroller devices and predicting the energy availability and intensity based on energy traces. Using this, the framework adapts the schedule of real-time tasks by effectively taking into account the predicted energy supply and the resulting age of information of each task, in order to achieve continuous sensing operations and satisfy given data freshness requirements. We discuss various design choices for adaptive scheduling and evaluate their performance in the context of battery-less devices. Experimental results show that the proposed adaptive real-time approach outperforms the recent methods based on static and reactive approaches, in both energy utilization and data freshness. (Published Abstract Provided)