SoTimer: A Software-based Timekeeper for Energy Harvesting Systems

The proliferation of IoT devices has made energy harvesting systems an appealing solution for power numerous IoT devices without the limitations imposed by battery life constraints. Despite its potential, energy harvesting systems often encounter frequent power failures leading to interruptions of tasks due to the generally weak energy output they rely on. In modern computer systems, maintaining a consistent power flow is crucial for tasks such as synchronization and system stability. However, in energy harvesting systems, maintaining this consistency is challenging as the system is hard to track time during power failures. Current approaches rely on additional hardware and exploit physical phenomena to estimate power-off duration, but these methods are susceptible to environmental variations like temperature, humidity, and radiation, and are not easily applicable to commercial-off-the-shelf (COTS) micro-controllers. In this study, we introduce SoTimer, a software-based timekeeping solution designed to mitigate the impact of environmental changes without the need for extra hardware. The core concept of SoTimer lies in the stability of charging power between adjacent power-on and power-off periods, as evidenced by extensive measurements under different energy resources. Building on this insight, we propose a machine learning algorithm to predict power-off times based on power conditions observed during power-on phases. Furthermore, we introduce optimization techniques tailored for MCUs with limited computational capabilities to ensure efficient inference. Experimental results demonstrate that SoTimer achieves a high inference accuracy and a negligible runtime overhead.