Batteryless IoT Nodes for Autonomous Environment-aware Sensing

The rapid growth of the Internet of Things (IoT) has led to a growing demand for long-lasting, maintenance-free, and sustainable IoT nodes, particularly in monitoring and sensing applications where dense deployments are required to improve coverage and accuracy. Batteryless IoT nodes powered by ambient energy harvesting offer a promising alternative to conventional battery-based solutions. However, designing a reliable, sustainable, and energy-efficient batteryless IoT sensor node requires navigating multiple challenges, including energy-sensitive hardware and software design, analytical energy modeling, and adaptive duty cycling strategies. This paper introduces a novel algorithm to enable autonomous predictive sleep cycle adaptation to adjust the measurement frequency and extend the lifespan of batteryless sensor nodes based on the analysis of energy availability in the environment. The proposed environment-aware algorithm is built over a comprehensive framework that includes energy-efficient integration of hardware and software and a detailed mathematical model of the node's energy dynamics, tailored specifically for environmental sensing applications. The proposed solution can be generalized to a wide range of IoT devices and is validated both in simulation and real-world with our batteryless IoT node prototype, ensuring consistency between digital and real-world performance. Experimental results show that our algorithm can successfully adapt its measurement frequency in real-time to diverse energy harvesting scenarios, extending the lifespan of the batteryless IoT device by up to 80\%, equivalent to more than one hour, compared to the current state-of-the-art solutions.