The rapid and widespread deployment of Internet of Things (IoT) sensors is limited by issues related to maintenance costs and safe battery disposal. While battery-less systems supported by harvesting ambient energy offer a potential solution, the weakness and instability of ambient energy result in intermittent execution. To provide data consistency, I/O operations are usually performed in the atomic sections, resulting in low-energy efficiency and low-system progress in intermittent systems. To improve application responsiveness and prevent low-energy efficiency due to atomic I/O operations, we propose a framework named FASE, which provides energy isolation assistance to support multiple asynchronous atomic I/O operations in intermittent systems. Our proposal was evaluated on a real platform, and the results show that, compared to state-of-the-art works, our proposed algorithm can increase the rate of completion for latency-sensitive applications by more than 50%.

Battery-less devices offer potential solutions for maintaining sustainable Internet of Things (IoT) networks. However, limited energy harvesting capacity can lead to power failures, limiting the system’s quality of service (QoS) . To improve timely task progress, we present ETIME, a scheduling framework that enables energy-efficient communication for intermittent-powered IoT devices. To maximize energy efficiency while meeting the timely requirements of intermittent systems, we first model the relationship between insufficient harvesting energy and task behavior time. We then propose a method for predicting response times for battery-less devices. Considering both delays from multiple task interference and insufficient system energy, we introduce a dynamic wake-up strategy to improve timely task progress. Additionally, to minimize power consumption from connection components, we propose a dynamic connection interval adjustment to provide energy-efficient communication. The proposed algorithms are implemented in a lightweight operating system on real devices. Experimental results show that our approach can significantly improve progress for timely applications while maintaining task progress.