Freshness-aware Data Backup for Batteryless Sensing Systems

Batteryless sensing systems rely on energy harvested from the environment to execute. However, as the harvested energy is generally weak and unstable, the system may experience frequent power failures during processing and sensing. To make forward progress across power outages, the system backs up the system state from static random access memory (SRAM) to non-volatile memory (NVM) before power failures and then restores it upon reboot. Moreover, to avoid losing the collected data, existing approaches save all the collected data from SRAM to NVM before system-off. The data saving and the frequent system reboots consume a lot of energy and time and thus cause a long blocking time. However, the data stored in SRAM can be retained for a short period even after the system is turned off, as the data retention voltage of SRAM is lower than the minimum operating voltage of the microcontroller unit (MCU). In this paper, we leverage the SRAM data retention capability to retain data with a short lifetime on SRAM, while only save data with a long lifetime to NVM. Consequently, the backup overhead is significantly reduced. However, a design challenge is to decide the turn-off voltage to minimize the blocking time. Specifically, turning off at a higher voltage leaves more energy for a longer retention time and results in lower data saving overhead. But this may also cause more on-offs, leading to more system states saving and system states restoring overhead. To address this challenge, the paper proposes a method to adaptively compute the optimal turn-off voltage. Experimental results show that the proposed method can significantly reduce the blocking time caused by data saving and system reboots. The system can collect more data and exhibits improved responsiveness in sensing the environment.