In this study, we propose SQUID, a software-based solution to predict the off-time of batteryless devices that operate in environments with short-term energy-harvesting stability. The key insight of SQUID is to sample the power in the environment when the device is on and use these samples to extrapolate the power availability when the device is off and charging its capacitor. Therefore, SQUID can predict the charging time of the batteryless sensors by using the predicted power availability. Our initial experiments showed that SQUID has a promising estimation accuracy by consuming up to 10 times less energy than existing timekeeping solutions.
Persistent Timekeeping Using Harvested Power Measurements / Yildiz, Eren; Yildirim, Kasim Sinan. - (2021), pp. 572-574. (Intervento presentato al convegno 19th ACM Conference on Embedded Networked Sensor Systems, SenSys 2021 tenutosi a prt nel 2021) [10.1145/3485730.3493361].
Persistent Timekeeping Using Harvested Power Measurements
Yildirim, Kasim Sinan
2021-01-01
Abstract
In this study, we propose SQUID, a software-based solution to predict the off-time of batteryless devices that operate in environments with short-term energy-harvesting stability. The key insight of SQUID is to sample the power in the environment when the device is on and use these samples to extrapolate the power availability when the device is off and charging its capacitor. Therefore, SQUID can predict the charging time of the batteryless sensors by using the predicted power availability. Our initial experiments showed that SQUID has a promising estimation accuracy by consuming up to 10 times less energy than existing timekeeping solutions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
