1Hardware Variability-Aware Duty Cycling for Embedded Sensors

Abstract—Instance and temperature-dependent power variation has a direct impact on quality of sensing for battery powered, long running sensing applications. We measure and characterize active and leakage power for an ARM Cortex M3 processor, and show that across a temperature range of 20–60◦C there is 10 % variation in active power, and 14x variation in leakage power. We introduce variability aware duty cycling methods and a duty cycle abstraction for TinyOS that allows ap-plications to explicitly specify lifetime and minimum duty cycle requirements for individual tasks, and dynamically adjusts duty cycle rates so that overall quality of service is maximized in the presence of power variability. We show that variability-aware duty cycling yields a 3–22x improve-ment in total active time over schedules based on worst-case estimations of power, with an average improvement of 6.4x across a wide variety of deployment scenarios based on collected temperature traces. Conversely, datasheet power specifications fail to meet required lifetimes by 7– 15%, with an average 37 days short of a required lifetime of one year. Finally, we show that a target localization application using variability-aware duty cycle yields a 50% improvement in quality of results over one based on worst-case estimations of power consumption. I