  We propose SmartON, a batteryless system that learns to wake up proactively
at the right moment in order to detect events of interest. It does so by
adapting the duty cycle to match the distribution of event arrival times under
the constraints of harvested energy. While existing energy harvesting systems
either wake up periodically at a fixed rate to sense and process the data, or
wake up only in accordance with the availability of the energy source, SmartON
employs a three-phase learning framework to learn the energy harvesting pattern
as well as the pattern of events at run-time, and uses that knowledge to wake
itself up when events are most likely to occur. The three-phase learning
framework enables rapid adaptation to environmental changes in both short and
long terms. Being able to remain asleep more often than a CTID
(charging-then-immediate-discharging) wake-up system and adapt to the event
pattern, SmartON is able to reduce energy waste, increase energy efficiency,
and capture more events. To realize SmartON we have developed a dedicated
hardware platform whose power management module activates capacitors on-the-fly
to dynamically increase its storage capacitance. We conduct both
simulation-driven and real-system experiments to demonstrate that SmartON
captures 1X--7X more events and is 8X--17X more energy-efficient than a CTID
system.
