SmartON: Just-in-Time Active Event Detection on Energy Harvesting Systems

TL;DR

SmartON is a novel energy-harvesting system that proactively learns event patterns and energy availability to optimize wake-up timing, significantly improving event detection and energy efficiency in batteryless environments.

Contribution

It introduces a three-phase learning framework and a dedicated hardware platform for adaptive, energy-efficient event detection in energy harvesting systems.

Findings

Captures 1X--7X more events than baseline systems

Achieves 8X--17X greater energy efficiency

Adapts rapidly to environmental changes

Abstract

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.