Poster Session 3, 1:15 PM - 2:00 PM: Room 163 [C25]

Intermittent Machine Learning for Battery-Free Environmental Sensing Under Solar Power

Presenter: Linh Dang

Group Members: Duc Ngoc Nghiem

Faculty Sponsor: VP Nguyen

School: UMass Amherst

Research Area: Computer Science

ABSTRACT

Battery-free sensing platforms powered by ambient energy harvesting offer a promising approach for long-term environmental monitoring without battery replacement or maintenance. However, these systems operate under highly unstable energy conditions, as available power varies with environmental factors such as lighting. This behavior conflicts with traditional machine learning assumptions that computation can run continuously. In this project, we investigate how lightweight on-device inference behaves when executed under intermittent power.

We developed a solar-powered prototype that activates only when its storage voltage exceeds an operating threshold. The device periodically collects environmental data and attempts local inference whenever energy is available. Because harvested energy fluctuates, the system repeatedly shuts down and restarts, leading to partial execution and inconsistent prediction behavior. A mobile logging interface was used to monitor voltage levels, execution cycles, and inference outcomes across repeated charge–discharge periods.

Initial observations show that sensing and inference perform reliably during stable illumination but degrade under dynamic lighting conditions. Frequent cold starts and incomplete computations were observed when energy availability was marginal, and prediction consistency decreased during these unstable periods. These results suggest that directly applying always-on inference strategies to energy-harvesting devices can lead to unreliable behavior.

This work highlights the importance of designing energy-aware inference workflows for intermittently powered systems. Future work will focus on calibrating real hardware operating thresholds and improving robustness to repeated power interruptions.

RELATED ABSTRACTS