An automated framework for power-efficient detection in embedded sensor systems

Abstract

The availability of miniature low-cost sensors has allowed for the capture of rich, multimodal data streams in compact embedded sensor nodes. These devices have the capacity to radically improve the quality and amount of data available in such diverse applications as detecting degenerative diseases, monitoring remote regions, and tracking the state of smart assets as they traverse the supply chain. However, current implementations of these applications suffer from short lifespans due to high sensor energy use and limited battery size. By concentrating our design efforts on the sensors themselves, it is possible to construct embedded systems that achieve their goal(s) while drawing significantly less power. This will increase their lifespan, allowing many more applications to make the transition from laboratory to marketplace and thereby benefit a much wider population. This dissertation presents an automated framework for power-efficient detection in embedded sensor systems. The core of this framework is a decision tree classifier that dynamically orders the activation and adjusts the sampling rate of the sensors, such that only the data necessary to determine the system state is collected at any given time.

(cont.) This classifier can be tuned to trade-off accuracy and power in a structured fashion. Use of a sensor set which measures the phenomena of interest in multiple modalities and at various rates further improves the power savings by increasing the information available to the classification process. An application based on a wearable gait monitor provides quantitative support for this framework. It is shown that the decision tree classifiers designed achieve roughly identical detection accuracies to those obtained using support vector machines while drawing three to nine times less power. A simulation of the real-time operation of the classifiers demonstrates that our multi-tiered classifier determines states as accurately as a single-trigger (binary) wakeup system while drawing half as much power, with only a negligible increase in latency.