An Energy-Efficient Computational Model for Uncertainty Management in Dynamically Changing Networked Wearables

Abstract

The utility of wearables is currently limited to lab experiments and controlled environments mainly because computational algorithms embedded in wearables fail to produce accurate measurements in uncontrolled, dynamically changing, and potentially harsh environments. With the exponentially growing adoption of these systems in human-centered Internet-of-Things (IoT) applications, development of resource-efficient solutions to enhance the accuracy of this systems remains a considerable research challenge. In this paper, we introduce an energy-efficient framework for uncertainty management of networked wearables. The core components of our framework are anomaly screening units for detecting anomalies that require handling, thus resulting in one order of magnitude less energy consumption compared to the conventional frameworks. Furthermore, our screening approach achieves 98.3% accuracy in detecting anomalies based on real data collected with wearable motion sensors.