Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control

TL;DR

This paper proposes a fog-assisted control framework to optimize energy efficiency in wearable sensors for healthcare, enabling prolonged monitoring by offloading computation and applying local energy management algorithms.

Contribution

It introduces a novel fog computing-based approach that enhances wearable sensor energy efficiency through offloading and local optimization algorithms.

Findings

Significant extension of wearable sensor battery life.

Effective offloading reduces computational load on sensors.

Improved continuous monitoring capability.

Abstract

Recent advances in the Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient's vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors' limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life.