Energy Efficiency of Fog Computing Health Monitoring Applications

TL;DR

This paper evaluates the energy efficiency of fog computing for real-time health monitoring, demonstrating significant energy savings in ECG processing compared to cloud-based solutions.

Contribution

It introduces an optimized fog computing framework for ECG analysis that minimizes energy consumption while meeting real-time constraints.

Findings

Up to 68% energy savings with fog processing

Optimized server placement reduces energy use

Effective real-time ECG analysis at fog nodes

Abstract

Fog computing offers a scalable and effective solution to overcome the increasing processing and networking demands of Internet of Thing (IoT) devices. In this paper, we investigate the use of fog computing for health monitoring applications. We consider a heart monitoring application where patients send their 30 minute recording of Electrocardiogram (ECG) signal for processing, analysis, and decision making at fog processing units within the time constraint recommended by the American Heart Association (AHA) to save heart patients when an abnormality in the ECG signal is detected. The locations of the processing servers are optimized so that the energy consumption of both the processing and networking equipment are minimised. The results show that processing the ECG signal at fog processing units yields a total energy consumption saving of up to 68% compared to processing the at the central cloud.