Fog Computing in Healthcare Internet of Things: A Case Study on ECG Feature Extraction

TLDR

Internet of Things enables real‑time health monitoring by acquiring biosignals from sensor nodes and transmitting them to cloud servers for processing and diagnosis. This study extends IoT‑based health monitoring by applying fog computing at smart gateways to perform ECG feature extraction for cardiac disease diagnosis. ECG signals are processed locally at gateways, extracting heart rate, P‑wave, and T‑wave features using a lightweight wavelet‑transform template. Experiments show that fog computing yields over 90 % bandwidth savings and delivers low‑latency real‑time responses at the network edge.

Abstract

Internet of Things technology provides a competent and structured approach to improve health and wellbeing of mankind. One of the feasible ways to offer healthcare services based on IoT is to monitor human's health in real-time using ubiquitous health monitoring systems which have the ability to acquire bio-signals from sensor nodes and send the data to the gateway via a particular wireless communication protocol. The real-time data is then transmitted to a remote cloud server for real-time processing, visualization, and diagnosis. In this paper, we enhance such a health monitoring system by exploiting the concept of fog computing at smart gateways providing advanced techniques and services such as embedded data mining, distributed storage, and notification service at the edge of network. Particularly, we choose Electrocardiogram (ECG) feature extraction as the case study as it plays an important role in diagnosis of many cardiac diseases. ECG signals are analyzed in smart gateways with features extracted including heart rate, P wave and T wave via a flexible template based on a lightweight wavelet transform mechanism. Our experimental results reveal that fog computing helps achieving more than 90% bandwidth efficiency and offering low-latency real time response at the edge of the network.