A1 Journal article – refereed
Energy efficient fog-assisted IoT system for monitoring diabetic patients with cardiovascular disease




List of Authors: Gia TN, Ben Dhaou I, Ali M, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H
Publisher: ELSEVIER SCIENCE BV
Publication year: 2019
Journal: Future Generation Computer Systems
Journal name in source: FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Journal acronym: FUTURE GENER COMP SY
Volume number: 93
Number of pages: 14
ISSN: 0167-739X
eISSN: 1872-7115

Abstract
Blood glucose plays an important role in maintaining body's activities. For example, brain only uses glucose as its energy source. However, when blood glucose level is abnormal, it causes some serious consequences. For instance, low-blood glucose phenomenon referred to as hypoglycemia can cause heart repolarization and induce cardiac arrhythmia causing sudden cardiac deaths. Diabetes, which can be viewed as a high-blood glucose level for a long period of time, is a dangerous disease as it can directly or indirectly cause heart attack, stroke, heart failure, and other vicious diseases. A solution for reducing the serious consequences caused by diabetes and hypoglycemia is to continuously monitor blood glucose level for real-time responses such as adjusting insulin levels from the insulin pump. Nonetheless, it is a misstep when merely monitoring blood glucose without considering other signals or data such as Electrocardiography (ECG) and activity status since they have close relationships. When hypoglycemia occurs, a fall can easily occur especially in case of people over 65 years old. Fall's consequences are more hazardous when a fall is not detected. Therefore, we present a Fog-based system for remote health monitoring and fall detection. Through the system, both e-health signals such as glucose, ECG, body temperature and contextual data such as room temperature, humidity, and air quality can be monitored remotely in real-time. By leveraging Fog computing at the edge of the network, the system offers many advanced services such as ECG feature extraction, security, and local distributed storage. Results show that the system works accurately and the wearable sensor node is energy efficient. Even though the node is equipped with many types of sensors, it can operate in a secure way for up to 157 h per a single charge when applying a 1000 mAh Lithium battery. (C) 2018 Elsevier B.V. All rights reserved.

Last updated on 2019-17-04 at 08:27