Energy Efficiency of Fog Computing Health Monitoring Applications

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

A Conceptual Framework for Data Governance in IoT-enabled Digital IS Ecosystems

Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved There is a growing interest in the use of Internet of Things (IoT) in information systems (IS). Data or information governance is a critical component of IoT enabled digital IS ecosystem. There is insufficient guidance available on how to effectively establish data governance for IoT enabled digital IS ecosystem. The introduction of new regulations related to privacy such as General Data Protection Regulation (GDPR) as well as existing regulations such as Health Insurance Portability and Accountability Act (HIPPA) has added complexity to this issue of data governance. This could possibly hinder the effective IoT adoption in healthcare digital IS ecosystem. This paper enhances the 4I framework, which is iteratively developed and updated using the design science research (DSR) method to address this pressing need for organizations to have a robust governance model to provide the coverage across the entire data lifecycle in IoT-enabled digital IS ecosystem. The 4I framework has four major phases: Identify, Insulate, Inspect and Improve. The application of this framework is demonstrated with the help of a Healthcare case study. It is anticipated that the proposed framework can help the practitioners to identify, insulate, inspect and improve governance of data in IoT enabled digital IS ecosystem

IoT-based Architectures for Sensing and Local Data Processing in Ambient Intelligence: Research and Industrial Trends

This paper presents an overview of new-generation technologies based on Internet of Things (IoT) and Ambient Intelligence (AmI), which create smart environments that respond intelligently to the presence of people, by collecting data from sensors, aggregating measurements, and extracting knowledge to support daily activities, perform proactive actions, and improve the quality of life. Recent advances in miniaturized instrumentation, general-purpose computing architectures, advanced communication networks, and non-intrusive measurement procedures are enabling the introduction of IoT and AmI technologies in a wider range of applications. To efficiently process the large quantities of data collected in recent AmI applications, many architectures use remote cloud computing, either for data storage or for faster computation. However, local data processing architectures are often preferred over cloud computing in the cases of privacy-compliant or time-critical applications. To highlight recent advances of AmI environments for these applications, in this paper we focus on the technologies, challenges, and research trends in new-generation IoT-based architectures requiring local data processing techniques, with specific attention to smart homes, intelligent vehicles, and healthcare

Enabling the Internet of Mobile Crowdsourcing Health Things: A Mobile Fog Computing, Blockchain and IoT Based Continuous Glucose Monitoring System for Diabetes Mellitus Research and Care

[Abstract] Diabetes patients suffer from abnormal blood glucose levels, which can cause diverse health disorders that affect their kidneys, heart and vision. Due to these conditions, diabetes patients have traditionally checked blood glucose levels through Self-Monitoring of Blood Glucose (SMBG) techniques, like pricking their fingers multiple times per day. Such techniques involve a number of drawbacks that can be solved by using a device called Continuous Glucose Monitor (CGM), which can measure blood glucose levels continuously throughout the day without having to prick the patient when carrying out every measurement. This article details the design and implementation of a system that enhances commercial CGMs by adding Internet of Things (IoT) capabilities to them that allow for monitoring patients remotely and, thus, warning them about potentially dangerous situations. The proposed system makes use of smartphones to collect blood glucose values from CGMs and then sends them either to a remote cloud or to distributed fog computing nodes. Moreover, in order to exchange reliable, trustworthy and cybersecure data with medical scientists, doctors and caretakers, the system includes the deployment of a decentralized storage system that receives, processes and stores the collected data. Furthermore, in order to motivate users to add new data to the system, an incentive system based on a digital cryptocurrency named GlucoCoin was devised. Such a system makes use of a blockchain that is able to execute smart contracts in order to automate CGM sensor purchases or to reward the users that contribute to the system by providing their own data. Thanks to all the previously mentioned technologies, the proposed system enables patient data crowdsourcing and the development of novel mobile health (mHealth) applications for diagnosing, monitoring, studying and taking public health actions that can help to advance in the control of the disease and raise global awareness on the increasing prevalence of diabetes.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2016-045Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

How much should you worry about contaminant neutrons in spatially fractionated grid radiation therapy?

Objectives: Telehealth monitoring applications are latency-sensitive. The current fog-based telehealth monitoring models are mainly focused on the role of the fog computing in improving response time and latency. In this paper, we have introduced a new service called “priority queue” in fog layer, which is programmed to prioritize the events sent by different sources in different environments to assist the cloud layer with reducing response time and latency. Material and Methods: We analyzed the performance of the proposed model in a fog-enabled cloud environment with the IFogSim toolkit. To provide a comparison of cloud and fog computing environments, three parameters namely response time, latency, and network usage were used. We used the Pima Indian diabetes dataset to evaluate the model. Result: The fog layer proved to be very effective in improving the response time while handling emergencies using priority queues. The proposed model reduces response time by 25.8%, latency by 36.18%, bandwidth by 28.17%, and network usage time by 41.4% as compared to the cloud. Conclusion: By combining priority queues, and fog computing in this study, the network usage, latency time, bandwidth, and response time were significantly reduced as compared to cloud computing

A fog-assisted information model based on priority queue and clinical decision support systems

ObjectivesTelehealth monitoring applications are latency-sensitive. The current fog-based telehealth monitoring models are mainly focused on the role of the fog computing in improving response time and latency. In this paper, we have introduced a new service called “priority queue” in fog layer, which is programmed to prioritize the events sent by different sources in different environments to assist the cloud layer with reducing response time and latency. Material and MethodsWe analyzed the performance of the proposed model in a fog-enabled cloud environment with the IFogSim toolkit. To provide a comparison of cloud and fog computing environments, three parameters namely response time, latency, and network usage were used. We used the Pima Indian diabetes dataset to evaluate the model. ResultThe fog layer proved to be very effective in improving the response time while handling emergencies using priority queues. The proposed model reduces response time by 25.8%, latency by 36.18%, bandwidth by 28.17%, and network usage time by 41.4% as compared to the cloud. ConclusionBy combining priority queues, and fog computing in this study, the network usage, latency time, bandwidth, and response time were significantly reduced as compared to cloud computing. Keyword

Energy Efficient Machine Learning-Based Classification of ECG Heartbeat Types

To meet the accuracy, latency and energy efficiency requirements during real-time collection and analysis of health data, a distributed edge computing environment is the answer, combined with 5G speeds and modern computing techniques. Using the state-of-the-art machine learning based classification techniques plays a crucial role in creating the optimal healthcare system on the edge. This thesis first provides a background on the current and emerging edge computing classification techniques for healthcare applications, specifically for electrocardiogram (ECG) beat classification. We then present key findings from an extensive survey of over hundred studies on the topic while taxonomizing the literature with respect to key architectural differences, application areas and requirements. Leveraging the insights drawn from the extensive analysis of the pertinent literature we select a set of most promising machine learning based classification techniques for ECG beats, based on their suitability for implementation on a small edge device called a Raspberry Pi. After implementing these classification techniques on a Raspberry Pi based platform we perform a comparison of the performance of these classification techniques with respect to three key performance indicators (KPI) of interest for health care applications namely accuracy, energy efficiency, and latency. ECG measures the electrical activity of the heart and help healthcare professionals to evaluate heart conditions of a patient, sometimes diagnosing life-threatening conditions. The features of ECG signals are pre-processed and fed into the classification algorithms to detect and classify abnormal beat types. ECG classification requires low complexity but still high level of performance in terms of aforementioned three KPIs. The classification algorithms chosen, namely Naïve Bayes, Multilayer Perceptron (MLP), and distilled deep neural network (DNN) are all energy efficient methods hence suitable for implementation for small edge devices. The comparative multi-faceted evaluation presented in this thesis is a new contribution to research that exists on edge based classification as it offers comparison of selected classification algorithms in terms three KPIs instead of one while using real edge device based implementation. The performance of analyzed machine learning classification techniques is ranked according to each KPI. Benefiting from the results of the comparative analysis presented in this thesis a particular classification algorithm can be selected for optimal deployment in given scenario in healthcare system depending on the specific requirements of the given scenario. Edge computing paves the way for a new generation of health devices that can offer a higher quality of life for users if low-latency, low-energy, and high- performance requirements are addressed