A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

[EN] Continuous monitoring of diabetic patients improves their quality of life. The use of multiple technologies such as the Internet of Things (IoT), embedded systems, communication technologies, artificial intelligence, and smart devices can reduce the economic costs of the healthcare system. Different communication technologies have made it possible to provide personalized and remote health services. In order to respond to the needs of future intelligent e-health applications, we are called to develop intelligent healthcare systems and expand the number of applications connected to the network. Therefore, the 5G network should support intelligent healthcare applications, to meet some important requirements such as high bandwidth and high energy efficiency. This article presents an intelligent architecture for monitoring diabetic patients by using machine learning algorithms. The architecture elements included smart devices, sensors, and smartphones to collect measurements from the body. The intelligent system collected the data received from the patient, and performed data classification using machine learning in order to make a diagnosis. The proposed prediction system was evaluated by several machine learning algorithms, and the simulation results demonstrated that the sequential minimal optimization (SMO) algorithm gives superior classification accuracy, sensitivity, and precision compared to other algorithms.Rghioui, A.; Lloret, J.; Sendra, S.; Oumnad, A. (2020). A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms. Healthcare. 8(3):1-16. https://doi.org/10.3390/healthcare80303481168

An Enhanced Architecture for LARIISA: An Intelligent System for Decision Making and Service Provision for e-Health using the cloud

International audienceHealth care services can be scarce and expensive in some countries and especially in isolated regions. The lack of information can degrade health care services, for example, by ineffective resource allocation or failure in epidemiological prediction. This paper proposes an architecture for system of decision making and service provisioning in the health care context. It encompasses and integrates data produced by environmental sensors installed in the assisted homes, medical data sets, domain-specific and semantic enriched data sets, and all data generated and collected in applications installed on mobile phones, wearable devices, desktops, web servers, and smart television. LARIISA architecture is presented as a platform to manage, provide and launch services that monitor and analyze data to supply relevant information to decision makers and health care actors that participate in the health care supply chain

A Home E-Health System for Dependent People Based on OSGI

This chapter presents a e-health system for dependent people installed in a home environment. After reviewing the state of art in e-health applications and technologies several limitations have been detected because many solutions are proprietary and lack interoperability. The developed home e-health system provides an architecture capable to integrate different telecare services in a smart home gateway hardware independent from the application layer. We propose a rule system to define users’ behavior and monitor relevant events. Two example systems have been implemented to monitor patients. A data model for the e-health platform is described as well.Ministerio de Educación y Ciencia TSI2006-13390-C02-0

Cloud Computing Based Network Analysis in Smart Healthcare System with Neural Network Architecture

The recent progressions in Artificial Intelligence (AI), the Internet of Things (IoT), and cloud computing transformed the traditional healthcare system into a smart healthcare system. Medical services can be improved through the incorporation of key technologies namely AI and IoT. The convergence of AI and IoT renders several openings in the healthcare system. In machine learning, deep learning can be considered a renowned topic with a wide range of applications like biomedicine, computer vision, speech recognition, drug discovery, visual object detection, natural language processing, disease prediction, bioinformatics, etc. Among these applications, medical science-related and health care applications were raised dramatically. This study develops a Cloud computing-based network analysis in the smart healthcare systems with neural network (CCNA-SHSNN) architecture. The presented CCNA-SHSNN technique assists in the decision-making process of the healthcare system in a real time cloud environment. For data pre-processing, the CCNA-SHSNN technique uses a normalization approach. Secondly, the CCNA-SHSNN technique applies the autoencoder (AE) model for healthcare data classification in the CC platform. At last, the gravitational search algorithm (GSA) is used for hyperparameter optimization of the AE model. The experimental outcomes are determined on a benchmark dataset and the outcomes signify the outperforming efficiency of the CCNA-SHSNN technique compared to existing techniques

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

Logic-centred architecture for ubiquitous health monitoring

One of the key points to maintain and boost research and development in the area of smart wearable systems (SWS) is the development of integrated architectures for intelligent services, as well as wearable systems and devices for health and wellness management. This paper presents such a generic architecture for\ud multiparametric, intelligent and ubiquitous wireless sensing platforms. It is a transparent, smartphone-based sensing framework\ud with customizable wireless interfaces and plug‘n’play capability to easily interconnect third party sensor devices. It caters to wireless\ud body, personal, and near-me area networks. A pivotal part of the platform is the integrated inference engine/runtime environment\ud that allows the mobile device to serve as a user-adaptable personal health assistant. The novelty of this system lays in a rapid visual\ud development and remote deployment model. The complementary visual InferenceEngineEditor that comes with the package enables\ud artificial intelligence specialists, alongside with medical experts, to build data processing models by assembling different components\ud and instantly deploying them (remotely) on patient mobile devices. In this paper, the new logic-centered software architecture for ubiquitous health monitoring applications is described, followed by a\ud discussion as to how it helps to shift focus from software and hardware development, to medical and health process-centered design of new SWS applications

A Lightweight Blockchain and Fog-enabled Secure Remote Patient Monitoring System

IoT has enabled the rapid growth of smart remote healthcare applications. These IoT-based remote healthcare applications deliver fast and preventive medical services to patients at risk or with chronic diseases. However, ensuring data security and patient privacy while exchanging sensitive medical data among medical IoT devices is still a significant concern in remote healthcare applications. Altered or corrupted medical data may cause wrong treatment and create grave health issues for patients. Moreover, current remote medical applications' efficiency and response time need to be addressed and improved. Considering the need for secure and efficient patient care, this paper proposes a lightweight Blockchain-based and Fog-enabled remote patient monitoring system that provides a high level of security and efficient response time. Simulation results and security analysis show that the proposed lightweight blockchain architecture fits the resource-constrained IoT devices well and is secure against attacks. Moreover, the augmentation of Fog computing improved the responsiveness of the remote patient monitoring system by 40%.Comment: 32 pages, 13 figures, 5 tables, accepted by Elsevier "Internet of Things; Engineering Cyber Physical Human Systems" journal on January 9, 202

A patient agent controlled customized blockchain based framework for internet of things

Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017