An architecture and protocol for smart continuous eHealth monitoring using 5G

[EN] Continuous monitoring of chronic patients improves their quality of life and reduces the economic costs of the sanitary system. However, in order to ensure a good monitoring, high bandwidth and low delay are needed. The 5G technology offers higher bandwidth, lower delays and packets loss than previous technologies. This paper presents an architecture for smart eHealth monitoring of chronic patients. The architecture elements include wearable devices, to collect measures from the body, and a smartphone at the patient side in order to process the data received from the wearable devices. We also need a DataBase with an intelligent system able to send an alarm when it detects that it is happening something anomalous. The intelligent system uses machine learning in BigData taken from different hospitals and the data received from the patient to diagnose and generate alarms. Experiment tests have been done to simulate the traffic from many users to the DataBase in order to evaluate the suitability of 5G in our architecture. When there are few users (less than 200 users), we do not find big differences of round trip time between 4G and 5G, but when there are more users, like 1000 users, it increases considerably reaching 4 times more in 4G The Packet Loss is almost null in 4G until 300 users, while in 5G it is possible to keep it null until 700 users. Our results point out that in order to have high number of patients continuously monitored, it is necessary to use the 5G network because it offers low delays and guarantees the availability of bandwidth for all users.This work has been partially supported by the "Ministerio de Educacion, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2014)". Grant number FPU14/02953.Lloret, J.; Parra-Boronat, L.; Abdullah, MTA.; Tomás Gironés, J. (2017). An architecture and protocol for smart continuous eHealth monitoring using 5G. Computer Networks. 129(2):340-351. https://doi.org/10.1016/j.comnet.2017.05.018S340351129

Blockchain leveraged decentralized IoT eHealth framework

Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data

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

Towards fostering the role of 5G networks in the field of digital health

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future

A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards

[EN] The Internet of Things paradigm in healthcare has boosted the design of new solutions for the promotion of healthy lifestyles and the remote care. Thanks to the effort of academia and industry, there is a wide variety of platforms, systems and commercial products enabling the real-time information exchange of environmental data and people's health status. However, one of the problems of these type of prototypes and solutions is the lack of interoperability and the compromised scalability in large scenarios, which limits its potential to be deployed in real cases of application. In this paper, we propose a health monitoring system based on the integration of rapid prototyping hardware and interoperable software to build system capable of transmitting biomedical data to healthcare professionals. The proposed system involves Internet of Things technologies and interoperablility standards for health information exchange such as the Fast Healthcare Interoperability Resources and a reference framework architecture for Ambient Assisted Living UniversAAL.This research received no external funding. The APC was funded by Research group Information and Communication Technologies against Climate Change (!CTCC) of the Universitat Politecnica de Valencia, Spain.Lemus Zúñiga, LG.; Félix, JM.; Fides Valero, Á.; Benlloch-Dualde, J.; Martinez-Millana, A. (2022). A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards. Sensors. 22(4):1-17. https://doi.org/10.3390/s2204164611722

Generalized Coordinated Multipoint Framework for 5G and Beyond

The characteristic feature of 5G is the diversity of its services for different user needs. However, the requirements for these services are competing in nature, which impresses the necessity of a coordinated and flexible network architecture. Although coordinated multipoint (CoMP) systems were primarily proposed to improve the cell edge performance in 4G, their collaborative nature can be leveraged to support the diverse requirements and enabling technologies of 5G and beyond networks. To this end, we propose generalization of CoMP to a proactive and efficient resource utilization framework capable of supporting different user requirements such as reliability, latency, throughput, and security while considering network constraints. This article elaborates on the multiple aspects, inputs, and outputs of the generalized CoMP (GCoMP) framework. Apart from user requirements, the GCoMP decision mechanism also considers the CoMP scenario and network architecture to decide upon outputs such as CoMP technique or appropriate coordinating clusters. To enable easier understanding of the concept, popular use cases, such as vehicle-to-everything (V2X) communication and eHealth, are studied. Additionally, interesting challenges and open areas in GCoMP are discussed.Comment: 11 pages, 7 figure

Efficient data uncertainty management for health industrial internet of things using machine learning

[EN] In modern technologies, the industrial internet of things (IIoT) has gained rapid growth in the fields of medical, transportation, and engineering. It consists of a self-governing configuration and cooperated with sensors to collect, process, and analyze the processes of a real-time system. In the medical system, healthcare IIoT (HIIoT) provides analytics of a huge amount of data and offers low-cost storage systems with the collaboration of cloud systems for the monitoring of patient information. However, it faces certain connectivity, nodes failure, and rapid data delivery challenges in the development of e-health systems. Therefore, to address such concerns, this paper presents an efficient data uncertainty management model for HIIoT using machine learning (EDM-ML) with declining nodes prone and data irregularity. Its aim is to increase the efficacy for the collection and processing of real-time data along with smart functionality against anonymous nodes. It developed an algorithm for improving the health services against disruption of network status and overheads. Also, the multi-objective function decreases the uncertainty in the management of medical data. Furthermore, it expects the routing decisions using a machine learning-based algorithm and increases the uniformity in health operations by balancing the network resources and trust distribution. Finally, it deals with a security algorithm and established control methods to protect the distributed data in the exposed health industry. Extensive simulations are performed, and their results reveal the significant performance of the proposed model in the context of uncertainty and intelligence than benchmark algorithms.This research is supported by Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University, Riyadh Saudi Arabia. Authors are thankful for the support.Haseeb, K.; Saba, T.; Rehman, A.; Ahmed, I.; Lloret, J. (2021). Efficient data uncertainty management for health industrial internet of things using machine learning. International Journal of Communication Systems. 34(16):1-14. https://doi.org/10.1002/dac.4948114341

Enable advanced QoS-aware network slicing in 5G networks for slice-based media use cases

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Media use cases for emergency services require mission-critical levels of reliability for the delivery of media-rich services, such as video streaming. With the upcoming deployment of the fifth generation (5G) networks, a wide variety of applications and services with heterogeneous performance requirements are expected to be supported, and any migration of mission-critical services to 5G networks presents significant challenges in the quality of service (QoS), for emergency service operators. This paper presents a novel SliceNet framework, based on advanced and customizable network slicing to address some of the highlighted challenges in migrating eHealth telemedicine services to 5G networks. An overview of the framework outlines the technical approaches in beyond the state-of-the-art network slicing. Subsequently, this paper emphasizes the design and prototyping of a media-centric eHealth use case, focusing on a set of innovative enablers toward achieving end-to-end QoS-aware network slicing capabilities, required by this demanding use case. Experimental results empirically validate the prototyped enablers and demonstrate the applicability of the proposed framework in such media-rich use cases.Peer ReviewedPostprint (author's final draft