scholarly journals The Role of IoT and Cloud Computing in Health Monitoring Systems

2020 ◽  
Author(s):  
Renta Tsiachri ◽  
Soultanopoulos Sotiriadis
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Health Monitoring ◽  
The Elderly ◽  
Lessons Learned ◽  
Time Data ◽  
Chronic Patients ◽  
Wireless Protocols ◽  
Wide Range ◽  
Remote Patient

Cloud computing emerges as the key platform for IoT data storage, processing and analytics due to its simplicity, scalability and affordability (i.e. no up-front investment, low operation costs). Remote patient monitoring in particular can benefit from for this technology in many ways: (a) the new solution is acceptable by many user categories and provides invaluable assistance to chronic patients and the elderly, (b) it is expected to increase users autonomy and confidence and enable self-managing of their condition with the help of caregivers remotely, (c) it reduces the need for face-to-face appointments with doctors and days in hospital. This work reviews key challenges for reliable and secure remote health monitoring based on experience and lessons learned from applying the above technology to the problem of real-time data collection using both wide-range and short-rage wireless protocols and health sensors.

scholarly journals An Intelligent and Secure Health Monitoring Scheme Using IoT Sensor Based on Cloud Computing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jin-Xin Hu ◽  
Chin-Ling Chen ◽  
Chun-Long Fan ◽  
Kun-hao Wang
Keyword(s):  
Cloud Computing ◽  
Health Monitoring ◽  
Cloud Model ◽  
The Elderly ◽  
Security Requirements ◽  
Embedded Devices ◽  
Monitoring Scheme ◽  
Security Issues ◽  
Sensor Cloud ◽  
Cloud Servers

Internet of Things (IoT) is the network of physical objects where information and communication technology connect multiple embedded devices to the Internet for collecting and exchanging data. An important advancement is the ability to connect such devices to large resource pools such as cloud. The integration of embedded devices and cloud servers offers wide applicability of IoT to many areas of our life. With the aging population increasing every day, embedded devices with cloud server can provide the elderly with more flexible service without the need to visit hospitals. Despite the advantages of the sensor-cloud model, it still has various security threats. Therefore, the design and integration of security issues, like authentication and data confidentiality for ensuring the elderly’s privacy, need to be taken into consideration. In this paper, an intelligent and secure health monitoring scheme using IoT sensor based on cloud computing and cryptography is proposed. The proposed scheme achieves authentication and provides essential security requirements.

scholarly journals Improving data availability for brain image biobanking in healthy subjects: practice-based suggestions from an international multidisciplinary working group

2017 ◽  
Author(s):  
◽  
Susan D Shenkin ◽  
Cyril Pernet ◽  
Thomas E Nichols ◽  
Jean-Baptiste Poline ◽  
...  
Keyword(s):  
Data Storage ◽  
Healthy Subjects ◽  
Image Data ◽  
Lessons Learned ◽  
Data Availability ◽  
Brain Diseases ◽  
Linkage Data ◽  
Brain Image ◽  
Wide Range ◽  
Computer Scientists

AbstractBrain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining ‘normality’); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function.

scholarly journals Research on Security Architecture in Mobile Cloud Computing to Prevent Adaptive Anomaly Attacks

2019 ◽  
Vol 8 (6S4) ◽  
pp. 942-947
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Security ◽  
Data Privacy ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Data Accessibility ◽  
Security Issues ◽  
Wide Range ◽  
Rapid Pace

Cloud Computing is a very viable data storage structure where the users can store and access the data from anywhere. Cloud computing use is increasing at a very rapid pace nowadays. But as cloud allows us data accessibility quite easily data security is a major concern and is an emerging area of study. Other issues related to cloud computing are data privacy and internet dependency. On the other cloud computing also has wide range of benefits over traditional storage and accessibility environment such as scalability, flexibility and resource utilization. We have worked in the area of mobile cloud computing to analyse and solve the problems of anomaly attacks. Our work focuses on preventing the adaptive anomaly attacks and some other security issues of cloud computing

scholarly journals Chronopolis Digital Preservation Network

2010 ◽  
Vol 5 (1) ◽  
pp. 119-133 ◽  
Author(s):  
David Minor ◽  
Don Sutton ◽  
Ardys Kozbial ◽  
Brad Westbrook ◽  
Michael Burek ◽  
...  
Keyword(s):  
Data Storage ◽  
High Speed ◽  
San Diego ◽  
Digital Preservation ◽  
Data Grid ◽  
Lessons Learned ◽  
First Year ◽  
Redundant Data ◽  
Wide Range

The Chronopolis Digital Preservation Initiative, one of the Library of Congress’ latest efforts to collect and preserve at-risk digital information, has completed its first year of service as a multi-member partnership to meet the archival needs of a wide range of domains.Chronopolis is a digital preservation data grid framework developed by the San Diego Supercomputer Center (SDSC) at UC San Diego, the UC San Diego Libraries (UCSDL), and their partners at the National Center for Atmospheric Research (NCAR) in Colorado and the University of Maryland's Institute for Advanced Computer Studies (UMIACS).Chronopolis addresses a critical problem by providing a comprehensive model for the cyberinfrastructure of collection management, in which preserved intellectual capital is easily accessible, and research results, education material, and new knowledge can be incorporated smoothly over the long term. Integrating digital library, data grid, and persistent archive technologies, Chronopolis has created trusted environments that span academic institutions and research projects, with the goal of long-term digital preservation.A key goal of the Chronopolis project is to provide cross-domain collection sharing for long-term preservation. Using existing high-speed educational and research networks and mass-scale storage infrastructure investments, the partnership is leveraging the data storage capabilities at SDSC, NCAR, and UMIACS to provide a preservation data grid that emphasizes heterogeneous and highly redundant data storage systems.In this paper we will explore the major themes within Chronopolis, including:a) The philosophy and theory behind a nationally federated data grid for preservation. b) The core tools and technologies used in Chronopolis. c) The metadata schema that is being developed within Chronopolis for all of the data elements. d) Lessons learned from the first year of the project.e) Next steps in digital preservation using Chronopolis: how we plan to strengthen and broaden our network with enhanced services and new customers.

scholarly journals Data collection in urban drainage and stormwater management systems – case studies

2021 ◽  
pp. 415-469
Author(s):  
Alma Schellart ◽  
Frank Blumensaat ◽  
Francois Clemens-Meyer ◽  
Job van der Werf ◽  
Wan Hanna Melina Wan Mohtar ◽  
...  
Keyword(s):  
Data Collection ◽  
Case Studies ◽  
Data Storage ◽  
Stormwater Management ◽  
Lessons Learned ◽  
Management Systems ◽  
Urban Drainage ◽  
Data Validation ◽  
Water Industry ◽  
Wide Range

Abstract Data collection in urban drainage systems comes with many challenges. However, many examples already exist, containing numerous useful lessons learned. This chapter therefore contains several urban drainage and stormwater management metrology case studies, selected to cover a wide range of scopes, scales, objectives, climates, data validation methods, and data storage approaches. The case studies are initiated by academics as well as by institutions from the water industry.

Big Data Technologies and Analytics

2014 ◽  
Vol 1 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Hoda Ahmed Abdelhafez
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Case Studies ◽  
Real World ◽  
Lessons Learned ◽  
The Internet ◽  
Time Data ◽  
Real Time Data ◽  
Big Data Technologies ◽  
Data Volume

The internet era creates new types of large and real-time data; much of those data are non-standard such as streaming and sensor-generated data. Advanced big data technologies enable organizations to extract insights from sophisticated data. Volume, variety and velocity represent big data challenges, which cause difficulties in capture, storage, search, sharing, analysis and visualization. Therefore, technologies like No-SQL, Hadoop and cloud computing used to extract value from large volumes and a wide variety of data to discover business needs. This article's goal is to focus on the challenges of big data and how the recent technologies can be used to address those issues, which are illustrated through real world case studies. The article also presents the lessons learned from these case studies.

The Research of Data Processing Method in a Client Oriented Body Sensor Network

2013 ◽  
Vol 321-324 ◽  
pp. 2828-2832
Author(s):  
Yun Peng Dong ◽  
Ying Long Wang ◽  
Ze Jun Yang ◽  
Tai Bo Huang ◽  
Chao Zhao
Keyword(s):  
Data Processing ◽  
Data Storage ◽  
Independent Living ◽  
The Elderly ◽  
Body Sensor Networks ◽  
Cyberphysical Systems ◽  
Time Data ◽  
Intelligent Terminal ◽  
Application Model ◽  
Data Processing Method

Body sensor networks(BSNs) are emerging cyberphysical systems that promise to improve quality of life through improved healthcare ,independent living for the elderly ,and reduced healthcare costs .In the BSN’s clients oriented family healthcare ,the methods of data storage and reading are important to data processing in BSN . This paper mainly studies the methods of real-time data storage in a BSN’s intelligent terminal based Android through the analysis of the client module’s architecture in a BSN .And an application model is finally designed to meet the need of mobile healthcare system’s data storage and reading .The application model is suitable for a BSN oriented family healthcare .Through testing this application model ,it indicates that this application model makes users more convenient to manage their medical data.

scholarly journals Cloud Computing in Bioinformatics: current solutions and challenges

2016 ◽  
Author(s):  
Barbara Calabrese ◽  
Mario Cannataro
Keyword(s):  
Experimental Data ◽  
Cloud Computing ◽  
Data Storage ◽  
High Throughput ◽  
Virtual Space ◽  
Security And Privacy ◽  
Analysis Pipeline ◽  
Complete Control ◽  
Platform As A Service ◽  
Wide Range

Abstract truncated at 3,000 characters - the full version is available in the pdf file MOTIVATIONS The availability of high-throughput technologies and the application of genomics and pharmacogenomics studies of large populations, are producing an increasing amount of experimental and clinical data, as well as specialized databases spread over the Internet. The storage, preprocessing and analysis of experimental data is becoming the main bottleneck of the analysis pipeline. Managing omics data requires both space for data storing as well as services for data preprocessing, analysis, and sharing. The resulting scenario comprises a set of bioinformatics tools, often implemented as web services, for the management and analysis of data stored in geographically distributed biological databases [1]. Cloud computing may play an important role in many phases of the bioinformatics analysis pipeline, from data management and processing, to data integration and analysis, including data exploration and visualization because it offers massive scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, thus it may represent the key technology for facing those issues [2]. METHODS This work reviews main academic and industrial cloud-based bioinformatics solutions developed in the recent years; moreover, it underlines main issues and problems related to the use of such platforms for the storage and analysis of patients’ data. Specifically, the analysed solutions regard: - Data as a Service (DaaS): it provides data storage in a dynamic virtual space hosted by the cloud and allows to have updated data that are accessible from a wide range of connected devices on the web. - Software as a Service (SaaS): several cloud-based tools to execute different bioinformatics tasks, e.g. mapping applications, sequences alignment, gene expression analysis have been proposed and made available. - Platform as a Service (PaaS): unlike SaaS solutions, PaaS solutions allow users to customize the deployment of bioinformatics applications as well as to retain complete control over their instances and associated data. - Infrastructure as a Service (IaaS): this service model is offered in a computing infrastructure that includes servers (typically virtualized) with specific computational capability and/or storage. The user controls all the deployed storage resources, operating systems and bioinformatics applications. For each analysed solution, main technical characteristics as well as security and privacy issues arising when storing and analysing patients data, are reported. RESULTS The application of cloud computing in bioinformatics regards the efficient storage, retrieval and integration of experimental data and their efficient and high-throughput preprocessing and analysis.

Homomorphic Encryption as a Service for Outsourced Images in Mobile Cloud Computing Environment

2017 ◽  
Vol 7 (2) ◽  
pp. 27-40 ◽  
Author(s):  
Mouhib Ibtihal ◽  
El Ouadghiri Driss ◽  
Naanani Hassan
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Mobile Cloud Computing ◽  
Homomorphic Encryption ◽  
Data Availability ◽  
Mobile Cloud ◽  
Time Data ◽  
Cloud Computing Environment ◽  
Encryption Decryption ◽  
Cloud Servers

The integration of cloud computing with mobile computing and internet has given birth to mobile cloud computing. This technology offers many advantages to users, like Storage capacity, Reliability, Scalability and Real time data availability. Therefore, it is s increasing fast and it is inevitably integrated into everyday life. In MCC, data processing and data storage can be migrated into the cloud servers. However, the confidentiality of images and data is most important in today's environment. In this paper, we mainly focus on secure outsourcing of images. For this purpose, we propose a secure architecture composed by two clouds a private cloud dedicated for encryption/decryption and a second public cloud dedicated for storage. We have implemented the first cloud using openstack while respecting the encryption as a service concept. As an encryption scheme, we have used paillier's homomorphic cryptosystem designed specifically for images. The test of the homomorphic property is done by applying the Watermarking algorithm DWT.

scholarly journals Evolution of Wearable Devices with Real-Time Disease Monitoring for Personalized Healthcare

Nanomaterials ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 813 ◽  
Author(s):  
Kyeonghye Guk ◽  
Gaon Han ◽  
Jaewoo Lim ◽  
Keunwon Jeong ◽  
Taejoon Kang ◽  
...  
Keyword(s):  
Real Time ◽  
Health Monitoring ◽  
Point Of Care ◽  
Monitoring And Evaluation ◽  
The Elderly ◽  
Wearable Devices ◽  
Medical Diagnostics ◽  
Biomedical Monitoring ◽  
Wide Range ◽  
Barriers And Challenges

Wearable devices are becoming widespread in a wide range of applications, from healthcare to biomedical monitoring systems, which enable continuous measurement of critical biomarkers for medical diagnostics, physiological health monitoring and evaluation. Especially as the elderly population grows globally, various chronic and acute diseases become increasingly important, and the medical industry is changing dramatically due to the need for point-of-care (POC) diagnosis and real-time monitoring of long-term health conditions. Wearable devices have evolved gradually in the form of accessories, integrated clothing, body attachments and body inserts. Over the past few decades, the tremendous development of electronics, biocompatible materials and nanomaterials has resulted in the development of implantable devices that enable the diagnosis and prognosis through small sensors and biomedical devices, and greatly improve the quality and efficacy of medical services. This article summarizes the wearable devices that have been developed to date, and provides a review of their clinical applications. We will also discuss the technical barriers and challenges in the development of wearable devices, and discuss future prospects on wearable biosensors for prevention, personalized medicine and real-time health monitoring.

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