Mobile Cloud Computing in Healthcare Using Dynamic Cloudlets for Energy-Aware Consumption

Mobile cloud computing (MCC) has increasingly been adopted in healthcare industry by healthcare professionals (HCPs) which has resulted in the growth of medical software applications for these platforms. There are different applications which help HCPs with many important tasks. Mobile cloud computing has helped HCPs in better decision making and improved patient care. MCC enables users to acquire the benefit of cloud computing services to meet the healthcare demands. However, the restrictions posed by network bandwidth and mobile device capacity has brought challenges with respect to energy consumption and latency delays. In this paper we propose dynamic energy consumption mobile cloud computing model (DEMCCM) which addresses the energy consumption issue by healthcare mobile devices using dynamic cloudlets

Data Security in Cloud for Medical Sciences using AES 512-bit Algorithm

Cloud computing is typically defined as a type of evaluation that relies on sharing computing resources to handle applications. There is a requirement to redesign the medical system to meet their better needs among the growing technologies .With the initiation of cloud computing the doctors can keep their information about the latest diseases, emergency cases and complex problems. However the privacy and security of data is extremely exigent. To guarantee privacy and security of data in cloud computing, we have proposed an effective approach for data security by the process of encrypting and decrypting the data through the concept of cryptography. In this paper the proposal is to prevent data access from cloud data storage centers by unauthorized access using AES-512 bit algorithm with key size and input block size of 512-bits that makes it more defiant to cryptanalysis. DOI: 10.17762/ijritcc2321-8169.16040

Key Security and Privacy Related Factors Influencing the Use of Cloud Computing in SMEs

Cloud computing-enabled solutions are widely used in various areas such as business, government, medical, education and entertainment. Indeed, cloud computing can deliver the most advanced software applications, hardware resources, and other services to both large organisations and small and medium-sized enterprises (SMEs). Even though, there are many advantages of using cloud computing, SMEs are slow in accepting cloud computing due to security and privacy related concerns. Additionally, the growing recognition of the importance of security and privacy has caused some concerns for organisations about the use of cloud computing. Therefore, the purpose of this study is to examine and identify the security and privacy related factors that influence the use of cloud computing by SMEs. The structured literature review has determined key eight security and privacy related factors linked to technical, social, organisational and environmental construct relation to SMEs. Based on the literature review analysis, an integrated framework was inspired by three theoretical frameworks, namely Technology-organization-environment (TOE) framework, Human organisation and technology-fit (HOT-fit) framework and Institutional theory. The conceptual model is presented in this paper in relation to the use of cloud computing among SMEs. The model presented in the paper provides viewpoints from which to identify the security and privacy related factors affecting the use of cloud computing by SMEs

Data mining for autonomous wearable sensors used for elderly healthcare monitoring.

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The paper presents some aspects regarding data mining used modeling and prediction of the patients’ health state parameters. The proposed wearable device integrated by using wireless personal networks (WPNs) can sense, process and communicate vital signs through internet for healthcare monitoring. These WPNs are fitted for medical applications and offer continuous ambulatory health monitoring by using non-invasive methods. Generally, the body sensor network (BSN) for medical applications are based on big data fusion and cloud computing technologies (PaaS, SaaS - for data storage and sharing solutions). The big data fusion includes preprocessing (filter the noise), feature extraction (data abstraction), data fusion computation (modeling different information type and fusion), and data compression (reducing the information stored in memory and transmitted by the transceiver). The fusion between wearable wireless body sensor network (WWBSN), IoT and Cloud Computing will allow doctors, emergency stations or caregivers to track and receive data from BSNs about patients in different places. By using biomedical sensors can be studied the human behavior and physiology, the body's response physiologically and emotionally to various physical and mental diseases. The WWBSN can cover monitoring for cardiovascular, diabetic problems or mental disorders (Alzheimer).European Cooperation in Science and Technology. COS

A Bibliometric Analysis of Health Cloud Scientific\u27s Productions

Introduction: Cloud computing is an innovative paradigm meeting the user\u27s demand for accessing a shared source comprising adjustable computational sources, such as servers and applied programs. An increase in the costs of information technology, emerging problems with updating software and hardware, and expanded storage volume, make it possible to utilize cloud-based health information cases. Organizations have focused on cloud platform-based services as a new opportunity to develop the software industry for healthcare. The aim of the research is to conduct a bibliometric study of the scientific productions on health cloud . Methodology: The present study, applied in nature, was conducted using a bibliometric and scientometric method. It was conducted in 2018 using PubMed and key portmanteaus over the period 2009-2018. Subjected to the application of input and output standards, 491 research papers were selected for analysis. Findings: The findings revealed that the production of health cloud-focused papers over a decade, excluding those in 2017, had an upward trend. The US, India, and China were the most productive in this respect. Having presented 5 papers on cloud computing, Costa, Lee, Malamateniou, Stoicu-Tivadar, Vassilacopoulos, writers, were most productive. The greatest co-occurrence was that of the words Internet, electronic health records, computer security, information storage and retrieval, algorithms, confidentiality, female, male, delivery of health care, computer communication networks, medical informatics, mobile applications, data mining, and health information exchang. Conclusion: The results of the present study indicate the leading status of the USA in health cloud publications. In view of the recognition received for using cloud computing, the trend of the papers in the base was upward in nature. On analysis of the co-occurrence of words, the largest cluster was that of cloud computing with 6 items focused on: The Internet of Things (IoT), Electronic health record, healthcare, and e-health in one cluster, indicating the continuity of the issues

MRI analysis for Hippocampus segmentation on a distributed infrastructure

Medical image computing raises new challenges due to the scale and the complexity of the required analyses. Medical image databases are currently available to supply clinical diagnosis. For instance, it is possible to provide diagnostic information based on an imaging biomarker comparing a single case to the reference group (controls or patients with disease). At the same time many sophisticated and computationally intensive algorithms have been implemented to extract useful information from medical images. Many applications would take great advantage by using scientific workflow technology due to its design, rapid implementation and reuse. However this technology requires a distributed computing infrastructure (such as Grid or Cloud) to be executed efficiently. One of the most used workflow manager for medical image processing is the LONI pipeline (LP), a graphical workbench developed by the Laboratory of Neuro Imaging (http://pipeline.loni.usc.edu). In this article we present a general approach to submit and monitor workflows on distributed infrastructures using LONI Pipeline, including European Grid Infrastructure (EGI) and Torque-based batch farm. In this paper we implemented a complete segmentation pipeline in brain magnetic resonance imaging (MRI). It requires time-consuming and data-intensive processing and for which reducing the computing time is crucial to meet clinical practice constraints. The developed approach is based on web services and can be used for any medical imaging application

EdgeEcho: An Architecture for Echocardiology at the Edge

Edge computing technologies have improved delays and privacy of several applications, including in medical imaging and eHealth. In this paper, we consider ultrasound technology and echocardiology (echo) and empower it with edge computing. Despite the many advances that ultrasound technology has seen recently, e.g., it is possible to perform echo scans using wireless ultrasound probes, the use of Artificial Intelligence (AI) techniques is becoming a necessity, for faster and more accurate echo diagnosis (not limited to heart diseases). While a few proprietary solutions exist that embed AI within echo devices, none of them uses resource-intensive tasks on handheld devices, and none of them is open-source. To this end, we propose EdgeEcho, an architecture that captures ultrasound data originated from handheld ultrasound probes and tags it using semantic segmentation performed on edge cloud. Our prototype focuses on optimizing the management of edge resources to address the specific requirements of echocardiology and the challenges of serving AI algorithms responsively. As a use case, we focus on a ventricular volume detection operation. Our performance evaluation results show that EdgeEcho can support multiple parallel medical video processing streaming sessions for continuing medical education, demonstrating a promising edge computing application with life-saving potential

Joint Elastic Cloud and Virtual Network Framework for Application Performance-cost Optimization

International audienceCloud computing infrastructures are providing resources on demand for tackling the needs of large-scale distributed applications. To adapt to the diversity of cloud infras- tructures and usage, new operation tools and models are needed. Estimating the amount of resources consumed by each application in particular is a difficult problem, both for end users who aim at minimizing their costs and infrastructure providers who aim at control- ling their resources allocation. Furthermore, network provision is generally not controlled on clouds. This paper describes a framework automating cloud resources allocation, deploy- ment and application execution control. It is based on a cost estimation model taking into account both virtual network and nodes managed by the cloud. The flexible provisioning of network resources permits the optimization of applications performance and infrastructure cost reduction. Four resource allocation strategies relying on the expertise that can be cap- tured in workflow-based applications are considered. Results of these strategies are confined virtual infrastructure descriptions that are interpreted by the HIPerNet engine responsible for allocating, reserving and configuring physical resources. The evaluation of this framework was carried out on the Aladdin/Grid'5000 testbed using a real application from the area of medical image analysis

CIVILITY: Cloud based interactive visualization of tractography brain connectome

Cloud based Interactive Visualization of Tractography Brain Connectome (CIVILITY) is an interactive visualization tool of brain connectome in the cloud. This application submits tasks to remote computing grids were the CIVILITY-tractography pipeline is deployed. The application will list the running tasks for the user and once a task is completed the brain connectome is visualized using Hierarchical Edge Bundling. The analysis pipeline uses FSL tools (bedpostx and probtrackx2) to generate a triangular matrix indicating the connectivity strength between different regions in the brain. This work is motivated by medical applications in which expensive computational tasks such as brain connectivity is needed and to provide a state of the art visualization tool of Brain Connectome. This work does not contribute any novelty with respect to the visualization methodology, is rather a new resource for the neuroimaging community. This work is submitted to the SPIE Biomedical Applications in Molecular, Structural, and Functional Imaging conference. The source code of this application is available in NITRC

FLBP: A Federated Learning-enabled and Blockchain-supported Privacy-Preserving of Electronic Patient Records for the Internet of Medical Things

The evolution of the computing paradigms and the Internet of Medical Things (IoMT) have transfigured the healthcare sector with an alarming rise of privacy issues in healthcare records. The rapid growth of medical data leads to privacy and security concerns to protect the confidentiality and integrity of the data in the feature-loaded infrastructure and applications. Moreover, the sharing of medical records of a patient among hospitals rises security and interoperability issues. This article, therefore, proposes a Federated Learning-and-Blockchain-enabled framework to protect electronic medical records from unauthorized access using a deep learning technique called Artificial Neural Network (ANN) for a collaborative IoMT-Fog-Cloud environment. ANN is used to identify insiders and intruders. An Elliptical Curve Digital Signature (ECDS) algorithm is adopted to devise a secured Blockchain-based validation method. To process the anti-malicious propagation method, a Blockchain-based Health Record Sharing (BHRS) is implemented. In addition, an FL approach is integrated into Blockchain for scalable applications to form a global model without the need of sharing and storing the raw data in the Cloud. The proposed model is evident from the simulations that it improves the operational cost and communication (latency) overhead with a percentage of 85.2% and 62.76%, respectively. The results showcase the utility and efficacy of the proposed model