Reporting an Experience on Design and Implementation of e-Health Systems on Azure Cloud

Electronic Health (e-Health) technology has brought the world with significant transformation from traditional paper-based medical practice to Information and Communication Technologies (ICT)-based systems for automatic management (storage, processing, and archiving) of information. Traditionally e-Health systems have been designed to operate within stovepipes on dedicated networks, physical computers, and locally managed software platforms that make it susceptible to many serious limitations including: 1) lack of on-demand scalability during critical situations; 2) high administrative overheads and costs; and 3) in-efficient resource utilization and energy consumption due to lack of automation. In this paper, we present an approach to migrate the ICT systems in the e-Health sector from traditional in-house Client/Server (C/S) architecture to the virtualised cloud computing environment. To this end, we developed two cloud-based e-Health applications (Medical Practice Management System and Telemedicine Practice System) for demonstrating how cloud services can be leveraged for developing and deploying such applications. The Windows Azure cloud computing platform is selected as an example public cloud platform for our study. We conducted several performance evaluation experiments to understand the Quality Service (QoS) tradeoffs of our applications under variable workload on Azure.Comment: Submitted to third IEEE International Conference on Cloud and Green Computing (CGC 2013

Cloud-assisted body area networks: state-of-the-art and future challenges

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed

Securing Patient Data Access using Segmented Key Management Approach

Cloud technology can be utilized to empower data sharing capacities, which can profit the client through more noteworthy efficiency and profitability. Nonetheless, the Cloud is defenseless to numerous security vulnerabilities and privacy, which thwarts the advance and wide scale reception of data sharing for the reasons for cooperation. Along these lines, there is a solid interest for data owners to not just guarantee that their information is kept private and secure in the Cloud, however to likewise have a level of control over their own particular data contents once they are imparted to data consumers. In particular, the principle issues for data sharing in the Cloud incorporate security attacks, key management and data owner access control. As far as key management, it is key that data should first be encrypted before storage in the Cloud, to prevent security breaches and privacy. In this paper, a segmented key management is proposed

BodyCloud: a SaaS approach for community body sensor networks

Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behaviour surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals

A STUDY ON CLOUD BASED BIO-SIGNALS MANAGEMENT FRAMEWORK

An analytical study of the complete framework for the management of biosignals is done. The framework provides for the acquisition, and storage of the biosignals, along with the associated metadata. It also provides solutions for validation, synchronization of acquired signals, thus allowing error-free signal inputs for further statistical analysis. The model comprises primarily of four layers, namely, acquisition, validation, post-processing and statistical analysis layers. Additionally, a presentation layer is also provided, wherein the appropriate end-user can use a suitable client or Web service to access the results of the statistical analysis. The raw data is deliberately spilt into two: Internal data (actual signal data) and External data (metadata) and they interact only when necessary (e.g. Identifying the biosignal's origin). Microservices are used to compartmentalize the functionalities required in the system. Additional solutions to problems plaguing the present models (like cloud-upload bottleneck) are also discussed.Â

A Dynamic Scaling Methodology for Improving Performance of Big Data Systems

The continuous growth of data volume in various fields such as, healthcare, sciences, economics, and business has caused an overwhelming flow of data in the last decade. The overwhelming flow of data has raised challenges in processing, analyzing, and storing data, which lead many systems to face an issue in performance. Poor performance of systems creates negative impact such as delays, unprocessed data, and increasing response time. Processing huge amounts of data demands a powerful computational infrastructure to ensure that data processing and analysis success [7]. However, the architectures of these systems are not suitable to process that quantity of data. This calls for necessity to develop a methodology to improve the performance of systems handle massive amount of data. This thesis presents a novel dynamic scaling methodology to improve the performance of big data systems. The dynamic scaling methodology is developed to scale up the system based on the several aspects from the big data perspective. Moreover, these aspects are used by the helper project algorithm which is designed to divide a task into small chunks to be processed by the system. These small chunks run on several virtual machines to work in parallel to enhance the system’s runtime performance. In addition, the dynamic scaling methodology does not require many modifications on the applied, which makes it easy to use. The dynamic scaling methodology improves the performance of the big data system significantly. As a result, it provides a solution for performance failures in systems that process huge amount of data. This is study would be beneficial to IT researches that focus on performance of big data systems

Providing security and fault tolerance in P2P connections between clouds for mHealth services

[EN] The mobile health (mHealth) and electronic health (eHealth) systems are useful to maintain a correct administration of health information and services. However, it is mandatory to ensure a secure data transmission and in case of a node failure, the system should not fall down. This fact is important because several vital systems could depend on this infrastructure. On the other hand, a cloud does not have infinite computational and storage resources in its infrastructure or would not provide all type of services. For this reason, it is important to establish an interrelation between clouds using communication protocols in order to provide scalability, efficiency, higher service availability and flexibility which allow the use of services, computing and storage resources of other clouds. In this paper, we propose the architecture and its secure protocol that allows exchanging information, data, services, computing and storage resources between all interconnected mHealth clouds. The system is based on a hierarchic architecture of two layers composed by nodes with different roles. The routing algorithm used to establish the connectivity between the nodes is the shortest path first (SPF), but it can be easily changed by any other one. Our architecture is highly scalable and allows adding new nodes and mHealth clouds easily, while it tries to maintain the load of the cloud balanced. Our protocol design includes node discovery, authentication and fault tolerance. We show the protocol operation and the secure system design. Finally we provide the performance results in a controlled test bench.Lloret, J.; Sendra, S.; Jimenez, JM.; Parra-Boronat, L. (2016). Providing security and fault tolerance in P2P connections between clouds for mHealth services. Peer-to-Peer Networking and Applications. 9(5):876-893. doi:10.1007/s12083-015-0378-3S87689395The Fifty-eighth World Health Assembly, Resolutions and Decisions. Document: A58/21. 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An enhanced healthcare system in mobile cloud computing environment

Abstract Mobile cloud computing (MCC) is a new technology for mobile web services. Accordingly, we assume that MCC is likely to be of the heart of healthcare transformation. MCC offers new kinds of services and facilities for patients and caregivers. In this regard, we have tried to propose a new mobile medical web service system. To this end, we implement a medical cloud multi-agent system (MCMAS) solution for polyclinic ESSALEMA Sfax—TUNISIA, using Google's Android operating system. The developed system has been assessing using the CloudSim Simulator. This paper presents initial results of the system in practice. In fact the proposed solution shows that the MCMAS has a commanding capability to cope with the problem of traditional application. The performance of the MCMAS is compared with the traditional system in polyclinic ESSALEMA which showed that this prototype yields better recital than using usual application