George Suciu

Big Data, Internet of Things and Cloud Convergence --- An Architecture for Secure E-Health Applications

Big data storage and processing are considered as one of the main applications for cloud computing systems. Furthermore, the development of the Internet of Things (IoT) paradigm has advanced the research on Machine to Machine (M2M) communications and enabled novel tele-monitoring architectures for E-Health applications. However, there is a need for converging current decentralized cloud systems, general software for processing big data and IoT systems. The purpose of this paper is to analyze existing components and methods of securely integrating big data processing with cloud M2M systems based on Remote Telemetry Units (RTUs) and to propose a converged E-Health architecture built on Exalead CloudView, a search based application. Finally, we discuss the main findings of the proposed implementation and future directions.

Developing and Operating Time Critical Applications in Clouds: The State of the Art and the SWITCH Approach☆

Cloud environments can provide virtualized, elastic, controllable and high quality on-demand services for supporting complex distributed applications. However, the engineering methods and software tools used for developing, deploying and executing classical time critical applications do not, as yet, account for the programmability and controllability provided by clouds, and so time critical applications cannot yet benefit from the full potential of cloud technology. This paper reviews the state of the art of technologies involved in developing time critical cloud applications, and presents the approach of a recently funded EU H2020 project: the Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications (SWITCH). SWITCH aims to improve the existing development and execution model of time critical applications by introducing a novel conceptual model—the application-infrastructure co-programming and control model—in which application QoS and QoE, together with the programmability and controllability of cloud environments, is included in the complete application lifecycle.

Cloud consulting: ERP and communication application integration in open source cloud systems

Cloud Consulting combines open source grid computing for distributed cloud computing and Enterprise Resource Modeling (ERP) to provide Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) through a simple, unified API. Cloud communications refers to using internet-based or cloud-based voice and data communications services, where telecommunications applications, switching, and storage are managed generally by third parties. These services can include capabilities that range from Voice over IP (VoIP) communications to hosted PBX and unified communications delivering voice, fax, video, and data requirements. Provisioning for these services is known as Communication as a Service (CaaS).

Big data search for environmental telemetry

This paper presents how Exalead CloudView is used to search for environmental parameters in big data. In particular, given an environmental application, we propose to leverage trivial and non-trivial connections between different sensor signals, in order to find patterns that are likely to provide innovative solutions to existing problems and to establish different data models. The aggregation of such data models will provide evidence of connections between different events and environmental responses to those triggers, faster and better than trivial mining sensors data. As a consequence, the software has a significant potential for matching environmental applications and challenges that are related in non-obvious ways.

A Software Workbench for Interactive, Time Critical and Highly Self-Adaptive Cloud Applications (SWITCH)

Time critical applications have very high requirements on network and computing services, in particular on well-tuned software architecture with sophisticated optimisation on data communication. Their development is often customised to dedicated infrastructure, and system performance is difficult to maintain when infrastructure changes. This fatal weakness in existing architecture and software tools causes very high development costs, and makes it difficult to fully utilise the virtualised, programmable and quality-on-demand services provided by networked Clouds to improve the system productivity. The Software Workbench for Interactive, Time Critical and Highly self-adaptive Cloud applications (SWITCH) is a newly funded project by EU H2020 to address this urgent industrial need, it aims at improving the existing development and execution model of time critical applications by introducing a novel conceptual model called application-infrastructure co-programming and control model, in which application QoS/QoE together with the programmability and controllability of Cloud environments can be all included in the complete lifecycle of applications.

Public versus private cloud adoption — A case study based on open source cloud platforms

The traditional way of IT in a company can be very problematic. As changes in the business environment occur, so appears the need to implement more efficient enterprise systems. Cloud Computing technology represents the next enterprise computing paradigm and a solution to most current enterprise IT problems. This article examines how Cloud technology has evolved and the way it affects related performances by presenting a case study based on SlapOS open source Cloud platform.

M2M remote telemetry and cloud IoT big data processing in viticulture

Current M2M communication platforms are being integrated in cloud IoT applications for providing remote sensing and actuating. Nevertheless, requirements for energy efficiency and resilience in severe operating environments are driving the development of new algorithms and infrastructures. This paper presents a survey of the measurement results for the winegrowing season 2014, as it was seen by an M2M remote telemetry station in cooperation with a big data processing platform and several sensors. We demonstrate the use of recent technologies such as Cloud IoT systems and Big Data processing in order to implement disease prediction and alerting application for viticulture. Finally, the extension of the proposed system for other agriculture applications is discussed.

Wearable sensors and cloud platform for monitoring environmental parameters in e-health applications

Wearable sensors enable long-term continuous physiological and environmental monitoring, which is important for the treatment and management of many chronic illnesses, neurological disorders, and mental health issues. In this paper we describe an experimental model based on wearable sensors, designed for monitoring the health condition of the patients. The system is based on a wearable sensor network connected to a cloud platform which aggregates the data received from the sensors. The sensors measure different parameters such as body temperature, humidity from the air, which are transmitted by microprocessor through a gateway to a cloud storage system. The data gathered in the cloud system is available for further processing, for the study of some correlations between measured parameters, environment conditions and health condition of the patients.

Big Data, Internet of Things and Cloud Convergence for E-Health Applications

Big data storage and processing are considered as one of the main applications for cloud computing systems. Furthermore, the development of the Internet of Things (IoT) paradigm has advanced the research on Machine to Machine (M2M) communications and enabled novel tele-monitoring architectures for E-Health applications. However, there is a need for converging current decentralized cloud systems, general software for processing big data and IoT systems. The purpose of this paper is to analyze existing components and methods of integrating big data processing with cloud M2M systems based on Remote Telemetry Units (RTUs) and to propose a converged E-Health architecture built on Exalead CloudView, a search based application. Finally, we discuss the main findings of the proposed implementation and future directions.

Big data fusion for eHealth and Ambient Assisted Living Cloud Applications

Senior citizens may suffer from a number of diseases, including but not limited to mild dementia and cognitive disabilities, which require either the institutionalization or the constant support from care-givers. To successfully help the senior citizens, usually a monitoring system is needed that can gather and process data from various sensors about the healthcare condition of the user and environment parameters (noise level, light, humidity and temperature). In this paper we describe a cloud-based approach for monitoring the healthcare condition of senior citizens and the fusion of big data from heterogeneous information flows coming from the sensors. Furthermore, because context understanding is not easily done with a single source of metadata, we analyze metadata available from various online sources, aiming to understand the context of its users, in order to offer them personalized eHealth services.