Ciprian Dobre – författare

Pervasive Computing: Next Generation Platforms for Intelligent Data Collection presents current advances and state-of-the-art work on methods, techniques, and algorithms designed to support pervasive collection of data under ubiquitous networks of devices able to intelligently collaborate towards common goals.

Using numerous illustrative examples and following both theoretical and practical results the authors discuss: a coherent and realistic image of today's architectures, techniques, protocols, components, orchestration, choreography, and developments related to pervasive computing components for intelligently collecting data, resource, and data management issues; the importance of data security and privacy in the era of big data; the benefits of pervasive computing and the development process for scientific and commercial applications and platforms to support them in this field.

Pervasive computing has developed technology that allows sensing, computing, and wireless communication to be embedded in everyday objects, from cell phones to running shoes, enabling a range of context-aware applications. Pervasive computing is supported by technology able to acquire and make use of the ubiquitous data sensed or produced by many sensors blended into our environment, designed to make available a wide range of new context-aware applications and systems. While such applications and systems are useful, the time has come to develop the next generation of pervasive computing systems. Future systems will be data oriented and need to support quality data, in terms of accuracy, latency and availability.

Pervasive Computing is intended as a platform for the dissemination of research efforts and presentation of advances in the pervasive computing area, and constitutes a flagship driver towards presenting and supporting advanced research in this area.

Indexing: The books of this series are submitted to EI-Compendex and SCOPUS

Ambient Assisted Living and Enhanced Living Environments: Principles, Technologies and Control separates the theoretical concepts concerning the design of such systems from their real-world implementations. For each important topic, the book bridges theory and practice, introducing the instruments needed by professionals in their activities.

To this aim, topics are presented in a logical sequence, with the introduction of each topic motivated by the need to respond to claims and requirements from a wide range of AAL/ELE applications. The advantages and limitations of each model or technology are presented through concrete case studies for AAL/ELE systems.

The book also presents up-to-date technological solutions to the main aspects regarding AAL/ELE systems and applications, a highly dynamic scientific domain that has gained much interest in the world of IT in the last decade. In addition, readers will find discussions on recent AAL/ELE technologies that were designed to solve some of the thorniest business problems that affect applications in areas such as health and medical supply, smart city and smart housing, Big Data and Internet of Things, and many more.

This book presents current progress on challenges related to Big Data management by focusing on the particular challenges associated with context-aware data-intensive applications and services. The book is a state-of-the-art reference discussing progress made, as well as prompting future directions on the theories, practices, standards and strategies that are related to the emerging computational technologies and their association with supporting the Internet of Things advanced functioning for organizational settings including both business and e-science. Apart from inter-operable and inter-cooperative aspects, the book deals with a notable opportunity namely, the current trend in which a collectively shared and generated content is emerged from Internet end-users. Specifically, the book presents advances on managing and exploiting the vast size of data generated from within the smart environment (i.e. smart cities) towards an integrated, collective intelligence approach. The book also presents methods and practices to improve large storage infrastructures in response to increasing demands of the data intensive applications. The book contains 19 self-contained chapters that were very carefully selected based on peer review by at least two expert and independent reviewers and is organized into the three sections reflecting the general themes of interest to the IoT and Big Data communities:

Section I: Foundations and Principles

Section II: Advanced Models and Architectures

Section III: Advanced Applications and Future Trends

The book is intended for researchers interested in joining interdisciplinary and transdisciplinary works in the areas of Smart Environments, Internet of Things and various computational technologies for the purpose of an integrated collective computational intelligence approach into the Big Data era.

This book reports on the latest advances on the theories, practices, standards and strategies that are related to the modern technology paradigms, the Mobile Cloud computing (MCC) and Big Data, as the pillars and their association with the emerging 5G mobile networks.

The book includes 15 rigorously refereed chapters written by leading international researchers, providing the readers with technical and scientific information about various aspects of Big Data and Mobile Cloud Computing, from basic concepts to advanced findings, reporting the state-of-the-art on Big Data management. It demonstrates and discusses methods and practices to improve multi-source Big Data manipulation techniques, as well as the integration of resources availability through the 3As (Anywhere, Anything, Anytime) paradigm, using the 5G access technologies.

This book reports on the latest advances in mobile technologies for collecting, storing and processing mobile big data in connection with wireless communications. It presents novel approaches and applications in which mobile big data is being applied from an engineering standpoint and addresses future theoretical and practical challenges related to the big data field from a mobility perspective.

Further, it provides an overview of new methodologies designed to take mobile big data to the Cloud, enable the processing of real-time streaming events on-the-move and enhance the integration of resource availability through the ‘Anywhere, Anything, Anytime’ paradigm. By providing both academia and industry researchers and professionals with a timely snapshot of emerging mobile big data-centric systems and highlighting related pitfalls, as well as potential solutions, the book fills an important gap in the literature and fosters the further development in the area of mobile technologies for exploiting mobile big data.

This book consists of eight chapters, five of which provide a summary of the tutorials and workshops organised as part of the cHiPSet Summer School: High-Performance Modelling and Simulation for Big Data Applications Cost Action on “New Trends in Modelling and Simulation in HPC Systems,” which was held in Bucharest (Romania) on September 21–23, 2016. As such it offers a solid foundation for the development of new-generation data-intensive intelligent systems. 

Modelling and simulation (MS) in the big data era is widely considered the essential tool in science and engineering to substantiate the prediction and analysis of complex systems and natural phenomena. MS offers suitable abstractions to manage the complexity of analysing big data in various scientific and engineering domains. Unfortunately, big data problems are not always easily amenable to efficient MS over HPC (high performance computing). Further, MS communities may lack the detailed expertise required to exploit the full potential of HPC solutions, and HPC architects may not be fully aware of specific MS requirements.

The main goal of the Summer School was to improve the participants’ practical skills and knowledge of the novel HPC-driven models and technologies for big data applications. The trainers, who are also the authors of this book, explained how to design, construct, and utilise the complex MS tools that capture many of the HPC modelling needs, from scalability to fault tolerance and beyond. In the final three chapters, the book presents the first outcomes of the school: new ideas and novel results of the research on security aspects in clouds, first prototypes of the complex virtual models of data in big data streams and a data-intensive computing framework for opportunistic networks. It is a valuable reference resource for those wanting to start working in HPC and big data systems, as well as for advanced researchers and practitioners.