Smart Data Pricing

A comprehensive text addressing the high demand for network, cloud, and content services through cutting-edge research on data pricing and business strategies Smart Data Pricing tackles the timely issue of surging demand for network, cloud, and content services and corresponding innovations in pricing these services to benefit consumers, operators, and content providers. The pricing of data traffic and other services is central to the core challenges of network monetization, growth sustainability, and bridging the digital divide. In this book, experts from both academia and industry discuss all aspects of smart data pricing research and development, including economic analyses, system development, user behavior evaluation, and business strategies. Smart Data Pricing: • Presents the analysis of leading researchers from industry and academia surrounding the pricing of network services and content. • Discusses current trends in mobile and wired data usage and their economic implications for content providers, network operators, end users, government regulators, and other players in the Internet ecosystem. • Includes new concepts and background technical knowledge that will help researchers and managers effectively monetize their networks and improve user quality-of-experience. • Provides cutting-edge research on business strategies and initiatives through a diverse collection of perspectives. • Combines academic and industry expertise from multiple disciplines and business organizations. The ideas and background of the technologies and economic principles discussed within these chapters are of real value to practitioners, researchers, and managers in identifying trends and deploying new pricing and network management technologies, and will help support managers in identifying new business directions and innovating solutions to challenging business problems.

"Provides insight on business strategies and initiatives through a diverse collection of perspectives"--

The third edition of this popular reference covers enabling technologies for building up 5G wireless networks. Due to extensive research and complexity of the incoming solutions for the next generation of wireless networks it is anticipated that the industry will select a subset of these results and leave some advanced technologies to be implemented later,. This new edition presents a carefully chosen combination of the candidate network architectures and the required tools for their analysis. Due to the complexity of the technology, the discussion on 5G will be extensive and it will be difficult to reach consensus on the new global standard. The discussion will have to include the vendors, operators, regulators as well as the research and academic community in the field. Having a comprehensive book will help many participants to join actively the discussion and make meaningful contribution to shaping the new standard.

The book examines how Fog will change the information technology industry in the next decade. Fog distributes the services of computation, communication, control and storage closer to the edge, access and users. As a computing and networking architecture, Fog enables key applications in wireless 5G, the Internet of Things, and big data. The authors cover the fundamental tradeoffs to major applications of fog. The book chapters are designed to motivate a transition from the current cloud architectures to the Fog (Chapter 1), and the necessary architectural components to support such a transition (Chapters 2-6). The rest of the book (Chapters 7-xxx) are dedicated to reviewing the various 5G and IoT applications that will benefit from Fog networking. This volume is edited by pioneers in Fog and includes contributions by active researchers in the field. Covers fog technologies and describes the interaction between fog and cloud Presents a view of fog and IoT (encompassing ubiquitous computing) that combines the aspects of both industry and academia Discusses the various architectural and design challenges in coordinating the interactions between M2M, D2D and fog technologies "Fog for 5G and IoT" serves as an introduction to the evolving Fog architecture, compiling work from different areas that collectively form this paradigm

This book presents Internet transport economics as a new approach to understanding the packet-switching paradigm of Internet infrastructure as a global transport system for data packets. It is a prescient view of the Internet’s evolution into a content-centric service platform where the quality of services (QoS) cannot be guaranteed due to the tens of thousands of autonomous systems that enact business decisions on peering, routing, and pricing in a way that determines aspects of the Internet ecosystem like network topology, latency and throughput of traffic flows, and performance of network applications. The trafficking issues created in this environment are a critical concern and barrier for user applications that require real-time responses, such as telesurgery and teleoperation of autonomous vehicles, and the book presents the Internet transport economics model as the solution. While engineering and business are the prevailing lenses through which the Internet is viewed, the book builds its methodological framework around transport. Further delving into economics, it establishes how the Internet can be understood as providing transport services for data packets, whose demand and supply are driven by the QoS metrics of delay and loss, which can be regarded as congestion costs that result in equilibrium rates of traffic flows sent by content providers (CPs). The book goes on to present a stylized model of content provider-to-access provider (CP–AP) service as well as congestion equilibrium and rate equilibrium solution concepts under the Internet transport economics framework. These are used to analyze the problem domains of service differentiation, market structure, and data pricing. Finally, it discusses various potential future applications. This book will be of interest to graduate students and researchers in areas of computer networking and performance evaluation.

A comprehensive guide to Fog and Edge applications, architectures, and technologies Recent years have seen the explosive growth of the Internet of Things (IoT): the internet-connected network of devices that includes everything from personal electronics and home appliances to automobiles and industrial machinery. Responding to the ever-increasing bandwidth demands of the IoT, Fog and Edge computing concepts have developed to collect, analyze, and process data more efficiently than traditional cloud architecture. Fog and Edge Computing: Principles and Paradigms provides a comprehensive overview of the state-of-the-art applications and architectures driving this dynamic field of computing while highlighting potential research directions and emerging technologies. Exploring topics such as developing scalable architectures, moving from closed systems to open systems, and ethical issues rising from data sensing, this timely book addresses both the challenges and opportunities that Fog and Edge computing presents. Contributions from leading IoT experts discuss federating Edge resources, middleware design issues, data management and predictive analysis, smart transportation and surveillance applications, and more. A coordinated and integrated presentation of topics helps readers gain thorough knowledge of the foundations, applications, and issues that are central to Fog and Edge computing. This valuable resource: Provides insights on transitioning from current Cloud-centric and 4G/5G wireless environments to Fog Computing Examines methods to optimize virtualized, pooled, and shared resources Identifies potential technical challenges and offers suggestions for possible solutions Discusses major components of Fog and Edge computing architectures such as middleware, interaction protocols, and autonomic management Includes access to a website portal for advanced online resources Fog and Edge Computing: Principles and Paradigms is an essential source of up-to-date information for systems architects, developers, researchers, and advanced undergraduate and graduate students in fields of computer science and engineering.

Smart Data: State-of-the-Art Perspectives in Computing and Applications explores smart data computing techniques to provide intelligent decision making and prediction services support for business, science, and engineering. It also examines the latest research trends in fields related to smart data computing and applications, including new computing theories, data mining and machine learning techniques. The book features contributions from leading experts and covers cutting-edge topics such as smart data and cloud computing, AI for networking, smart data deep learning, Big Data capture and representation, AI for Big Data applications, and more. Features Presents state-of-the-art research in big data and smart computing Provides a broad coverage of topics in data science and machine learning Combines computing methods with domain knowledge and a focus on applications in science, engineering, and business Covers data security and privacy, including AI techniques Includes contributions from leading researchers

Bachelor Thesis from the year 2021 in the subject Business economics - Miscellaneous, grade: 1,0, Pforzheim University, language: English, abstract: Big data is getting larger, the pressure in the market to use the existing data is getting stronger and therefore also the number of companies that address the topic of data science increases. This dissertation focuses on identifying big or smart data science trends in marketing and sales within the consumer-packaged goods industry. The objective of this research is to address actual opportunities around data science for the selected focus area. The following research project analyzes those opportunities and identifies nine data science trends. Via in-depth interviews, the expert’s experiences and difficulties with data science are questioned, emotions that arise through the interaction with this science are recognized, and potentials for improvements are discussed. Subsequently, central meaningful quotations are analyzed with Mayring’s qualitative content analysis, reformulated into condensed codes, and summarized through eighteen overarching categories. The general findings of this analysis include the necessity of smart data insights within this low margin industry, the dependence on consultancy support due to knowledge gaps, expandable engagement in the B2B environment, the promotion of data-thinking and acting, the merge of sales and marketing for data science knowledge generations, and the extension of data science knowledge to maintain competitive advantage within the market for the long run. The improvement proposals consist mainly of automated data cleaning, intelligent algorithms, data handling knowledge development, data democracy, and knowledge combinations in form of project dependent focus teams to broaden data science applications within the industry.