This book provides a comprehensive and systematic exploration of next-generation Edge Intelligence (EI) Networks. It delves deep into the critical design considerations within this context, emphasizing the necessity for functional and dependable interactions between networking strategies and the diverse application scenarios. This should help assist to encompass a wide range of environments. This book also discusses topics such as resource optimization, incentive mechanisms, channel prediction and cutting-edge technologies, which includes digital twins and advanced machine learning techniques. It underscores the importance of functional integration to facilitate meaningful collaborations between networks and systems, while operating across heterogeneous environments aiming support novel and disruptive human-oriented services and applications. Valuable insights into the stringent requirements for intelligence capabilities, communication latency and real-time response are discussed. This characterizes the new EI era, driving the creation of comprehensive cross-domain architectural ecosystems that infuse human-like intelligence into every aspect of emerging EI systems. This book primarily targets advanced-level students as well as postdoctoral researchers, who are new to this field and are searching for a comprehensive understanding of emerging EI systems. Practitioners seeking guidance in the development and implementation of EI systems in practical contexts will also benefit from this book.

Applying Computational Intelligence for Social Good: Track, Understand and Build a Better World, Volume 132 presents views on how Computational Intelligent and ICT technologies can be applied to ease or solve social problems by sharing examples of research results from studies of social anxiety, environmental issues, mobility of the disabled, and problems in social safety. Sample chapters in this release include Why is implementing Computational Intelligence for social good so challenging? Principles and its Application, Smart crisis management system for road accidents using Geo-Spacial Machine Learning Techniques, Residential Energy Management System (REMS) Using Machine Learning, Text-Based Personality Prediction using XLNet, and much more. - Explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation for solving socially relevant problems - Focuses on Forecasting applications, Human Behavior and Critics response analysis in social forums, Healthcare monitoring Systems, Disaster Management, Industrial management, and most recently, Epidemics and Outbreaks - Brings together many different aspects of the current research on intelligence technologies, such as neural networks, support vector machines, fuzzy logic, and evolutionary computation

A practical guide to the design, implementation, evaluation, and deployment of emerging technologies for intelligent IoT applications With the rapid development in artificially intelligent and hybrid technologies, IoT, edge, fog-driven, and pervasive computing techniques are becoming important parts of our daily lives. This book focuses on recent advances, roles, and benefits of these technologies, describing the latest intelligent systems from a practical point of view. Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications is also valuable for engineers and professionals trying to solve practical, economic, or technical problems. With a uniquely practical approach spanning multiple fields of interest, contributors cover theory, applications, and design methodologies for intelligent systems. These technologies are rapidly transforming engineering, industry, and agriculture by enabling real-time processing of data via computational, resource-oriented metaheuristics and machine learning algorithms. As edge/fog computing and associated technologies are implemented far and wide, we are now able to solve previously intractable problems. With chapters contributed by experts in the field, this book: Describes Machine Learning frameworks and algorithms for edge, fog, and pervasive computing Considers probabilistic storage systems and proven optimization techniques for intelligent IoT Covers 5G edge network slicing and virtual network systems that utilize new networking capacity Explores resource provisioning and bandwidth allocation for edge, fog, and pervasive mobile applications Presents emerging applications of intelligent IoT, including smart farming, factory automation, marketing automation, medical diagnosis, and more Researchers, graduate students, and practitioners working in the intelligent systems domain will appreciate this book’s practical orientation and comprehensive coverage. Intelligent IoT is revolutionizing every industry and field today, and Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications provides the background, orientation, and inspiration needed to begin.

The rise of the Internet of Things (IoT) and Artificial Intelligence (AI) leads to the emergence of edge computing systems that push the training and deployment of AI models to the edge of networks for reduced bandwidth cost, improved responsiveness, and better privacy protection, allowing for the ubiquitous AI that can happen anywhere and anytime. Motivated by the above trend, this book introduces a new computing paradigm, the Social Edge Computing (SEC), that empowers human-centric edge intelligent applications by revolutionizing the computing, intelligence, and the training of the AI models at the edge. The SEC paradigm introduces a set of critical human-centric challenges such as the rational nature of edge device owners, pronounced heterogeneity of the edge devices, real-time AI at the edge, human and AI interaction, and the privacy of the edge users. The book addresses these challenges by presenting a series of principled models and systems that enable the confluence of the computing capabilities of devices and the domain knowledge of the people, while explicitly addressing the unique concerns and constraints from humans. Compared to existing books in the field of edge computing, the vision of this book is unique: we focus on the social edge computing (SEC), an emerging paradigm at the intersection of edge computing, AI, and social computing. This book discusses the unique vision, challenges and applications in SEC. To our knowledge, keeping humans in the loop of edge intelligence has not been systematically reviewed and studied in an existing book. The SEC vision generalizes the current machine-to-machine interactions in edge computing (e.g., mobile edge computing literature), and machine-to-AI interactions (e.g., edge intelligence literature) into a holistic human-machine-AI ecosystem.

This book discusses fundamental and high-level concepts relating to intelligent computing and communications in the context of distributed computing, big data, high performance computing and the Internet of Things. It is becoming increasingly important to develop adaptive, intelligent, computing-centric, energy-aware, secure and privacy-aware mechanisms in high-performance computing and IoT applications. Serving as a useful guide for researchers and practitioners working in the field of information technology and computer science, the book also appeals to beginners wanting to learn more about the better computing paradigm. In addition, it provides a platform for researchers, engineers, academics and industry professionals from around the globe to share their research findings.

Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering. Provides a reference handbook on the evolution of distributed systems, including Cloud, Fog and Edge Computing Integrates the various Artificial Intelligence and Machine Learning techniques for effective predictions at Edge rather than Cloud or remote Data Centers Provides insight into the features and constraints in Edge Computing and storage, including hardware constraints and the technological/architectural developments that shall overcome those constraints

This book presents the proceedings of the Computing Conference 2019, providing a comprehensive collection of chapters focusing on core areas of computing and their real-world applications. Computing is an extremely broad discipline, encompassing a range of specialized fields, each focusing on particular areas of technology and types of application, and the conference offered pioneering researchers, scientists, industrial engineers, and students from around the globe a platform to share new ideas and development experiences. Providing state-of-the-art intelligent methods and techniques for solving real- world problems, the book inspires further research and technological advances in this important area.

This book highlights the emerging field of intelligent computing and developing smart systems. It includes chapters discussing the outcome of challenging research related to distributed computing, smart machines and their security related research, and also covers next-generation communication techniques and the networking technologies that have the potential to build the future communication infrastructure. Bringing together computing, communications and other aspects of intelligent and smart computing, it contributes to developing a roadmap for future research on intelligent systems.

This book is part I of a two-volume work that contains the refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2010 and the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010, held in Wuxi, China, in September 2010. The 194 revised full papers presented were carefully reviewed and selected from over 880 submissions and recommended for publication by Springer in two volumes of Lecture Notes in Computer Science (LNCS) and one volume of Lecture Notes in Bioinformatics (LNBI). This particular volume of Lecture Notes in Computer Science (LNCS) includes 55 papers covering 7 relevant topics. The 55 papers in this volume are organized in topical sections on intelligent modeling, monitoring, and control of complex nonlinear systems; autonomy-oriented computing and intelligent agents; advanced theory and methodology in fuzzy systems and soft computing; computational intelligence in utilization of clean and renewable energy resources; intelligent modeling, control and supervision for energy saving and pollution reduction; intelligent methods in developing vehicles, engines and equipments; computational methods and intelligence in modeling genetic and biochemical networks and regulation.

Edge Intelligence in Cyber-Physical Systems: Foundations and Applications provides a comprehensive overview of best practices for building edge intelligence into cyber-physical systems. This book covers the foundations and applications of synergizing machine learning at the edge of CPS, leveraging an edge computing infrastructure. Divided into four parts, the first section of the book reviews the foundations, principles, and representative application domains of CPS. The second part covers machine learning, edge computing, and their needs in CPS, defining edge intelligence and its principles, challenges, and research directions. The third part presents tutorials and foundational research works on realizing edge intelligence in representative CPS. The fourth part explores the problem space of threats and countermeasures in building edge intelligence into CPS. Researchers, graduate students and professionals in computer science, data science, and electrical engineering will find this to be a valuable resource on the principles and applications of edge intelligence in cyber-physical systems as well as the development of interdisciplinary techniques to advance the field. - Provides comprehensive overview of CPS design challenges and representative application domains - Introduces the principles of machine learning and edge computing and their applications in cyber-physical systems - Explores edge intelligence applications as well as complete case studies of problem formalization, method/algorithm design, and performance evaluation