This book highlights original approaches of modelling and intelligent control of cyber-physical systems covering both theoretical and practical aspects. The novel contribution of the book covers the transformation of scientific research and their results into applications for cyber-physical systems design and operation during the whole life cycle in different domains. Given its scope, the book offers an excellent reference book for researchers and other readers in the fields of cyber-physical systems modelling and intelligent control, space exploration and practical implementation of cyber-physical systems. The book also benefits researchers and practitioners in artificial intelligence and machine learning, as described results can be applied in cyber-physical systems design and cost-effectively maintenance. The target audience of this book also includes practitioners and experts, as well as state authorities and representatives of international organizations interested in creating mechanisms for implementing Cyber-Physical Systems projects. .

Intelligent Techniques for Cyber-Physical Systems covers challenges, opportunities, and open research directions for cyber-physical systems (CPS). It focuses on the design and development of machine learning and metaheuristics-enabled methods as well as blockchain for various challenges like security, resource management, computation offloading, trust management, and others in edge, fog, and cloud computing, Internet of Things (IoT), Internet of Everything (IoE), and smart cities. It also includes the design and analysis of deep learning-based models, sensing technologies, metaheuristics, and blockchain for complex real-life systems for CPS. Offers perspectives on the research directions in CPS; Provides state-of-the-art reviews on intelligent techniques, machine learning, deep learning, and reinforcement learning-based models for cloud-enabled IoT environment; Discusses intelligent techniques for complex real-life problems in different CPS scenarios; Reviews advancements in blockchain technology and smart cities; Explores machine learning-based intelligent models for combinatorial optimization problems. The book is aimed at researchers and graduate students in computer science, engineering, and electrical and electronics engineering.

"This book focuses upon the recent advances in the realization of Artificial Intelligence-based approaches towards affecting secure Cyber-Physical Systems. It features contributions pertaining to this multidisciplinary paradigm, in particular, in its application to building sustainable space by investigating state-of-art research issues, applications and achievements in the field of Computational Intelligence Paradigms for Cyber-Physical Systems"--

Big data generates around us constantly from daily business, custom use, engineering, and science activities. Sensory data is collected from the internet of things (IoT) and cyber-physical systems (CPS). Merely storing such a massive amount of data is meaningless, as the key point is to identify, locate, and extract valuable knowledge from big data to forecast and support services. Such extracted valuable knowledge is usually referred to as smart data. It is vital to providing suitable decisions in business, science, and engineering applications. Deep Learning Applications for Cyber-Physical Systems provides researchers a platform to present state-of-the-art innovations, research, and designs while implementing methodological and algorithmic solutions to data processing problems and designing and analyzing evolving trends in health informatics and computer-aided diagnosis in deep learning techniques in context with cyber physical systems. Covering topics such as smart medical systems, intrusion detection systems, and predictive analytics, this text is essential for computer scientists, engineers, practitioners, researchers, students, and academicians, especially those interested in the areas of internet of things, machine learning, deep learning, and cyber-physical systems.

Smart Cyber Physical Systems: Advances, Challenges and Opportunities ISBN: 9780367337889 Cyber Physical Systems (CPS) are the new generation of collaborative computational entities, with a prime focus on integration of the physical world and cyber space. Through a feedback mechanism, the system adapts itself to new conditions in real time. The scope of this book includes research experience by experts in CPS infrastructure systems, incorporating sustainability by embedding computing and communication in day-to-day applications. CPS, integrated with Blockchain, Artificial Intelligence, Internet of Things, Big Data, Cloud Computing and Communication, lay a foundation for the fourth industrial revolution, Industry 4.0. This book will be of immense use to practitioners in industries with a focus on autonomous and adaptive configuration, and on optimization, leading to increased agility, elasticity and cost effectiveness. The contributors of this book include renowned academics, industry practitioners and researchers. It offers a rigorous introduction to the theoretical foundations, techniques and practical solutions, through case studies. Building CPS with effective communication, control, intelligence and security is discussed in terms of societal and research perspectives. The objective of this book is to provide a forum for researchers and practitioners to exchange ideas and to achieve progress in CPS by highlighting applications, advances and research challenges. It is highly recommended to be used as a reference book for graduate and post-graduate level programmes in universities, with a focus on research in computer science-related courses.

Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields of reinforcement learning (RL) and cyber-physical systems (CPSs). Rooted in behavioral psychology, RL is one of the primary strands of machine learning. Different from other machine learning algorithms, such as supervised learning and unsupervised learning, the key feature of RL is its unique learning paradigm, i.e., trial-and-error. Combined with the deep neural networks, deep RL become so powerful that many complicated systems can be automatically managed by AI agents at a superhuman level. On the other hand, CPSs are envisioned to revolutionize our society in the near future. Such examples include the emerging smart buildings, intelligent transportation, and electric grids. However, the conventional hand-programming controller in CPSs could neither handle the increasing complexity of the system, nor automatically adapt itself to new situations that it has never encountered before. The problem of how to apply the existing deep RL algorithms, or develop new RL algorithms to enable the real-time adaptive CPSs, remains open. This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms, each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness of RL techniques. Features Introduces reinforcement learning, including advanced topics in RL Applies reinforcement learning to cyber-physical systems and cybersecurity Contains state-of-the-art examples and exercises in each chapter Provides two cybersecurity case studies Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies is an ideal text for graduate students or junior/senior undergraduates in the fields of science, engineering, computer science, or applied mathematics. It would also prove useful to researchers and engineers interested in cybersecurity, RL, and CPS. The only background knowledge required to appreciate the book is a basic knowledge of calculus and probability theory.

Offers a one-stop reference on the application of advanced modeling and simulation (M&S) in cyber physical systems (CPS) engineering This book provides the state-of-the-art in methods and technologies that aim to elaborate on the modeling and simulation support to cyber physical systems (CPS) engineering across many sectors such as healthcare, smart grid, or smart home. It presents a compilation of simulation-based methods, technologies, and approaches that encourage the reader to incorporate simulation technologies in their CPS engineering endeavors, supporting management of complexity challenges in such endeavors. Complexity Challenges in Cyber Physical Systems: Using Modeling and Simulation (M&S) to Support Intelligence, Adaptation and Autonomy is laid out in four sections. The first section provides an overview of complexities associated with the application of M&S to CPS Engineering. It discusses M&S in the context of autonomous systems involvement within the North Atlantic Treaty Organization (NATO). The second section provides a more detailed description of the challenges in applying modeling to the operation, risk and design of holistic CPS. The third section delves in details of simulation support to CPS engineering followed by the engineering practices to incorporate the cyber element to build resilient CPS sociotechnical systems. Finally, the fourth section presents a research agenda for handling complexity in application of M&S for CPS engineering. In addition, this text: Introduces a unifying framework for hierarchical co-simulations of cyber physical systems (CPS) Provides understanding of the cycle of macro-level behavior dynamically arising from spaciotemporal interactions between parts at the micro-level Describes a simulation platform for characterizing resilience of CPS Complexity Challenges in Cyber Physical Systems has been written for researchers, practitioners, lecturers, and graduate students in computer engineering who want to learn all about M&S support to addressing complexity in CPS and its applications in today’s and tomorrow’s world.