"Optimal Observation for Cyber-physical Systems" addresses the challenge, fundamental to the design of wireless sensor networks (WSNs), presented by the obligatory trade-off between precise estimates and system constraints. A unified theoretical framework, based on the well-established theory of optimal experimental design and providing consistent solutions to problems hitherto requiring a variety of approaches, is put forward to solve a large class of optimal observation problems. The Fisher information matrix plays a key role in this framework and makes it feasible to provide analytical solutions to some complex and important questions which could not be answered in the past. Readers with an applied background in WSN implementation will find all the understanding of the key theory of optimal experimental design they need within this book. The use of multiple examples to illustrate the theoretical parts of the book brings the subject into sharper focus than would an abstract theoretical disquisition.

A successful cyber-physical system, a complex interweaving of hardware and software with some part of the physical environment, depends on proper identification of the, often pre-existing, physical element. A bespoke “cyber” part of the system may then be designed from scratch. Optimal Mobile Sensing and Actuation Strategies in Cyber-physical Systems focuses on distributed-parameter systems the dynamics of which can be modelled with partial differential equations. These are very challenging to observe, their states and inputs being distributed throughout a spatial domain. Consequently, systematic approaches to the optimization of sensor location have to be devised for parameter estimation. The text begins by reviewing the field of cyber-physical systems and introducing background notions of distributed parameter systems and optimal observation theory. New research problems are then defined within this framework. Two important problems considered are optimal mobile sensor trajectory planning and the accuracy effects and allocation of remote sensors. These are followed up with a solution to the problem of optimal robust estimation. Actuation policies are then introduced into the framework with the purpose of improving estimation and optimizing the trajectories of both sensors and actuators simultaneously. The large number of illustrations within the text will assist the reader to visualize the application of the methods proposed. A group of similar examples are used throughout the book to help the reader assimilate the material more easily. The monograph concentrates on the use of methods for which a cyber-physical-systems infrastructure is required. The methods are computationally heavy and require mobile sensors and actuators with communications abilities. Application examples cover fields from environmental science to national security so that readers are encouraged to link the ideas of cyber-physical systems with their own research.

Although comprehensive knowledge of cyber-physical systems (CPS) is becoming a must for researchers, practitioners, system designers, policy makers, system managers, and administrators, there has been a need for a comprehensive and up-to-date source of research and information on cyber-physical systems. This book fills that need.Cyber-Physical Syst

This book is a compilation of research accomplishments in the fields of modeling, simulation, and their applications, as presented at AsiaSim 2011 (Asia Simulation Conference 2011). The conference, held in Seoul, Korea, November 16–18, was organized by ASIASIM (Federation of Asian Simulation Societies), KSS (Korea Society for Simulation), CASS (Chinese Association for System Simulation), and JSST (Japan Society for Simulation Technology). AsiaSim 2011 provided a forum for scientists, academicians, and professionals from the Asia-Pacific region and other parts of the world to share their latest exciting research findings in modeling and simulation methodologies, techniques, and their tools and applications in military, communication network, industry, and general engineering problems.

In Industry 4.0, industrial productions are adjusted to complete smart automation, which means introducing self-automation methods, self-configuration, self-diagnosis of problems and removal, cognition, and intelligent decision making. This implementation of Industry 4.0 brings about a change in business paradigms and production models, and this will be reflected at all levels of the production process including supply chains and will involve all workers in the production process from managers to cyber-physical systems designers and customers as end-users. The Handbook of Research on Integrating Industry 4.0 in Business and Manufacturing is an essential reference source that explores the development and integration of Industry 4.0 by examining changes and innovations to manufacturing processes as well as its applications in different industrial areas. Featuring coverage on a wide range of topics such as cyber physical systems, integration criteria, and artificial intelligence, this book is ideally designed for mechanical engineers, electrical engineers, manufacturers, supply chain managers, logistics specialists, investors, managers, policymakers, production scientists, researchers, academicians, and students at the postgraduate level.

This book gathers a selection of peer-reviewed papers presented at the first Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2019) conference, held in Shengyang, China, on 28–29 December 2019. The contributions, prepared by an international team of scientists and engineers, cover the latest advances made in the field of machine learning, and big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems.

This book provides comprehensive coverage of various solutions that address issues related to real-time performance, security, and robustness in emerging automotive platforms. The authors discuss recent advances towards the goal of enabling reliable, secure, and robust, time-critical automotive cyber-physical systems, using advanced optimization and machine learning techniques. The focus is on presenting state-of-the-art solutions to various challenges including real-time data scheduling, secure communication within and outside the vehicle, tolerance to faults, optimizing the use of resource-constrained automotive ECUs, intrusion detection, and developing robust perception and control techniques for increasingly autonomous vehicles.

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.

Applied Cyber-Physical Systems presents the latest methods and technologies in the area of cyber-physical systems including medical and biological applications. Cyber-physical systems (CPS) integrate computing and communication capabilities by monitoring, and controlling the physical systems via embedded hardware and computers. This book brings together unique contributions from renowned experts on cyber-physical systems research and education with applications. It also addresses the major challenges in CPS, and then provides a resolution with various diverse applications as examples. Advanced-level students and researchers focused on computer science, engineering and biomedicine will find this to be a useful secondary text book or reference, as will professionals working in this field.

Cyber-physical systems (CPSs) have quickly become one of the hottest computer applications today. With their tight integration of cyber and physical objects, it is believed CPSs will transform how we interact with the physical world, just like the Internet transformed how we interact with one another. A CPS could be a system at multiple scales, from large smart bridges with fluctuation detection and responding functions, to autonomous cars and tiny implanted medical devices. Cyber-Physical Systems: Integrated Computing and Engineering Design supplies comprehensive coverage of the principles and design of CPSs. It addresses the many challenges that must be overcome and outlines a roadmap of how to get there. Emphasizes the integration of cyber computing and physical objects control Covers important CPS theory foundations and models Includes interesting case studies of several important civilian and health care applications that illustrate the CPS design process Addresses the collaboration of the sensing and controlling of a physical system with robust software architecture Explains how to account for random failure events that can occur in a real CPS environment Presented in a systematic manner, the book begins by discussing the basic concept underlying CPSs and examining some challenging design issues. It then covers the most important design theories and modeling methods for a practical CPS. Next, it moves on to sensor-based CPSs, which use embedded sensors and actuators to interact with the physical world. The text presents concrete CPS designs for popular civilian applications, including building and energy management. Reflecting the importance of human health care in society, it includes CPS examples of rehabilitation applications such as virtual reality-based disability recovery platforms.