This book offers the latest research results in security and privacy for Intelligent Edge Computing Systems. It presents state-of-the art content and provides an in-depth overview of the basic background in this related field. Practical areas in both security and risk analysis are addressed as well as connections directly linked to Edge Computing paradigms. This book also offers an excellent foundation on the fundamental concepts and principles of security, privacy and risk analysis in Edge Computation infrastructures. It guides the reader through the core ideas with relevant ease. Edge Computing has burst onto the computational scene offering key technologies for allowing more flexibility at the edge of networks. As Edge Computing has evolved as well as the need for more in-depth solutions in security, privacy and risk analysis at the edge. This book includes various case studies and applications on Edge Computing. It includes the Internet of Things related areas, such as smart cities, blockchain, mobile networks, federated learning, cryptography and cybersecurity. This book is one of the first reference books covering security and risk analysis in Edge Computing Systems. Researchers and advanced-level students studying or working in Edge Computing and related security fields will find this book useful as a reference. Decision makers, managers and professionals working within these fields will want to purchase this book as well.

This edited book is a compilation of scholarly articles on the latest developments in the field of AI, Blockchain, and ML/DL in cloud security. This book is designed for security and risk assessment professionals, and to help undergraduate, postgraduate students, research scholars, academicians, and technology professionals who are interested in learning practical approaches to cloud security. It covers practical strategies for assessing the security and privacy of cloud infrastructure and applications and shows how to make cloud infrastructure secure to combat threats and attacks, and prevent data breaches. The chapters are designed with a granular framework, starting with the security concepts, followed by hands-on assessment techniques based on real-world studies. Readers will gain detailed information on cloud computing security that—until now—has been difficult to access. This book: • Covers topics such as AI, Blockchain, and ML/DL in cloud security. • Presents several case studies revealing how threat actors abuse and exploit cloud environments to spread threats. • Explains the privacy aspects you need to consider in the cloud, including how they compare with aspects considered in traditional computing models. • Examines security delivered as a service—a different facet of cloud security.

The effectiveness of federated learning in high‐performance information systems and informatics‐based solutions for addressing current information support requirements is demonstrated in this book. To address heterogeneity challenges in Internet of Things (IoT) contexts, Federated Learning for Smart Communication using IoT Application analyses the development of personalized federated learning algorithms capable of mitigating the detrimental consequences of heterogeneity in several dimensions. It includes case studies of IoT‐based human activity recognition to show the efficacy of personalized federated learning for intelligent IoT applications. Features: • Demonstrates how federated learning offers a novel approach to building personalized models from data without invading users’ privacy. • Describes how federated learning may assist in understanding and learning from user behavior in IoT applications while safeguarding user privacy. • Presents a detailed analysis of current research on federated learning, providing the reader with a broad understanding of the area. • Analyses the need for a personalized federated learning framework in cloud‐edge and wireless‐edge architecture for intelligent IoT applications. • Comprises real‐life case illustrations and examples to help consolidate understanding of topics presented in each chapter. This book is recommended for anyone interested in federated learning‐based intelligent algorithms for smart communications.

This book provides a practical guide to federated deep learning for healthcare including fundamental concepts, framework, and the applications comprising domain adaptation, model distillation, and transfer learning. It covers concerns in model fairness, data bias, regulatory compliance, and ethical dilemmas. It investigates several privacy-preserving methods such as homomorphic encryption, secure multi-party computation, and differential privacy. It will enable readers to build and implement federated learning systems that safeguard private medical information. Features: Offers a thorough introduction of federated deep learning methods designed exclusively for medical applications. Investigates privacy-preserving methods with emphasis on data security and privacy. Discusses healthcare scaling and resource efficiency considerations. Examines methods for sharing information among various healthcare organizations while retaining model performance. This book is aimed at graduate students and researchers in federated learning, data science, AI/machine learning, and healthcare.

Cybersecurity is a paramount concern in both Internet of Things (IoT) and Cyber-Physical Systems (CPSs) due to the interconnected and often critical nature of these systems. The integration of AI/ML into the realm of IoT and CPS security has gained significant attention and momentum in recent years. The success of AI/ML in various domains has sparked interest in leveraging these technologies to enhance the security, resilience, and adaptability of IoT and CPS. Secure and Smart Cyber-Physical Systems provides an extensive exploration of AI/ML-based security applications in the context of IoT and CPS. Features Presents cutting-edge topics and research in IoT and CPS Includes contributions from leading worldwide researchers Focuses on CPS architectures for secure and smart environments Explores AI/ML and blockchain approaches for providing security and privacy to CPS including smart grids, smart cities, and smart healthcare Provides comprehensive guidance into the intricate world of software development for medical devices Covers a blueprint for the emergence of 6G communications technology in Industry 5.0 and federated-learning-based secure financial services This book covers state-of-the-art problems, existing solutions, and potential research directions for CPS researchers, scholars, and professionals in both industry and academia.

This book explains the best practices and their respective outcomes in artificial intelligence (AI) to meet sustainable development goals and demands. It examines the practices, technologies, and innovations at the core of various research issues to meet the sustainable development demands in agriculture to balance social, economic, and environmental sustainability with AI. AI in Agriculture for Sustainable and Economic Management discusses AI-driven nanotechnology approaches for precision agriculture and solutions for the optimization of farming resources and their management. The authors examine the impact of AI in agriculture and how technology-driven sustainable farming with smart waste-water treatment for zero waste for the circular economy can extend crop shelf-life. It discusses how AI expertise can be advantageous to envisage and evaluate the increasing demands of productivity, and to help to maintain ecosystems and strengthen the capacity for crop adaptation in response to drastic changes in climate and weather, natural disasters, and other significant factors. These findings and practices are also useful to emphasize how an agricultural ecosystem can be advanced and industrialized so that it can aid not only large commercial farms but also smaller farmlands. Finally, it also discusses how AI practices will help to find a balance between the volume of food manufactured and the proper maintenance of the ecosystem. This book is intended for researchers and upper graduate students interested in artificial intelligence in agricultural engineering, AI advances in crop science and technology for sustainable development.

The Internet of Things offers massive societal and economic opportunities while at the same time significant challenges, not least the delivery and management of the technical infrastructure underpinning it, the deluge of data generated from it, ensuring privacy and security, and capturing value from it. This Open Access Pivot explores these challenges, presenting the state of the art and future directions for research but also frameworks for making sense of this complex area. This book provides a variety of perspectives on how technology innovations such as fog, edge and dew computing, 5G networks, and distributed intelligence are making us rethink conventional cloud computing to support the Internet of Things. Much of this book focuses on technical aspects of the Internet of Things, however, clear methodologies for mapping the business value of the Internet of Things are still missing. We provide a value mapping framework for the Internet of Things to address this gap. While there is much hype about theInternet of Things, we have yet to reach the tipping point. As such, this book provides a timely entrée for higher education educators, researchers and students, industry and policy makers on the technologies that promise to reshape how society interacts and operates.