This book provides a comprehensive introduction to applying compressive sensing to improve data quality in the context of mobile crowdsensing. It addresses the following main topics: recovering missing data, efficiently collecting data, preserving user privacy, and detecting false data. Mobile crowdsensing, as an emerging sensing paradigm, enables the masses to take part in data collection tasks with the aid of powerful mobile devices. However, mobile crowdsensing platforms have yet to be widely adopted in practice, the major concern being the quality of the data collected. There are numerous causes: some locations may generate redundant data, while others may not be covered at all, since the participants are rarely systematically coordinated; privacy is a concern for some people, who don't wish to share their real-time locations, and therefore some key information may be missing; further, some participants may upload fake data in order to fraudulently gain rewards. To address these problematic aspects, compressive sensing, which works by accurately recovering a sparse signal using very few samples, has proven to offer an effective solution.

This book aims to provide the latest research developments and results in the domain of AI techniques for smart cyber ecosystems. It presents a holistic insight into AI-enabled theoretic approaches and methodology in IoT networking, security analytics using AI tools and network automation, which ultimately enable intelligent cyber space. This book will be a valuable resource for students, researchers, engineers and policy makers working in various areas related to cybersecurity and privacy for Smart Cities. This book includes chapters titled "An Overview of the Artificial Intelligence Evolution and Its Fundamental Concepts, and Their Relationship with IoT Security", "Smart City: Evolution and Fundamental Concepts", "Advances in AI-Based Security for Internet of Things in Wireless Virtualization Environment", "A Conceptual Model for Optimal Resource Sharing of Networked Microgrids Focusing Uncertainty: Paving Path to Eco-friendly Smart Cities", "A Novel Framework for a Cyber Secure Smart City", "Contemplating Security Challenges and Threats for Smart Cities", "Self-Monitoring Obfuscated IoT Network", "Introduction to Side Channel Attacks and Investigation of Power Analysis and Fault Injection Attack Techniques", "Collaborative Digital Forensic Investigations Model for Law Enforcement: Oman as a Case Study", "Understanding Security Requirements and Challenges in the Industrial Internet of Things: A Review", "5G Security and the Internet of Things", "The Problem of Deepfake Videos and How to Counteract Them in Smart Cities", "The Rise of Ransomware Aided by Vulnerable IoT Devices", "Security Issues in Self-Driving Cars within Smart Cities", and "Trust-Aware Crowd Associated Network-Based Approach for Optimal Waste Management in Smart Cities". This book provides state-of-the-art research results and discusses current issues, challenges, solutions and recent trends related to security and organization within IoT and Smart Cities. We expect this book to be of significant importance not only to researchers and practitioners in academia, government agencies and industries, but also for policy makers and system managers. We anticipate this book to be a valuable resource for all those working in this new and exciting area, and a "must have" for all university libraries.

This book aims to provide the latest research developments and results in the domain of AI techniques for smart cyber ecosystems. It presents a holistic insight into AI-enabled theoretic approaches and methodology in IoT networking, security analytics using AI tools and network automation, which ultimately enable intelligent cyber space. This book will be a valuable resource for students, researchers, engineers and policy makers working in various areas related to cybersecurity and privacy for Smart Cities. This book includes chapters titled "An Overview of the Artificial Intelligence Evolution and Its Fundamental Concepts, and Their Relationship with IoT Security", "Smart City: Evolution and Fundamental Concepts", "Advances in AI-Based Security for Internet of Things in Wireless Virtualization Environment", "A Conceptual Model for Optimal Resource Sharing of Networked Microgrids Focusing Uncertainty: Paving Path to Eco-friendly Smart Cities", "A Novel Framework for a Cyber Secure Smart City", "Contemplating Security Challenges and Threats for Smart Cities", "Self-Monitoring Obfuscated IoT Network", "Introduction to Side Channel Attacks and Investigation of Power Analysis and Fault Injection Attack Techniques", "Collaborative Digital Forensic Investigations Model for Law Enforcement: Oman as a Case Study", "Understanding Security Requirements and Challenges in the Industrial Internet of Things: A Review", "5G Security and the Internet of Things", "The Problem of Deepfake Videos and How to Counteract Them in Smart Cities", "The Rise of Ransomware Aided by Vulnerable IoT Devices", "Security Issues in Self-Driving Cars within Smart Cities", and "Trust-Aware Crowd Associated Network-Based Approach for Optimal Waste Management in Smart Cities". This book provides state-of-the-art research results and discusses current issues, challenges, solutions and recent trends related to security and organization within IoT and Smart Cities. We expect this book to be of significant importance not only to researchers and practitioners in academia, government agencies and industries, but also for policy makers and system managers. We anticipate this book to be a valuable resource for all those working in this new and exciting area, and a "must have" for all university libraries.

The three-volume sets constitute the refereed proceedings of the 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022, held in Singapore, during August 6-8, 2022. The 169 full papers presented in these proceedings were carefully reviewed and selected from 498 submissions. The papers are organized in the following topical sections: Volume I:Knowledge Science with Learning and AI (KSLA) Volume II:Knowledge Engineering Research and Applications (KERA) Volume III:Knowledge Management with Optimization and Security (KMOS)

The three-volume sets constitute the refereed proceedings of the 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022, held in Singapore, during August 6-8, 2022. The 169 full papers presented in these proceedings were carefully reviewed and selected from 498 submissions. The papers are organized in the following topical sections: Volume I:Knowledge Science with Learning and AI (KSLA) Volume II:Knowledge Engineering Research and Applications (KERA) Volume III:Knowledge Management with Optimization and Security (KMOS)

The three-volume sets constitute the refereed proceedings of the 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022, held in Singapore, during August 6-8, 2022. The 169 full papers presented in these proceedings were carefully reviewed and selected from 498 submissions. The papers are organized in the following topical sections: Volume I: Knowledge Science with Learning and AI (KSLA) Volume II: Knowledge Engineering Research and Applications (KERA) Volume III: Knowledge Management with Optimization and Security (KMOS)

This book provides a comprehensive introduction to applying compressive sensing to improve data quality in the context of mobile crowdsensing. It addresses the following main topics: recovering missing data, efficiently collecting data, preserving user privacy, and detecting false data. Mobile crowdsensing, as an emerging sensing paradigm, enables the masses to take part in data collection tasks with the aid of powerful mobile devices. However, mobile crowdsensing platforms have yet to be widely adopted in practice, the major concern being the quality of the data collected. There are numerous causes: some locations may generate redundant data, while others may not be covered at all, since the participants are rarely systematically coordinated; privacy is a concern for some people, who don't wish to share their real-time locations, and therefore some key information may be missing; further, some participants may upload fake data in order to fraudulently gain rewards. To address these problematic aspects, compressive sensing, which works by accurately recovering a sparse signal using very few samples, has proven to offer an effective solution.