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Intelligent Surfaces Empowered 6G Wireless Network / / edited by Qingqing Wu [and four others]
| Intelligent Surfaces Empowered 6G Wireless Network / / edited by Qingqing Wu [and four others] |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024] |
| Descrizione fisica | 1 online resource (365 pages) |
| Disciplina | 621.38456 |
| Soggetto topico |
6G mobile communication systems
Smart materials Surfaces (Technology) |
| ISBN |
1-119-91310-1
1-119-91312-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgement -- Part I Fundamentals of IRS -- Chapter 1 Introduction to Intelligent Surfaces -- 1.1 Background -- 1.2 Concept of Intelligent Surfaces -- 1.3 Advantages of Intelligence Surface -- 1.4 Potential Applications -- 1.5 Conclusion -- Bibliography -- Chapter 2 IRS Architecture and Hardware Design -- 2.1 Metamaterials: Basics of IRS -- 2.2 Programmable Metasurfaces -- 2.3 IRS Hardware Design -- 2.3.1 IRS System Architecture -- 2.3.2 IRS Element Design -- 2.3.3 IRS Array Design -- 2.3.4 IRS Controller Design -- 2.3.5 Full‐Wave Simulation and Field Test -- 2.4 State‐of‐the‐Art IRS Prototype -- 2.4.1 Passive IRS Prototype by Tsinghua -- 2.4.2 Active IRS Prototype by Tsinghua -- 2.4.3 IRS Modulation Prototype by SEU -- 2.4.3.1 Transmitter Design -- 2.4.3.2 Frame Structure Design -- 2.4.3.3 Receiver Design -- 2.4.3.4 System Design -- 2.4.4 Transmissive IRS Prototype by MIT -- 2.4.5 IRS Prototype by China Mobile -- 2.4.6 IRS Prototype by DOCOMO -- Bibliography -- Chapter 3 On Path Loss and Channel Reciprocity of RIS‐Assisted Wireless Communications -- 3.1 Introduction -- 3.2 Path Loss Modeling and Channel Reciprocity Analysis -- 3.2.1 System Description -- 3.2.2 General Path Loss Model -- 3.2.3 Path Loss Models for Typical Scenarios -- 3.2.4 Discussion on RIS Path Loss and Channel Reciprocity -- 3.3 Path Loss Measurement and Channel Reciprocity Validation -- 3.3.1 Two Fabricated RISs -- 3.3.2 Two Measurement Systems -- 3.3.3 Validation of RIS Path Loss Models -- 3.3.4 Validation of RIS Channel Reciprocity -- 3.4 Conclusion -- 3.A Appendix -- 3.A.1 Proof of Theorem 3.1 -- Bibliography -- Chapter 4 Intelligent Surface Communication Design: Main Challenges and Solutions -- 4.1 Introduction -- 4.2 Channel Estimation.
4.2.1 Problem Description and Challenges -- 4.2.2 Semi‐Passive IRS Channel Estimation -- 4.2.3 Fully‐Passive IRS Channel Estimation -- 4.3 Passive Beamforming Optimization -- 4.3.1 IRS‐aided SISO System: Passive Beamforming Basics and Power Scaling Order -- 4.3.2 IRS‐aided MISO System: Joint Active and Passive Beamforming -- 4.3.3 IRS‐Aided MIMO System -- 4.3.4 IRS‐Aided OFDM System -- 4.3.5 Passive Beamforming with Discrete Reflection Amplitude and Phase Shift -- 4.3.6 Other Related Works and Future Directions -- 4.4 IRS Deployment -- 4.4.1 IRS Deployment Optimization at the Link Level -- 4.4.1.1 Optimal Deployment of Single IRS -- 4.4.1.2 Single IRS versus Multiple Cooperative IRSs -- 4.4.1.3 LoS versus Non‐LoS (NLoS) -- 4.4.2 IRS Deployment at the Network Level: Distributed or Centralized? -- 4.4.3 Other Related Work and Future Direction -- 4.5 Conclusion -- Bibliography -- Part II IRS for 6G Wireless Systems -- Chapter 5 Overview of IRS for 6G and Industry Advance -- 5.1 IRS for 6G -- 5.1.1 Potential Use Cases -- 5.1.1.1 Indoor Use Cases -- 5.1.1.2 Outdoor Use Cases -- 5.1.2 Deployment Scenarios -- 5.2 Industrial Progresses -- 5.2.1 Funded Projects -- 5.2.2 White Papers -- 5.2.3 Prototyping and Testing -- 5.2.4 Standardization Progress -- Bibliography -- Chapter 6 RIS‐Aided Massive MIMO Antennas* -- 6.1 Introduction -- 6.1.0 Notation -- 6.2 System Model -- 6.2.1 Channel Model -- 6.2.2 Active Antenna Configuration -- 6.3 Uplink/Downlink Signal Processing -- 6.3.1 Uplink Channel Estimation -- 6.3.2 Downlink Data Transmission -- 6.4 Performance Measures -- 6.4.1 SINR and Spectral Efficiency under Perfect Channel State Information (CSI) -- 6.4.2 SINR and Spectral Efficiency under Imperfect Channel State Information (CSI) -- 6.4.2.1 The Upper‐Bound (UB) to the System Performance -- 6.4.2.2 The Hardening Lower‐Bound (LB) to System Performance. 6.5 Optimization of the RIS Phase Shifts -- 6.6 Numerical Results -- 6.7 Conclusions -- 6.A Appendix -- Bibliography -- Chapter 7 Localization, Sensing, and Their Integration with RISs -- 7.1 Introduction -- 7.1.1 Localization in 5G -- 7.1.2 RIS Key Advantages -- 7.1.2.1 Localization -- 7.1.2.2 Sensing -- 7.2 RIS Types and Channel Modeling -- 7.2.1 RIS Hardware Architectures -- 7.2.2 RIS‐Parameterized Channel Models -- 7.2.2.1 Geometric Channel Model -- 7.2.2.2 Stochastic Channel Modeling -- 7.3 Localization with RISs -- 7.3.1 Fundamentals on Localization -- 7.3.2 Localization with Reflective RISs -- 7.3.3 Localization with a Single STAR‐RIS -- 7.3.4 Localization with Multiple Receiving RISs -- 7.4 Sensing with RISs -- 7.4.1 Link Budget Analysis -- 7.4.1.1 Monostatic Radar Sensing -- 7.4.1.2 Bistatic Radar Sensing -- 7.4.2 Joint Sensing and Localization with a Single RIS -- 7.4.2.0 UE and Landmark Estimates -- 7.5 Conclusion and Open Challenges -- Bibliography -- Chapter 8 IRS‐Aided THz Communications -- 8.1 IRS‐Aided THz MIMO System Model -- 8.2 Beam Training Protocol -- 8.3 IRS Prototyping -- 8.3.1 Active Beam Steering at THz transceivers -- 8.3.2 Passive Beam Steering on THz IRS -- 8.3.3 Codebook Design for Beam Scanning -- 8.3.4 Beam‐Scanning Reflectarray -- 8.4 IRS‐THz Communication Applications -- 8.4.1 High Speed Fronthaul/Backhaul -- 8.4.2 Cellular Connected Drones -- 8.4.3 Wireless Data Center -- 8.4.4 Enhanced Indoor Coverage -- 8.4.5 Vehicular Communications -- 8.4.6 Physical‐Layer Security -- Bibliography -- Chapter 9 Joint Design of Beamforming, Phase Shifting, and Power Allocation in a Multi‐cluster IRS‐NOMA Network -- 9.1 Introduction -- 9.1.1 Previous Works -- 9.1.2 Motivation and Challenge -- 9.2 System Model and Problem Formulation -- 9.2.1 System Model -- 9.2.2 Problem Formulation -- 9.3 Alternating Algorithm. 9.3.1 Beamforming Optimization -- 9.3.2 Phase‐Shift Feasibility -- 9.3.3 Algorithm Design -- 9.4 Simulation Result -- 9.5 Conclusion -- Bibliography -- Chapter 10 IRS‐Aided Mobile Edge Computing: From Optimization to Learning -- 10.1 Introduction -- 10.2 System Model and Objective -- 10.3 Optimization‐Based Approaches to IRS‐Aided MEC -- 10.3.1 IRS Reflecting Coefficients Design -- 10.3.2 Receive Beamforming Design -- 10.3.3 Energy Partition Optimization -- 10.3.4 Convergence and Complexity -- 10.4 Deep Learning Approaches to IRS‐Aided MEC -- 10.4.1 CSI‐Based Learning Architecture -- 10.4.2 Location‐Only Learning Architecture -- 10.4.3 Input Feature Uncertainty -- 10.4.4 Comparison Between the CSI‐Based and CSI‐Free Learning Architectures -- 10.4.5 Complexity Reduction via Learning -- 10.5 Comparative Evaluation Results -- 10.5.1 Scenario Without LoS Direct Links -- 10.5.2 Scenario with Strong LoS Direct Links -- 10.6 Conclusions -- Bibliography -- Chapter 11 Interference Nulling Using Reconfigurable Intelligent Surface -- 11.1 Introduction -- 11.2 System Model -- 11.3 Interference Nulling via RIS -- 11.3.1 Feasibility of Interference Nulling -- 11.3.2 Alternating Projection Algorithm -- 11.3.3 Simulation Results -- 11.4 Learning to Minimize Interference -- 11.4.1 Learning to Initialize -- 11.4.2 Simulation Results -- 11.5 Conclusions -- Bibliography -- Chapter 12 Blind Beamforming for IRS Without Channel Estimation -- 12.1 Introduction -- 12.2 System Model -- 12.3 Random‐Max Sampling (RMS) -- 12.4 Conditional Sample Mean (CSM) -- 12.5 Some Comments on CSM -- 12.5.1 Connection to Closest Point Projection -- 12.5.2 Connection to Phase Retrieval -- 12.5.3 CSM for General Utility Functions -- 12.6 Field Tests -- 12.7 Conclusion -- Bibliography -- Chapter 13 RIS in Wireless Information and Power Transfer -- 13.1 Introduction -- 13.1.1 WPT and WIPT. 13.1.2 RIS -- 13.1.3 RIS in WPT and WIPT -- 13.2 RIS‐Aided WPT -- 13.2.1 WPT Architecture -- 13.2.2 Waveform and Beamforming -- 13.2.3 Channel Acquisition -- 13.2.3.1 Direct Channel -- 13.2.3.2 RIS‐Related Channels -- 13.2.4 Prototype and Experiments -- 13.3 RIS‐Aided WIPT -- 13.3.1 WIPT Categories -- 13.3.2 RIS‐Aided SWIPT -- 13.3.2.1 SWIPT Architecture -- 13.3.2.2 Waveform and Beamforming -- 13.3.2.3 Channel Acquisition -- 13.3.3 RIS‐Aided WPCN and WPBC -- 13.4 Conclusion -- Bibliography -- Chapter 14 Beamforming Design for Self‐Sustainable IRS‐Assisted MISO Downlink Systems -- 14.1 Introduction -- 14.2 System Model -- 14.2.1 Self‐Sustainable IRS Model -- 14.2.2 Channel and Signal Models -- 14.2.3 Power Harvesting Model at the IRS -- 14.3 Problem Formulation -- 14.4 Solution -- 14.4.1 Problem Transformation -- 14.4.2 Address the Coupling Variables and Binary Variables -- 14.4.3 Successive Convex Approximation -- 14.5 Numerical Results -- 14.6 Summary -- 14.7 Further Extension -- Bibliography -- Chapter 15 Optical Intelligent Reflecting Surfaces -- 15.1 Introduction -- 15.2 System and Channel Model -- 15.2.1 IRS Model -- 15.2.2 Transmitter and Receiver Model -- 15.2.3 Channel Model -- 15.3 Communication Theoretical Modeling of Optical IRSs -- 15.3.1 Scattering Theory -- 15.3.1.1 Incident Beam on the IRS -- 15.3.1.2 Huygens-Fresnel Principle -- 15.3.1.3 Intermediate‐Field Versus Far‐Field -- 15.3.1.4 Received Power Density -- 15.3.2 Geometric Optics -- 15.3.2.1 Equivalent Mirror‐Assisted Analysis -- 15.3.2.2 Received Power Density -- 15.4 Design of Optical IRSs for FSO Systems -- 15.4.1 IRS‐Assisted Point‐to‐Point System -- 15.4.1.1 IRS Phase‐Shift Profile Φ(r,rt) -- 15.4.1.2 IRS Efficiency ζ -- 15.4.2 IRS‐Assisted Multi‐Link System -- 15.4.2.1 Time Division Protocol -- 15.4.2.2 IRS Division Protocol -- 15.4.2.3 IRS Homogenization Protocol. 15.5 Simulation Results. |
| Record Nr. | UNINA-9910830726203321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2024] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Security and Privacy Vision In 6G : A Comprehensive Guide / / Pawani Porambage and Madhusanka Liyanage
| Security and Privacy Vision In 6G : A Comprehensive Guide / / Pawani Porambage and Madhusanka Liyanage |
| Autore | Porambage Pawani |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023] |
| Descrizione fisica | 1 online resource (387 pages) |
| Disciplina | 621.38456 |
| Soggetto topico |
6G mobile communication systems
Data privacy |
| ISBN |
1-119-87543-9
1-119-87541-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Acronyms -- About the Authors -- Foreword -- Preface -- Acknowledgments -- Part I Introduction -- Chapter 1 Evolution of Mobile Networks -- 1.1 Introduction -- 1.2 6G Mobile Communication Networks -- 1.2.1 6G as Envisioned Today -- 1.3 Key Driving Trends Toward 6G -- 1.4 6G Requirements/Vision -- 1.4.1 6G Development Timeline -- References -- Chapter 2 Key 6G Technologies -- 2.1 Radio Network Technologies -- 2.1.1 Beyond Sub 6 GHz Toward THz Communication -- 2.1.2 Nonterrestrial Networks Toward 3D Networking -- 2.2 AI/ML/FL -- 2.3 DLT/Blockchain -- 2.4 Edge Computing -- 2.5 Quantum Communication -- 2.6 Other New Technologies -- 2.6.1 Visible Light Communications -- 2.6.2 Large Intelligent Surfaces -- 2.6.3 Compressive Sensing -- 2.6.4 Zero‐Touch Network and Service Management -- 2.6.5 Efficient Energy Transfer and Harvesting -- References -- Chapter 3 6G Security Vision -- 3.1 Overview of 6G Security Vision -- 3.1.1 New 6G Requirements -- 3.2 6G Security Vision and KPIs -- 3.2.1 Security Threat Landscape for 6G Architecture -- References -- Part II Security in 6G Architecture -- Chapter 4 6G Device Security -- 4.1 Overview of 6G Devices -- 4.2 6G Device Security Challenges -- 4.2.1 Growth of Data Collection -- 4.2.2 Cloud Connectivity -- 4.2.3 Device Capacity -- 4.2.4 Ultrasaturated Devices -- 4.3 Addressing Device Security in 6G -- References -- Chapter 5 Open RAN and RAN‐Core Convergence -- 5.1 Introduction -- 5.2 Open RAN Architecture -- 5.3 Threat Vectors and Security Risks Associated with Open RAN -- 5.3.1 Threat Taxonomy -- 5.3.2 Risks Related to the Process -- 5.3.2.1 Prerequisites -- 5.3.2.2 General Regulations -- 5.3.2.3 Privacy -- 5.3.2.4 People -- 5.3.3 Risks Related to the Technology -- 5.3.3.1 Open Source Software -- 5.3.3.2 Radio/Open Interface -- 5.3.3.3 Intelligence.
5.3.3.4 Virtualization -- 5.3.4 Global Risks -- 5.4 Security Benefits of Open RAN -- 5.4.1 Open RAN specific -- 5.4.1.1 Full Visibility -- 5.4.1.2 Selection of Best Modules -- 5.4.1.3 Diversity -- 5.4.1.4 Modularity -- 5.4.1.5 Enforcement of Security Controls -- 5.4.1.6 Open Interfaces -- 5.4.1.7 Open Source Software -- 5.4.1.8 Automation -- 5.4.1.9 Open Standards -- 5.4.2 V‐RAN Specific -- 5.4.2.1 Isolation -- 5.4.2.2 Increased Scalability for Security Management -- 5.4.2.3 Control Trust -- 5.4.2.4 Less Dependency Between hardware [HW] and SW -- 5.4.2.5 Private Network -- 5.4.2.6 More Secure Storage of Key Material -- 5.4.3 5G Networks Related -- 5.4.3.1 Edge Oriented -- 5.4.3.2 Simpler Security Model -- 5.5 Conclusion -- References -- Chapter 6 Edge Intelligence* -- 6.1 Overview of Edge Intelligence -- 6.2 State‐of‐the‐Art Related to 5G -- 6.2.1 Denial of Service (DOS) -- 6.2.2 Man‐in‐the‐Middle (MitM) Attack -- 6.2.3 Privacy Leakage -- 6.3 State‐of‐the‐Art Related to 6G -- 6.3.1 Training Dataset Manipulation -- 6.3.2 Interception of Private Information -- 6.3.3 Attacks on Learning Agents -- 6.4 Edge Computing Security in Autonomous Driving -- 6.5 Future and Challenges -- References -- Chapter 7 Specialized 6G Networks and Network Slicing -- 7.1 Overview of 6G Specialized Networks -- 7.2 Network Slicing in 6G -- 7.2.1 Trust in Network Slicing -- 7.2.2 Privacy Aspects in Network Slicing -- 7.2.3 Solutions for Privacy and Trust in NS -- References -- Chapter 8 Industry 5.0* -- 8.1 Introduction -- 8.2 Motivations Behind the Evolution of Industry 5.0 -- 8.3 Key Features of Industry 5.0 -- 8.3.1 Smart Additive Manufacturing -- 8.3.2 Predictive Maintenance -- 8.3.3 Hyper Customization -- 8.3.4 Cyber‐Physical Cognitive Systems -- 8.4 Security of Industry 5.0 -- 8.4.1 Security Issues of Industry 5.0 -- 8.5 Privacy of Industry 5.0 -- References. Part III Security in 6G Use Cases -- Chapter 9 Metaverse Security in 6G -- 9.1 Overview of Metaverse -- 9.2 What Is Metaverse? -- 9.2.1 Metaverse Architecture -- 9.2.2 Key Characteristics of Metaverse -- 9.2.3 Role of 6G in Metaverse -- 9.3 Security Threats in Metaverse -- 9.4 Countermeasures for Metaverse Security Threats -- 9.5 New Trends in Metaverse Security -- Chapter 10 Society 5.0 and Security* -- 10.1 Industry and Society Evolution -- 10.1.1 Industry 4.0 -- 10.1.2 Society 5.0 -- 10.2 Technical Enablers and Challenges -- 10.2.1 Dependable Wireless Connectivity -- 10.2.1.1 New Spectrum and Extreme Massive MIMO -- 10.2.1.2 In‐X Subnetworks -- 10.2.1.3 Semantic Communication -- 10.2.2 Integrated Communication, Control, Computation, and Sensing -- 10.2.2.1 CoCoCo -- 10.2.2.2 JCAS -- 10.2.3 Intelligence Everywhere -- 10.2.4 Energy Harvesting and Transfer -- 10.2.4.1 Energy Harvesting -- 10.2.4.2 Wireless Power Transfer -- 10.3 Security in Society 5.0 -- References -- Chapter 11 6G‐Enabled Internet of Vehicles -- 11.1 Overview of V2X Communication and IoV -- 11.2 IoV Use Cases -- 11.3 Connected Autonomous Vehicles (CAV) -- 11.4 Unmanned Aerial Vehicles in Future IoV -- 11.5 Security Landscape for IoV -- 11.5.1 Security Threats -- 11.5.2 Security Requirements -- References -- Chapter 12 Smart Grid 2.0 Security* -- 12.1 Introduction -- 12.2 Evolution of SG 2.0 -- 12.3 Smart Grid 2.0 -- 12.3.1 Comparison of Smart Grids 1.0 and 2.0 -- 12.4 Role of 6G in SG 2.0 -- 12.5 Security Challenges of SG 2.0 -- 12.5.1 Physical Attacks -- 12.5.2 Software Attacks -- 12.5.3 Network Attacks -- 12.5.4 Attacks to the Controller -- 12.5.5 Encryption‐Related Attacks -- 12.5.6 AI‐ and ML‐Related Attacks -- 12.5.7 Stability and Reliability of Power Supply -- 12.5.8 Secure and Transparent Energy Trading Among Prosumers and Consumers. 12.5.9 Efficient and Reliable Communication Topology for Information and Control Signal Exchange -- 12.6 Privacy Issues of SG2.0 -- 12.7 Trust Management -- 12.8 Security and Privacy Standardization on SG 2.0 -- References -- Part IV Privacy in 6G Vision -- Chapter 13 6G Privacy* -- 13.1 Introduction -- 13.2 Privacy Taxonomy -- 13.3 Privacy in Actions on Data -- 13.3.1 Information Collection -- 13.3.2 Information Processing -- 13.3.3 Information Dissemination -- 13.3.4 Invasion -- 13.4 Privacy Types for 6G -- 13.4.1 Data -- 13.4.2 Actions and Personal Behavior -- 13.4.3 Image and Video -- 13.4.4 Communication -- 13.4.5 Location -- 13.5 6G Privacy Goals -- 13.5.1 Ensure of Privacy‐Protected Big Data -- 13.5.2 Privacy Guarantees for Edge Networks -- 13.5.3 Achieving Balance Between Privacy and Performance of Services -- 13.5.4 Standardization of Privacy in Technologies, and Applications -- 13.5.5 Balance the Interests in Privacy Protection in Global Context -- 13.5.6 Achieving Proper Utilization of Interoperability and Data Portability -- 13.5.7 Quantifying Privacy and Privacy Violations -- 13.5.7.1 Achieving Privacy Protected AI‐Driven Automated Network Management Operations -- 13.5.8 Getting Explanations of AI Actions for Privacy Requirements -- References -- Chapter 14 6G Privacy Challenges and Possible Solution* -- 14.1 Introduction -- 14.2 6G Privacy Challenges and Issues -- 14.2.1 Advanced 6G Applications with New Privacy Requirements -- 14.2.2 Privacy Preservation Limitations for B5G/6G Control and Orchestration Layer -- 14.2.3 Privacy Attacks on AI Models -- 14.2.4 Privacy Requirements in Cloud Computing and Storage Environments -- 14.2.5 Privacy Issues in Edge Computing and Edge AI -- 14.2.6 Cost on Privacy Enhancements -- 14.2.7 Limited Availability of Explainable AI (XAI) Techniques -- 14.2.8 Ambiguity in Responsibility of Data Ownership. 14.2.9 Data Communication Confidentiality Issues -- 14.2.10 Private Data Access Limitations -- 14.2.11 Privacy Differences Based on Location -- 14.2.12 Lack of Understanding of Privacy Rights and Threats in General Public -- 14.2.13 Difficulty in Defining Levels and Indicators for Privacy -- 14.2.13.1 Proper Evaluation of Potential Privacy Leakages from Non‐personal Data -- 14.3 Privacy Solutions for 6G -- 14.3.1 Privacy‐Preserving Decentralized AI -- 14.3.2 Edge AI -- 14.3.3 Intelligent Management with Privacy -- 14.3.4 XAI for Privacy -- 14.3.5 Privacy Measures for Personally Identifiable Information -- 14.3.6 Blockchain‐Based Solutions -- 14.3.7 Lightweight and Quantum Resistant Encryption Mechanisms -- 14.3.8 Homomorphic Encryption -- 14.3.9 Privacy‐Preserving Data Publishing Techniques -- 14.3.9.1 Syntactic Anonymization -- 14.3.9.2 Differential Privacy -- 14.3.10 Privacy by Design and Privacy by Default -- 14.3.11 Regulation of Government, Industry, and Consumer -- 14.3.12 Other Solutions -- 14.3.12.1 Location Privacy Considerations -- 14.3.12.2 Personalized Privacy -- 14.3.12.3 Fog Computing Privacy -- References -- Chapter 15 Legal Aspects and Security Standardization -- 15.1 Legal -- 15.2 Security Standardization -- 15.2.1 ETSI -- 15.2.2 ITU‐T -- 15.2.3 3GPP -- 15.2.4 NIST -- 15.2.5 IETF -- 15.2.6 5G PPP -- 15.2.7 NGMN -- 15.2.8 IEEE -- References -- Part V Security in 6G Technologies -- Chapter 16 Distributed Ledger Technologies (DLTs) and Blockchain* -- 16.1 Introduction -- 16.2 What Is Blockchain -- 16.2.1 Types of Blockchain -- 16.3 What Is Smart Contracts -- 16.4 Salient Features of Blockchain -- 16.5 Key Security Challenges Which Blockchain Can Solve -- 16.5.1 Role of Blockchain -- 16.6 Key Privacy Challenges Which Blockchain Can Solve -- 16.6.1 Key Challenges -- 16.6.2 Role of Blockchain -- 16.7 Threat Landscape of Blockchain. 16.8 Possible Solutions to Secure 6G Blockchains. |
| Record Nr. | UNINA-9910830165503321 |
Porambage Pawani
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| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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