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[null,"1.","Record Nr.",null,"UNINA9911019870103321"]
[null,null,"Autore",null,"Chopra Garima"]
[null,null,"Titolo",null,"Current and Future Cellular Systems : Technologies, Applications, and Challenges"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2025"]
[{"$nulls":4},"©2025"]
[null,null,"ISBN",null,"9781394256051"]
[{"$nulls":4},"1394256051"]
[{"$nulls":4},"9781394256075"]
[{"$nulls":4},"1394256078"]
[{"$nulls":4},"9781394256068"]
[{"$nulls":4},"139425606X"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (333 pages)"]
[null,null,"Disciplina",null,"621.382"]
[null,null,"Soggetti",null,"5G mobile communication systems"]
[{"$nulls":4},"Internet of things"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Glossary -- Introduction -- Chapter 1 Spectrum Sharing Schemes for 5G and Beyond in Wireless Communication -- 1.1 Introduction -- 1.1.1 Motivation -- 1.1.2 Literature Review -- 1.2 Spectrum Sharing Technologies -- 1.2.1 Machine Learning in Spectrum Sharing -- 1.2.2 Cooperative and Cognitive Radio Networks -- 1.2.2.1 Integration of Cooperative and Cognitive Radio Networks -- 1.2.3 Interference Mitigation Strategies -- 1.3 Case Study and Performance Evaluation -- 1.4 Future Trends and Challenges -- 1.4.1 Challenges Facing Wireless Communication -- 1.5 Conclusion -- References -- Chapter 2 Synergizing 5G, IoT, and Deep Learning: Pioneering Technological Integration for a Connected Future -- 2.1 Introduction -- 2.2 Security Threats on 5G Network -- 2.3 Applications of 5G -- 2.4 Advanced Intrusion Detection Systems (IDS) -- 2.5 Integration of 5G‐IoT‐DL -- 2.6 Security Challenges -- 2.7 Role of ML and DL in 5G at Application and Infra Level -- 2.8 Conclusion -- References -- Chapter 3 Driving Next Generation IoT with 5G and Beyond -- 3.1 Introduction -- 3.2 Need for Technological Advancement -- 3.3 Existing Wireless Technologies -- 3.4 Challenges in Existing "]
[{"$nulls":4},"Technologies -- 3.5 Towards 5G Communication -- 3.5.1 MIMO and Massive MIMO -- 3.5.2 Millimeter Wave (mmWave) Communication -- 3.5.3 Small Cells -- 3.5.4 Visible Light Communication -- 3.6 IoT and its Evolution -- 3.7 Role of 5G in IoT -- 3.8 Integration of 5G IoT with Other Technologies -- 3.8.1 AI/ML -- 3.8.2 Cloud Computing -- 3.8.3 Fog Computing -- 3.8.4 Digital Twin -- 3.8.4.1 Digital Twin Lifecycle: From Data to Transformation -- 3.9 Techniques to Improve the Performance of Wireless Networks -- 3.10 Performance Parameters of Next Generation Wireless Systems."]
[{"$nulls":4},"3.10.1 The Elaborate Rhythm of Performance Indicators -- 3.11 Challenges and Future Directions -- 3.12 Conclusion -- References -- Chapter 4 Emerging Communication Paradigms for 6G IoT: Challenges and Opportunities -- 4.1 Introduction -- 4.1.1 Breakthrough 6G Technologies -- 4.1.1.1 Holographic MIMO (Multiple Input Multiple Output) -- 4.1.1.2 Intelligent Reflecting Surfaces (IRSs) -- 4.1.1.3 Cell free Massive MIMO -- 4.1.1.4 Edge Computing -- 4.1.1.5 Terahertz (THz) Communication -- 4.1.1.6 Quantum Communication -- 4.2 Internet‐of‐Things and its Evolution -- 4.2.1 Role of 6G IoT -- 4.2.2 6G IoT Framework -- 4.3 Enabling 6G Technologies for IoT -- 4.3.1 Convergence with Other Key Technologies -- 4.3.1.1 Advancing Beyond Sub‐6 GHz Towards THz Communication -- 4.3.1.2 Artificial Intelligence and Advanced Machine Learning -- 4.3.1.3 Compressive Sensing -- 4.3.1.4 Blockchain/Distributed Ledger Technology -- 4.3.1.5 Digital Twin -- 4.3.1.6 Intelligent Edge Computing -- 4.3.1.7 Dynamic Network Slicing -- 4.3.1.8 Big Data Analytics -- 4.3.1.9 Wireless Information and Power Transfer (WIPT) -- 4.3.1.10 Backscatter Communication -- 4.3.1.11 Communication‐Computing‐Control Convergence -- 4.4 Use Case Scenarios -- 4.4.1 Smart Healthcare -- 4.4.2 Smart Transportation -- 4.4.3 Smart Manufacturing -- 4.4.4 Smart Agriculture -- 4.4.5 Smart Classrooms -- 4.4.6 Smart Cities -- 4.5 Challenges Faced and the Solutions Offered -- 4.6 Conclusion -- References -- Chapter 5 Securing the Internet of Things: Cybersecurity Challenges, Strategies, and Future Directions in the Era of 5G and Edge Computing -- 5.1 Introduction -- 5.1.1 History of IoT and Edge Computing in 5G -- 5.2 Literature Review -- 5.3 Applications in IoT and Edge Computing -- 5.4 Cybersecurity Management System for IoT Environments -- 5.4.1 Security Layers -- 5.5 Current Cyber Security Strategies in IoT."]
[{"$nulls":4},"5.6 IoT Cybersecurity's Role in Reshaping Machine Learning -- 5.6.1 Role of IoT in Artificial Intelligence -- 5.7 Real Life Scenario -- 5.8 Conclusions -- References -- Chapter 6 Autonomous Systems for 5G Networks: A Comprehensive Analysis of Features Toward Generalization and Adaptability -- 6.1 Introduction -- 6.2 Survey Method -- 6.3 Background and Related Works -- 6.3.1 Autonomous System Architecture -- 6.3.1.1 Application Layer -- 6.3.1.2 Cognitive Layer -- 6.3.1.3 Perception Layer -- 6.3.1.4 Physical Layer -- 6.3.2 Sensors -- 6.3.3 Artificial Intelligence Techniques -- 6.3.4 Intelligent Transport System (ITS) -- 6.3.5 B5G‐Based Vehicular Telecommunication -- 6.4 Discussion -- 6.4.1 Environmental Uncertainties -- 6.4.2 Security Challenges and Counter Measures -- 6.5 Conclusion -- References -- Chapter 7 Integrated Trends, Opportunities, and Challenges of 5G and Internet of Things -- 7.1 Introduction -- 7.1.1 Overview of 5G -- 7.1.2 Evolution from 1G to 5G -- 7.1.3 5G Architecture -- 7.1.4 Overview of IoT -- 7.1.5 Features of IoT -- 7.1.5.1 Avalability -- 7.1.5.2 Mobility -- 7.1.5.3 Scalabilty -- 7.1.5.4 Security -- 7.1.5.5 Context Awareness -- 7.1.6 IoT Architecture -- 7.1.6.1 Application Layer -- 7.1.6.2 Network Layer -- 7.1.6.3 Edge Layer -- 7.2 Requirements for Integration of 5G with IoT -- 7.2.1 Integrated 5G IoT Layered Architecture -- 7.3 "]
[{"$nulls":4},"Opportunities of 5G integrated IoT -- 7.3.1 Smart Cities -- 7.3.2 Smart Vehicles -- 7.3.3 Device to Device Communications -- 7.3.4 Business -- 7.3.5 Satelite and Aerial Research -- 7.3.6 Video Surveillance -- 7.4 Challenges of 5G Integrated IOT -- 7.4.1 Insufficient Control over Data Storage and Usage -- 7.4.2 Scalability -- 7.4.3 Heterogeneity of 5G and IoT Data -- 7.4.4 Blockchain Processing Time -- 7.4.5 5G mm‐Wave Issues -- 7.4.6 Threat Protection of 5G IoT -- 7.5 Conclusion -- References."]
[{"$nulls":4},"Chapter 8 Advancement in Resource Allocation for Future Generation of Communications -- 8.1 Introduction -- 8.2 Current Trends in Multiple Access Techniques -- 8.3 Scheduling Algorithms for 5G/Beyond 5G -- 8.4 Factors Influencing Scheduling Algorithms -- 8.5 Resource Allocation for 5G Ultra‐Dense Networks -- 8.6 Conclusion -- References -- Chapter 9 Next‐Gen Networked Healthcare: Requirements and Challenges -- 9.1 Introduction -- 9.2 Applications -- 9.2.1 Remote Robotic‐Assisted Surgery -- 9.2.2 Remote Diagnosis and Teleconsultation -- 9.2.3 In‐Ambulance Treatment -- 9.2.4 Remote Patient Monitoring -- 9.2.5 Medical Big Data Management -- 9.2.6 Augmented reality (AR) and Virtual Reality (VR) -- 9.2.7 Emergency Response Strategies -- 9.3 Technological Prerequisites -- 9.4 Challenges in 5G Integration in Healthcare -- 9.5 Conclusion -- References -- Chapter 10 Dynamic Resource Orchestration for Computing, Data, and IoT in Networked Systems: A Data‐Centric Approach -- 10.1 Introduction -- 10.1.1 Motivation -- 10.1.2 Objectives -- 10.2 Dynamic Resource Orchestration: Foundations -- 10.2.1 Resource Orchestration Concepts -- 10.2.2 Dynamic Resource Orchestration's Evolution -- 10.2.3 Importance of a Data‐Centric Perspective -- 10.3 Computing in Networked Systems -- 10.3.1 Cloud Computing Paradigm -- 10.3.2 Edge Computing and Fog Computing -- 10.3.3 Integration of Computing Resources -- 10.4 Data‐Centric Orchestration -- 10.4.1 Data‐Driven Resource Allocation -- 10.4.1.1 Data‐Driven Decision‐Making -- 10.4.1.2 Dynamic Scaling -- 10.4.1.3 Perceptive Formulas -- 10.4.1.4 Customization and Adaptability -- 10.4.2 Data Processing and Management -- 10.4.2.1 Data Locality and Optimization -- 10.4.2.2 Techniques for Data Movement -- 10.4.2.3 Data Lifecycle Management -- 10.4.2.4 AI and Data Analytics Integration -- 10.4.3 Security and Privacy Considerations."]
[{"$nulls":4},"10.4.3.1 Completely Encryption -- 10.4.3.2 Identity and Access Management -- 10.4.3.3 Safe Data Processing -- 10.4.3.4 Regulatory Standard Compliance -- 10.4.3.5 Privacy‐Preserving Techniques -- 10.4.3.6 Audit Trails and Monitoring -- 10.5 IoT Integration -- 10.5.1 Overview of IoT Architecture -- 10.5.2 IoT Resource Orchestration Challenges -- 10.5.2.1 Device Heterogeneity -- 10.5.2.2 Scalability and Data Volume -- 10.5.2.3 Low‐Latency and Real‐Time Processing -- 10.5.2.4 Compatibility and Standards -- 10.5.3 Combining Data and Computing -- 10.5.3.1 Data‐Centric Orchestration -- 10.5.3.2 IoT with Machine Learning and AI -- 10.5.3.3 Dynamic Resource Allocation -- 10.5.3.4 IoT Security Measures -- 10.6 Methodologies for Dynamic Resource Orchestration -- 10.6.1 Methods of Machine Learning -- 10.6.1.1 Overview of Machine Learning for Resource Management -- 10.6.1.2 Predictive Resource -- 10.6.1.3 Fault Prediction and Anomaly Detection -- 10.6.2 Methods of Optimisation -- 10.6.2.1 Introducing Resource Orchestration's Optimisation Techniques -- 10.6.3 Hybrid Models -- 10.6.3.1 Optimisation Through Machine Learning Hybrids -- 10.6.3.2 Combining Rule‐Based and Learning‐Based Methods: Advancing Hybrid Approaches -- 10.6.3.3 Continual Enhancement Through Responsive Feedback Mechanisms -- 10.6.3.4 Harnessing the Power of Adaptive Model Switching -- 10.7 Case Studies -- 10.7.1 "]
[{"$nulls":4},"Practical Applications -- 10.7.1.1 AWS -- 10.7.1.2 Autoscaling of Kubernetes Horizontal Pods -- 10.7.2 Achievements and Insights Acquired -- 10.7.2.1 Netflix: Using Machine Learning to Deliver Content -- 10.7.2.2 Google's Expansion of Kubernetes: Enhancing Scalability -- 10.7.2.3 Achieving Dynamic Scalability with AWS Auto Scaling: An Airbnb Success Story -- 10.8 Conclusion -- References."]
[{"$nulls":4},"Chapter 11 Cognitive Cellular Networks: Empowering Future Connectivity Through Artificial Intelligence."]
[null,null,null,"Sommario/riassunto","\"Due to the explosive demand of high data rates by subscribers and applications, 5G in wireless communication is dynamic and keeps evolving to cater to future needs. As conventional schemes and techniques have their limitations in terms of performance, modification and adaptiveness are required while designing the architecture. Also, limited spectrum availability has pushed towards work in high frequency bands with bandwidth availability in abundance. At these high frequencies the expected losses are also very high in comparison to previous generations of communications. These losses at high frequencies can be overcome by deploying high power, multiple transmitting antennas, called massive MIMO. With a larger number of antennas, the power of multiple antennas can be combined together to form beams for loss coverage.\"--"]