Garima Chopra, PhD, is an Assistant Professor with Chitkara University Institute of Engineering & Technology at Chitkara University, Punjab, India. Suhaib Ahmed, PhD, is an Assistant Professor with Model Institute of Engineering and Technology, Jammu, J&K, India. Shalli Rani, PhD, is a Professor with Chitkara University Institute of Engineering & Technology at Chitkara University, Punjab, India.

• About the Editors xviiList of Contributors xixPreface xxvGlossary xxviiIntroduction xxix1 Spectrum Sharing Schemes for 5G and Beyond in Wireless Communication 1Aditya Bakshi, Akhil Gupta, and Arushi Pandey1.1 Introduction 11.1.1 Motivation 21.1.2 Literature Review 21.2 Spectrum Sharing Technologies 61.2.1 Machine Learning in Spectrum Sharing 71.2.2 Cooperative and Cognitive Radio Networks 91.2.2.1 Integration of Cooperative and Cognitive Radio Networks 101.2.3 Interference Mitigation Strategies 101.3 Case Study and Performance Evaluation 121.4 Future Trends and Challenges 141.4.1 Challenges Facing Wireless Communication 151.5 Conclusion 16References 172 Synergizing 5G, IoT, and Deep Learning: Pioneering Technological Integration for a Connected Future 21Ankita Sharma and Shalli Rani2.1 Introduction 212.2 Security Threats on 5G Network 222.3 Applications of 5G 242.4 Advanced Intrusion Detection Systems (IDS) 252.5 Integration of 5G-IoT-DL 252.6 Security Challenges 262.7 Role of ML and DL in 5G at Application and Infra Level 272.8 Conclusion 29References 293 Driving Next Generation IoT with 5G and Beyond 33Shishir Shrivastava, Ankita Rana, and Ashu Taneja3.1 Introduction 333.2 Need for Technological Advancement 353.3 Existing Wireless Technologies 353.4 Challenges in Existing Technologies 373.5 Towards 5G Communication 393.5.1 MIMO and Massive MIMO 393.5.2 Millimeter Wave (mmWave) Communication 423.5.3 Small Cells 433.5.4 Visible Light Communication 443.6 IoT and its Evolution 453.7 Role of 5G in IoT 463.8 Integration of 5G IoT with Other Technologies 473.8.1 Ai/ml 503.8.2 Cloud Computing 503.8.3 Fog Computing 513.8.4 Digital Twin 523.8.4.1 Digital Twin Lifecycle: From Data to Transformation 533.9 Techniques to Improve the Performance of Wireless Networks 553.10 Performance Parameters of Next Generation Wireless Systems 583.10.1 The Elaborate Rhythm of Performance Indicators 603.11 Challenges and Future Directions 603.12 Conclusion 61References 624 Emerging Communication Paradigms for 6G IoT: Challenges and Opportunities 65Aditya Soni, Ashu Taneja, Neeti Taneja, and Laith Abualigah4.1 Introduction 654.1.1 Breakthrough 6G Technologies 684.1.1.1 Holographic MIMO (Multiple Input Multiple Output) 684.1.1.2 Intelligent Reflecting Surfaces (IRSs) 684.1.1.3 Cell free Massive MIMO 694.1.1.4 Edge Computing 704.1.1.5 Terahertz (THz) Communication 704.1.1.6 Quantum Communication 714.2 Internet-of-Things and its Evolution 714.2.1 Role of 6G IoT 714.2.2 6G IoT Framework 724.3 Enabling 6G Technologies for IoT 734.3.1 Convergence with Other Key Technologies 754.3.1.1 Advancing Beyond Sub-6 GHz Towards THz Communication 764.3.1.2 Artificial Intelligence and Advanced Machine Learning 764.3.1.3 Compressive Sensing 764.3.1.4 Blockchain/Distributed Ledger Technology 774.3.1.5 Digital Twin 774.3.1.6 Intelligent Edge Computing 774.3.1.7 Dynamic Network Slicing 784.3.1.8 Big Data Analytics 784.3.1.9 Wireless Information and Power Transfer (WIPT) 784.3.1.10 Backscatter Communication 794.3.1.11 Communication-Computing-Control Convergence 794.4 Use Case Scenarios 804.4.1 Smart Healthcare 804.4.2 Smart Transportation 814.4.3 Smart Manufacturing 824.4.4 Smart Agriculture 834.4.5 Smart Classrooms 834.4.6 Smart Cities 844.5 Challenges Faced and the Solutions Offered 854.6 Conclusion 86References 875 Securing the Internet of Things: Cybersecurity Challenges, Strategies, and Future Directions in the Era of 5G and Edge Computing 89Geetanshi, Harshit Manocha, Himanshi Babbar, and Cherry Mangla5.1 Introduction 895.1.1 History of IoT and Edge Computing in 5G 945.2 Literature Review 955.3 Applications in IoT and Edge Computing 955.4 Cybersecurity Management System for IoT Environments 975.4.1 Security Layers 975.5 Current Cyber Security Strategies in IoT 995.6 IoT Cybersecurity’s Role in Reshaping Machine Learning 1005.6.1 Role of IoT in Artificial Intelligence 1015.7 Real Life Scenario 1025.8 Conclusions 105References 1056 Autonomous Systems for 5G Networks: A Comprehensive Analysis of Features Toward Generalization and Adaptability 107Durga Shankar Baggam and Shalli Rani6.1 Introduction 1076.2 Survey Method 1096.3 Background and Related Works 1136.3.1 Autonomous System Architecture 1146.3.1.1 Application Layer 1206.3.1.2 Cognitive Layer 1206.3.1.3 Perception Layer 1206.3.1.4 Physical Layer 1206.3.2 Sensors 1216.3.3 Artificial Intelligence Techniques 1216.3.4 Intelligent Transport System (ITS) 1246.3.5 B5G-Based Vehicular Telecommunication 1256.4 Discussion 1266.4.1 Environmental Uncertainties 1286.4.2 Security Challenges and Counter Measures 1296.5 Conclusion 129References 1307 Integrated Trends, Opportunities, and Challenges of 5G and Internet of Things 139Ekta Dixit and Shalli Rani7.1 Introduction 1397.1.1 Overview of 5G 1407.1.2 Evolution from 1G to 5G 1417.1.3 5G Architecture 1417.1.4 Overview of IoT 1437.1.5 Features of IoT 1437.1.5.1 Avalability 1437.1.5.2 Mobility 1437.1.5.3 Scalabilty 1437.1.5.4 Security 1447.1.5.5 Context Awareness 1447.1.6 IoT Architecture 1447.1.6.1 Application Layer 1447.1.6.2 Network Layer 1447.1.6.3 Edge Layer 1457.2 Requirements for Integration of 5G with IoT 1457.2.1 Integrated 5G IoT Layered Architecture 1457.3 Opportunities of 5G integrated IoT 1467.3.1 Smart Cities 1467.3.2 Smart Vehicles 1467.3.3 Device to Device Communications 1477.3.4 Business 1477.3.5 Satelite and Aerial Research 1477.3.6 Video Surveillance 1477.4 Challenges of 5G Integrated IoT 1477.4.1 Insufficient Control over Data Storage and Usage 1487.4.2 Scalability 1487.4.3 Heterogeneity of 5G and IoT Data 1487.4.4 Blockchain Processing Time 1487.4.5 5G mm-Wave Issues 1497.4.6 Threat Protection of 5G IoT 1497.5 Conclusion 149References 1508 Advancement in Resource Allocation for Future Generation of Communications 153Garima Chopra and Suhaib Ahmed8.1 Introduction 1538.2 Current Trends in Multiple Access Techniques 1548.3 Scheduling Algorithms for 5G/Beyond 5G 1558.4 Factors Influencing Scheduling Algorithms 1588.5 Resource Allocation for 5G Ultra-Dense Networks 1608.6 Conclusion 162References 1629 Next-Gen Networked Healthcare: Requirements and Challenges 165Kanica Sachdev and Brejesh Lall9.1 Introduction 1659.2 Applications 1669.2.1 Remote Robotic-Assisted Surgery 1679.2.2 Remote Diagnosis and Teleconsultation 1679.2.3 In-Ambulance Treatment 1689.2.4 Remote Patient Monitoring 1699.2.5 Medical Big Data Management 1709.2.6 Augmented Reality (AR) and Virtual Reality (VR) 1709.2.7 Emergency Response Strategies 1719.3 Technological Prerequisites 1729.4 Challenges in 5G Integration in Healthcare 1759.5 Conclusion 177References 18010 Dynamic Resource Orchestration for Computing, Data, and IoT in Networked Systems: A Data-Centric Approach 185Suresh Limkar, Mohammad Alamgir Hossain, Sherif Tawfik Amin, and Yasir Ahmad10.1 Introduction 18510.1.1 Motivation 18710.1.2 Objectives 18710.2 Dynamic Resource Orchestration: Foundations 18710.2.1 Resource Orchestration Concepts 18710.2.2 Dynamic Resource Orchestration’s Evolution 18810.2.3 Importance of a Data-Centric Perspective 18810.3 Computing in Networked Systems 18910.3.1 Cloud Computing Paradigm 18910.3.2 Edge Computing and Fog Computing 19110.3.3 Integration of Computing Resources 19210.4 Data-Centric Orchestration 19310.4.1 Data-Driven Resource Allocation 19310.4.1.1 Data-Driven Decision-Making 19310.4.1.2 Dynamic Scaling 19410.4.1.3 Perceptive Formulas 19410.4.1.4 Customization and Adaptability 19410.4.2 Data Processing and Management 19410.4.2.1 Data Locality and Optimization 19410.4.2.2 Techniques for Data Movement 19410.4.2.3 Data Lifecycle Management 19410.4.2.4 AI and Data Analytics Integration 19510.4.3 Security and Privacy Considerations 19510.4.3.1 Completely Encryption 19510.4.3.2 Identity and Access Management 19510.4.3.3 Safe Data Processing 19510.4.3.4 Regulatory Standard Compliance 19510.4.3.5 Privacy-Preserving Techniques 19510.4.3.6 Audit Trails and Monitoring 19610.5 IoT Integration 19610.5.1 Overview of IoT Architecture 19610.5.2 IoT Resource Orchestration Challenges 19710.5.2.1 Device Heterogeneity 19710.5.2.2 Scalability and Data Volume 19710.5.2.3 Low-Latency and Real-Time Processing 19710.5.2.4 Compatibility and Standards 19710.5.3 Combining Data and Computing 19710.5.3.1 Data-Centric Orchestration 19810.5.3.2 IoT with Machine Learning and AI 19810.5.3.3 Dynamic Resource Allocation 19810.5.3.4 IoT Security Measures 19910.6 Methodologies for Dynamic Resource Orchestration 20010.6.1 Methods of Machine Learning 20010.6.1.1 Overview of Machine Learning for Resource Management 20010.6.1.2 Predictive Resource 20010.6.1.3 Fault Prediction and Anomaly Detection 20010.6.2 Methods of Optimisation 20110.6.2.1 Introducing Resource Orchestration’s Optimisation Techniques 20110.6.3 Hybrid Models 20110.6.3.1 Optimisation Through Machine Learning Hybrids 20110.6.3.2 Combining Rule-Based and Learning-Based Methods: Advancing Hybrid Approaches 20110.6.3.3 Continual Enhancement Through Responsive Feedback Mechanisms 20210.6.3.4 Harnessing the Power of Adaptive Model Switching 20210.7 Case Studies 20210.7.1 Practical Applications 20210.7.1.1 Aws 20210.7.1.2 Autoscaling of Kubernetes Horizontal Pods 20210.7.2 Achievements and Insights Acquired 20310.7.2.1 Netflix: Using Machine Learning to Deliver Content 20310.7.2.2 Google’s Expansion of Kubernetes: Enhancing Scalability 20310.7.2.3 Achieving Dynamic Scalability with AWS Auto Scaling: An Airbnb Success Story 20310.8 Conclusion 204References 20411 Cognitive Cellular Networks: Empowering Future Connectivity Through Artificial Intelligence 209Mohammad Alamgir Hossain, Suresh Limkar, Sherif Tawfik Amin, and Yasir Ahmad11.1 Introduction 20911.1.1 Background 20911.1.2 Key Objectives of the Chapter 21011.2 Foundations of Cognitive Cellular Networks 21111.2.1 Architecture of Cellular Networks 21111.2.2 Radio Technologies Induced by Cognition 21111.2.3 Artificial Intelligence Integration 21211.3 AI Algorithms for Network Optimization 21311.3.1 Machine Learning Models for Predictive Analysis 21311.3.1.1 Machine Learning in Resource Allocation 21311.3.1.2 Predictive Analytics for Traffic Management 21311.3.1.3 Reinforcement Learning for Self-Optimizing Networks 21311.3.1.4 Anomaly Detection to Strengthen Security 21411.3.1.5 Artificial Neural Networks for Dynamic Optimization 21411.3.1.6 Combining Genetic Algorithms with Cross-Layer Optimization 21411.3.2 Spectrum Utilization and Management 21411.3.2.1 Dynamic Spectrum Access 21411.3.2.2 Brain CRT 21511.3.2.3 Enhancing Spectrum Management with AI-Powered Solutions to Combat Interference 21511.3.2.4 Achieving Regulatory Compliance in Spectrum Sharing 21511.4 Reinforcement Learning in Autonomous Network Management 21511.4.1 Essential Guidelines for Mastering Reinforcement Learning 21611.4.2 Adaptive Decision-Making in Dynamic Environments 21711.4.2.1 Time-Based Learning and the Trade-Off Between Exploration and Exploitation 21711.4.2.2 Dynamic Approaches to State Representation and Policy Adaptation 21811.4.3 Case Studies on Autonomous Network Management 21811.5 Applications of Cognitive Cellular Networks 21911.5.1 Upgraded Mobile Broadband 22011.5.2 Massive Machine-Type Communication 22011.5.3 Ultra-reliable Low-Latency Communication 22111.5.4 Use Cases and Practical Implementations 22111.6 Challenges and Future Directions 22211.6.1 Scalability and Standardization 22211.6.2 Future Trends in Cognitive Cellular Networks 22211.7 Conclusion 223References 22412 Enhancing Scalability and Performance in Networked Applications Through Smart Computing Resource Allocation 227Araddhana Arvind Deshmukh, Shailesh Pramod Bendale, Sheela Hundekari, Abhijit Chitre, Kirti Wanjale, Amol Dhumane, Garima Chopra, and Shalli Rani12.1 Introduction 22712.1.1 Scope and Objectives 22912.1.2 Objectives 22912.1.2.1 Key Goals of This Study 22912.2 Foundations of Smart Computing Resource Allocation 23012.2.1 Key Concepts in Resource Allocation 23212.2.1.1 Dynamic Resource Allocation 23212.2.1.2 Artificial Intelligence (AI) in Resource Management 23212.2.1.3 Using Real-Time Analytics to Track Performance 23212.2.1.4 Scalability and Elasticity Measures 23212.2.1.5 Mechanisms of Adaptive Learning 23312.2.1.6 Security-Driven Resource Allocation 23312.2.2 The Evolution of Scalability and Performance in Networked Applications 23312.2.2.1 Conventional Static Resource Allocation 23312.2.2.2 The Arise of Scalability Issues 23312.2.2.3 The Cloud Paradigm and Dynamic Resource Allocation 23412.2.2.4 Using Smart Computing to Allocate Intelligent Resources 23412.2.2.5 Real-Time Adaptation and Predictive Scaling 23412.2.2.6 Scalability Beyond Traditionally Assigned Limitations 23412.2.2.7 Automation and Autonomy’s Role 23412.3 Dynamic Resource Allocation Strategies 23512.3.1 Static vs. Dynamic Resource Allocation 23712.3.1.1 Static Resource Allocation 23712.3.1.2 Dynamic Resource Allocation 23712.3.2 Adaptive Resource Allocation Algorithms 23712.3.3 Machine Learning Approaches in Resource Allocation 23812.4 Intelligent Load Balancing Techniques 23812.4.1 Load Balancing in Networked Environments 23912.4.2 Importance of Load Balancing in Scalability 24012.4.2.1 Load Balancing with Machine Learning 24012.4.2.2 Adaptive Load Balancing Algorithms 24012.5 Real-Time Monitoring and Feedback Mechanisms 24112.5.1 Proactive Monitoring for Allocation of Resources 24112.5.2 Decision-Making and Feedback Loops 24112.5.3 Real-Time Monitoring 24212.6 Case Studies and Best Practices 24312.6.1 Cloud-Based Resource Allocation 24312.6.2 Edge Computing and Resource Optimization 24312.6.3 High-Performance Computing (HPC) Environments 24412.7 Security and Privacy Considerations 24412.7.1 Ensuring Security in Resource Allocation 24412.7.1.1 Overview of Security 24412.7.2 Privacy Issues with Wise Resource Distribution 24512.7.2.1 Overview of Privacy 24512.7.3 Balancing Security and Performance 24512.7.3.1 Understanding the Art of Balancing Responsibilities 24512.8 Future Trends and Emerging Technologies 24612.8.1 Resource Allocation and Edge AI 24612.8.1.1 Understanding the Basics of Edge AI 24612.8.2 Implications for Quantum Computing 24612.8.2.1 A Comprehensive Look at the World of Quantum Computing 24612.8.3 Allocating Resources with Blockchain 24712.8.3.1 Overview of Blockchain 24712.9 Conclusion 248References 24813 5G-Enabled Fusion: Navigating the Future Landscape of Cloud Computing, Internet of Things, and Recommender Systems 251Sheetal Sharma13.1 Basics of Cloud Computing 25113.2 Internet of Things 25413.3 5G Technology 25713.4 Recommender System 25813.5 Conclusion 262References 26214 Confluence of Cellular IoT and Data Science for Smart Application using 5G 267Shruti and Shalli Rani14.1 Introduction 26714.2 Data Science and Cellular IoT 27014.3 Research Problems in Data Science for Cellular IoT 27214.4 Sensors in Cellular IoT Smart Farming 27314.5 Related Work 27514.6 Data Science for Agriculture 27714.7 Challenges Faced by Cellular IoT Application in Data Science 27814.8 Proposed Model and its Discussion 28014.9 Conclusion 281References 282Index 285

Garima Chopra, PhD, is an Assistant Professor with Chitkara University Institute of Engineering & Technology at Chitkara University, Punjab, India. Suhaib Ahmed, PhD, is an Assistant Professor with Model Institute of Engineering and Technology, Jammu, J&K, India. Shalli Rani, PhD, is a Professor with Chitkara University Institute of Engineering & Technology at Chitkara University, Punjab, India.

• About the Editors xviiList of Contributors xixPreface xxvGlossary xxviiIntroduction xxix1 Spectrum Sharing Schemes for 5G and Beyond in Wireless Communication 1Aditya Bakshi, Akhil Gupta, and Arushi Pandey1.1 Introduction 11.1.1 Motivation 21.1.2 Literature Review 21.2 Spectrum Sharing Technologies 61.2.1 Machine Learning in Spectrum Sharing 71.2.2 Cooperative and Cognitive Radio Networks 91.2.2.1 Integration of Cooperative and Cognitive Radio Networks 101.2.3 Interference Mitigation Strategies 101.3 Case Study and Performance Evaluation 121.4 Future Trends and Challenges 141.4.1 Challenges Facing Wireless Communication 151.5 Conclusion 16References 172 Synergizing 5G, IoT, and Deep Learning: Pioneering Technological Integration for a Connected Future 21Ankita Sharma and Shalli Rani2.1 Introduction 212.2 Security Threats on 5G Network 222.3 Applications of 5G 242.4 Advanced Intrusion Detection Systems (IDS) 252.5 Integration of 5G-IoT-DL 252.6 Security Challenges 262.7 Role of ML and DL in 5G at Application and Infra Level 272.8 Conclusion 29References 293 Driving Next Generation IoT with 5G and Beyond 33Shishir Shrivastava, Ankita Rana, and Ashu Taneja3.1 Introduction 333.2 Need for Technological Advancement 353.3 Existing Wireless Technologies 353.4 Challenges in Existing Technologies 373.5 Towards 5G Communication 393.5.1 MIMO and Massive MIMO 393.5.2 Millimeter Wave (mmWave) Communication 423.5.3 Small Cells 433.5.4 Visible Light Communication 443.6 IoT and its Evolution 453.7 Role of 5G in IoT 463.8 Integration of 5G IoT with Other Technologies 473.8.1 Ai/ml 503.8.2 Cloud Computing 503.8.3 Fog Computing 513.8.4 Digital Twin 523.8.4.1 Digital Twin Lifecycle: From Data to Transformation 533.9 Techniques to Improve the Performance of Wireless Networks 553.10 Performance Parameters of Next Generation Wireless Systems 583.10.1 The Elaborate Rhythm of Performance Indicators 603.11 Challenges and Future Directions 603.12 Conclusion 61References 624 Emerging Communication Paradigms for 6G IoT: Challenges and Opportunities 65Aditya Soni, Ashu Taneja, Neeti Taneja, and Laith Abualigah4.1 Introduction 654.1.1 Breakthrough 6G Technologies 684.1.1.1 Holographic MIMO (Multiple Input Multiple Output) 684.1.1.2 Intelligent Reflecting Surfaces (IRSs) 684.1.1.3 Cell free Massive MIMO 694.1.1.4 Edge Computing 704.1.1.5 Terahertz (THz) Communication 704.1.1.6 Quantum Communication 714.2 Internet-of-Things and its Evolution 714.2.1 Role of 6G IoT 714.2.2 6G IoT Framework 724.3 Enabling 6G Technologies for IoT 734.3.1 Convergence with Other Key Technologies 754.3.1.1 Advancing Beyond Sub-6 GHz Towards THz Communication 764.3.1.2 Artificial Intelligence and Advanced Machine Learning 764.3.1.3 Compressive Sensing 764.3.1.4 Blockchain/Distributed Ledger Technology 774.3.1.5 Digital Twin 774.3.1.6 Intelligent Edge Computing 774.3.1.7 Dynamic Network Slicing 784.3.1.8 Big Data Analytics 784.3.1.9 Wireless Information and Power Transfer (WIPT) 784.3.1.10 Backscatter Communication 794.3.1.11 Communication-Computing-Control Convergence 794.4 Use Case Scenarios 804.4.1 Smart Healthcare 804.4.2 Smart Transportation 814.4.3 Smart Manufacturing 824.4.4 Smart Agriculture 834.4.5 Smart Classrooms 834.4.6 Smart Cities 844.5 Challenges Faced and the Solutions Offered 854.6 Conclusion 86References 875 Securing the Internet of Things: Cybersecurity Challenges, Strategies, and Future Directions in the Era of 5G and Edge Computing 89Geetanshi, Harshit Manocha, Himanshi Babbar, and Cherry Mangla5.1 Introduction 895.1.1 History of IoT and Edge Computing in 5G 945.2 Literature Review 955.3 Applications in IoT and Edge Computing 955.4 Cybersecurity Management System for IoT Environments 975.4.1 Security Layers 975.5 Current Cyber Security Strategies in IoT 995.6 IoT Cybersecurity’s Role in Reshaping Machine Learning 1005.6.1 Role of IoT in Artificial Intelligence 1015.7 Real Life Scenario 1025.8 Conclusions 105References 1056 Autonomous Systems for 5G Networks: A Comprehensive Analysis of Features Toward Generalization and Adaptability 107Durga Shankar Baggam and Shalli Rani6.1 Introduction 1076.2 Survey Method 1096.3 Background and Related Works 1136.3.1 Autonomous System Architecture 1146.3.1.1 Application Layer 1206.3.1.2 Cognitive Layer 1206.3.1.3 Perception Layer 1206.3.1.4 Physical Layer 1206.3.2 Sensors 1216.3.3 Artificial Intelligence Techniques 1216.3.4 Intelligent Transport System (ITS) 1246.3.5 B5G-Based Vehicular Telecommunication 1256.4 Discussion 1266.4.1 Environmental Uncertainties 1286.4.2 Security Challenges and Counter Measures 1296.5 Conclusion 129References 1307 Integrated Trends, Opportunities, and Challenges of 5G and Internet of Things 139Ekta Dixit and Shalli Rani7.1 Introduction 1397.1.1 Overview of 5G 1407.1.2 Evolution from 1G to 5G 1417.1.3 5G Architecture 1417.1.4 Overview of IoT 1437.1.5 Features of IoT 1437.1.5.1 Avalability 1437.1.5.2 Mobility 1437.1.5.3 Scalabilty 1437.1.5.4 Security 1447.1.5.5 Context Awareness 1447.1.6 IoT Architecture 1447.1.6.1 Application Layer 1447.1.6.2 Network Layer 1447.1.6.3 Edge Layer 1457.2 Requirements for Integration of 5G with IoT 1457.2.1 Integrated 5G IoT Layered Architecture 1457.3 Opportunities of 5G integrated IoT 1467.3.1 Smart Cities 1467.3.2 Smart Vehicles 1467.3.3 Device to Device Communications 1477.3.4 Business 1477.3.5 Satelite and Aerial Research 1477.3.6 Video Surveillance 1477.4 Challenges of 5G Integrated IoT 1477.4.1 Insufficient Control over Data Storage and Usage 1487.4.2 Scalability 1487.4.3 Heterogeneity of 5G and IoT Data 1487.4.4 Blockchain Processing Time 1487.4.5 5G mm-Wave Issues 1497.4.6 Threat Protection of 5G IoT 1497.5 Conclusion 149References 1508 Advancement in Resource Allocation for Future Generation of Communications 153Garima Chopra and Suhaib Ahmed8.1 Introduction 1538.2 Current Trends in Multiple Access Techniques 1548.3 Scheduling Algorithms for 5G/Beyond 5G 1558.4 Factors Influencing Scheduling Algorithms 1588.5 Resource Allocation for 5G Ultra-Dense Networks 1608.6 Conclusion 162References 1629 Next-Gen Networked Healthcare: Requirements and Challenges 165Kanica Sachdev and Brejesh Lall9.1 Introduction 1659.2 Applications 1669.2.1 Remote Robotic-Assisted Surgery 1679.2.2 Remote Diagnosis and Teleconsultation 1679.2.3 In-Ambulance Treatment 1689.2.4 Remote Patient Monitoring 1699.2.5 Medical Big Data Management 1709.2.6 Augmented Reality (AR) and Virtual Reality (VR) 1709.2.7 Emergency Response Strategies 1719.3 Technological Prerequisites 1729.4 Challenges in 5G Integration in Healthcare 1759.5 Conclusion 177References 18010 Dynamic Resource Orchestration for Computing, Data, and IoT in Networked Systems: A Data-Centric Approach 185Suresh Limkar, Mohammad Alamgir Hossain, Sherif Tawfik Amin, and Yasir Ahmad10.1 Introduction 18510.1.1 Motivation 18710.1.2 Objectives 18710.2 Dynamic Resource Orchestration: Foundations 18710.2.1 Resource Orchestration Concepts 18710.2.2 Dynamic Resource Orchestration’s Evolution 18810.2.3 Importance of a Data-Centric Perspective 18810.3 Computing in Networked Systems 18910.3.1 Cloud Computing Paradigm 18910.3.2 Edge Computing and Fog Computing 19110.3.3 Integration of Computing Resources 19210.4 Data-Centric Orchestration 19310.4.1 Data-Driven Resource Allocation 19310.4.1.1 Data-Driven Decision-Making 19310.4.1.2 Dynamic Scaling 19410.4.1.3 Perceptive Formulas 19410.4.1.4 Customization and Adaptability 19410.4.2 Data Processing and Management 19410.4.2.1 Data Locality and Optimization 19410.4.2.2 Techniques for Data Movement 19410.4.2.3 Data Lifecycle Management 19410.4.2.4 AI and Data Analytics Integration 19510.4.3 Security and Privacy Considerations 19510.4.3.1 Completely Encryption 19510.4.3.2 Identity and Access Management 19510.4.3.3 Safe Data Processing 19510.4.3.4 Regulatory Standard Compliance 19510.4.3.5 Privacy-Preserving Techniques 19510.4.3.6 Audit Trails and Monitoring 19610.5 IoT Integration 19610.5.1 Overview of IoT Architecture 19610.5.2 IoT Resource Orchestration Challenges 19710.5.2.1 Device Heterogeneity 19710.5.2.2 Scalability and Data Volume 19710.5.2.3 Low-Latency and Real-Time Processing 19710.5.2.4 Compatibility and Standards 19710.5.3 Combining Data and Computing 19710.5.3.1 Data-Centric Orchestration 19810.5.3.2 IoT with Machine Learning and AI 19810.5.3.3 Dynamic Resource Allocation 19810.5.3.4 IoT Security Measures 19910.6 Methodologies for Dynamic Resource Orchestration 20010.6.1 Methods of Machine Learning 20010.6.1.1 Overview of Machine Learning for Resource Management 20010.6.1.2 Predictive Resource 20010.6.1.3 Fault Prediction and Anomaly Detection 20010.6.2 Methods of Optimisation 20110.6.2.1 Introducing Resource Orchestration’s Optimisation Techniques 20110.6.3 Hybrid Models 20110.6.3.1 Optimisation Through Machine Learning Hybrids 20110.6.3.2 Combining Rule-Based and Learning-Based Methods: Advancing Hybrid Approaches 20110.6.3.3 Continual Enhancement Through Responsive Feedback Mechanisms 20210.6.3.4 Harnessing the Power of Adaptive Model Switching 20210.7 Case Studies 20210.7.1 Practical Applications 20210.7.1.1 Aws 20210.7.1.2 Autoscaling of Kubernetes Horizontal Pods 20210.7.2 Achievements and Insights Acquired 20310.7.2.1 Netflix: Using Machine Learning to Deliver Content 20310.7.2.2 Google’s Expansion of Kubernetes: Enhancing Scalability 20310.7.2.3 Achieving Dynamic Scalability with AWS Auto Scaling: An Airbnb Success Story 20310.8 Conclusion 204References 20411 Cognitive Cellular Networks: Empowering Future Connectivity Through Artificial Intelligence 209Mohammad Alamgir Hossain, Suresh Limkar, Sherif Tawfik Amin, and Yasir Ahmad11.1 Introduction 20911.1.1 Background 20911.1.2 Key Objectives of the Chapter 21011.2 Foundations of Cognitive Cellular Networks 21111.2.1 Architecture of Cellular Networks 21111.2.2 Radio Technologies Induced by Cognition 21111.2.3 Artificial Intelligence Integration 21211.3 AI Algorithms for Network Optimization 21311.3.1 Machine Learning Models for Predictive Analysis 21311.3.1.1 Machine Learning in Resource Allocation 21311.3.1.2 Predictive Analytics for Traffic Management 21311.3.1.3 Reinforcement Learning for Self-Optimizing Networks 21311.3.1.4 Anomaly Detection to Strengthen Security 21411.3.1.5 Artificial Neural Networks for Dynamic Optimization 21411.3.1.6 Combining Genetic Algorithms with Cross-Layer Optimization 21411.3.2 Spectrum Utilization and Management 21411.3.2.1 Dynamic Spectrum Access 21411.3.2.2 Brain CRT 21511.3.2.3 Enhancing Spectrum Management with AI-Powered Solutions to Combat Interference 21511.3.2.4 Achieving Regulatory Compliance in Spectrum Sharing 21511.4 Reinforcement Learning in Autonomous Network Management 21511.4.1 Essential Guidelines for Mastering Reinforcement Learning 21611.4.2 Adaptive Decision-Making in Dynamic Environments 21711.4.2.1 Time-Based Learning and the Trade-Off Between Exploration and Exploitation 21711.4.2.2 Dynamic Approaches to State Representation and Policy Adaptation 21811.4.3 Case Studies on Autonomous Network Management 21811.5 Applications of Cognitive Cellular Networks 21911.5.1 Upgraded Mobile Broadband 22011.5.2 Massive Machine-Type Communication 22011.5.3 Ultra-reliable Low-Latency Communication 22111.5.4 Use Cases and Practical Implementations 22111.6 Challenges and Future Directions 22211.6.1 Scalability and Standardization 22211.6.2 Future Trends in Cognitive Cellular Networks 22211.7 Conclusion 223References 22412 Enhancing Scalability and Performance in Networked Applications Through Smart Computing Resource Allocation 227Araddhana Arvind Deshmukh, Shailesh Pramod Bendale, Sheela Hundekari, Abhijit Chitre, Kirti Wanjale, Amol Dhumane, Garima Chopra, and Shalli Rani12.1 Introduction 22712.1.1 Scope and Objectives 22912.1.2 Objectives 22912.1.2.1 Key Goals of This Study 22912.2 Foundations of Smart Computing Resource Allocation 23012.2.1 Key Concepts in Resource Allocation 23212.2.1.1 Dynamic Resource Allocation 23212.2.1.2 Artificial Intelligence (AI) in Resource Management 23212.2.1.3 Using Real-Time Analytics to Track Performance 23212.2.1.4 Scalability and Elasticity Measures 23212.2.1.5 Mechanisms of Adaptive Learning 23312.2.1.6 Security-Driven Resource Allocation 23312.2.2 The Evolution of Scalability and Performance in Networked Applications 23312.2.2.1 Conventional Static Resource Allocation 23312.2.2.2 The Arise of Scalability Issues 23312.2.2.3 The Cloud Paradigm and Dynamic Resource Allocation 23412.2.2.4 Using Smart Computing to Allocate Intelligent Resources 23412.2.2.5 Real-Time Adaptation and Predictive Scaling 23412.2.2.6 Scalability Beyond Traditionally Assigned Limitations 23412.2.2.7 Automation and Autonomy’s Role 23412.3 Dynamic Resource Allocation Strategies 23512.3.1 Static vs. Dynamic Resource Allocation 23712.3.1.1 Static Resource Allocation 23712.3.1.2 Dynamic Resource Allocation 23712.3.2 Adaptive Resource Allocation Algorithms 23712.3.3 Machine Learning Approaches in Resource Allocation 23812.4 Intelligent Load Balancing Techniques 23812.4.1 Load Balancing in Networked Environments 23912.4.2 Importance of Load Balancing in Scalability 24012.4.2.1 Load Balancing with Machine Learning 24012.4.2.2 Adaptive Load Balancing Algorithms 24012.5 Real-Time Monitoring and Feedback Mechanisms 24112.5.1 Proactive Monitoring for Allocation of Resources 24112.5.2 Decision-Making and Feedback Loops 24112.5.3 Real-Time Monitoring 24212.6 Case Studies and Best Practices 24312.6.1 Cloud-Based Resource Allocation 24312.6.2 Edge Computing and Resource Optimization 24312.6.3 High-Performance Computing (HPC) Environments 24412.7 Security and Privacy Considerations 24412.7.1 Ensuring Security in Resource Allocation 24412.7.1.1 Overview of Security 24412.7.2 Privacy Issues with Wise Resource Distribution 24512.7.2.1 Overview of Privacy 24512.7.3 Balancing Security and Performance 24512.7.3.1 Understanding the Art of Balancing Responsibilities 24512.8 Future Trends and Emerging Technologies 24612.8.1 Resource Allocation and Edge AI 24612.8.1.1 Understanding the Basics of Edge AI 24612.8.2 Implications for Quantum Computing 24612.8.2.1 A Comprehensive Look at the World of Quantum Computing 24612.8.3 Allocating Resources with Blockchain 24712.8.3.1 Overview of Blockchain 24712.9 Conclusion 248References 24813 5G-Enabled Fusion: Navigating the Future Landscape of Cloud Computing, Internet of Things, and Recommender Systems 251Sheetal Sharma13.1 Basics of Cloud Computing 25113.2 Internet of Things 25413.3 5G Technology 25713.4 Recommender System 25813.5 Conclusion 262References 26214 Confluence of Cellular IoT and Data Science for Smart Application using 5G 267Shruti and Shalli Rani14.1 Introduction 26714.2 Data Science and Cellular IoT 27014.3 Research Problems in Data Science for Cellular IoT 27214.4 Sensors in Cellular IoT Smart Farming 27314.5 Related Work 27514.6 Data Science for Agriculture 27714.7 Challenges Faced by Cellular IoT Application in Data Science 27814.8 Proposed Model and its Discussion 28014.9 Conclusion 281References 282Index 285

Garima Chopra, PhD, is an Assistant Professor with Chitkara University Institute of Engineering & Technology at Chitkara University, Punjab, India. Suhaib Ahmed, PhD, is an Assistant Professor with Model Institute of Engineering and Technology, Jammu, J&K, India. Shalli Rani, PhD, is a Professor with Chitkara University Institute of Engineering & Technology at Chitkara University, Punjab, India.

• About the Editors xviiList of Contributors xixPreface xxvGlossary xxviiIntroduction xxix1 Spectrum Sharing Schemes for 5G and Beyond in Wireless Communication 1Aditya Bakshi, Akhil Gupta, and Arushi Pandey1.1 Introduction 11.1.1 Motivation 21.1.2 Literature Review 21.2 Spectrum Sharing Technologies 61.2.1 Machine Learning in Spectrum Sharing 71.2.2 Cooperative and Cognitive Radio Networks 91.2.2.1 Integration of Cooperative and Cognitive Radio Networks 101.2.3 Interference Mitigation Strategies 101.3 Case Study and Performance Evaluation 121.4 Future Trends and Challenges 141.4.1 Challenges Facing Wireless Communication 151.5 Conclusion 16References 172 Synergizing 5G, IoT, and Deep Learning: Pioneering Technological Integration for a Connected Future 21Ankita Sharma and Shalli Rani2.1 Introduction 212.2 Security Threats on 5G Network 222.3 Applications of 5G 242.4 Advanced Intrusion Detection Systems (IDS) 252.5 Integration of 5G-IoT-DL 252.6 Security Challenges 262.7 Role of ML and DL in 5G at Application and Infra Level 272.8 Conclusion 29References 293 Driving Next Generation IoT with 5G and Beyond 33Shishir Shrivastava, Ankita Rana, and Ashu Taneja3.1 Introduction 333.2 Need for Technological Advancement 353.3 Existing Wireless Technologies 353.4 Challenges in Existing Technologies 373.5 Towards 5G Communication 393.5.1 MIMO and Massive MIMO 393.5.2 Millimeter Wave (mmWave) Communication 423.5.3 Small Cells 433.5.4 Visible Light Communication 443.6 IoT and its Evolution 453.7 Role of 5G in IoT 463.8 Integration of 5G IoT with Other Technologies 473.8.1 Ai/ml 503.8.2 Cloud Computing 503.8.3 Fog Computing 513.8.4 Digital Twin 523.8.4.1 Digital Twin Lifecycle: From Data to Transformation 533.9 Techniques to Improve the Performance of Wireless Networks 553.10 Performance Parameters of Next Generation Wireless Systems 583.10.1 The Elaborate Rhythm of Performance Indicators 603.11 Challenges and Future Directions 603.12 Conclusion 61References 624 Emerging Communication Paradigms for 6G IoT: Challenges and Opportunities 65Aditya Soni, Ashu Taneja, Neeti Taneja, and Laith Abualigah4.1 Introduction 654.1.1 Breakthrough 6G Technologies 684.1.1.1 Holographic MIMO (Multiple Input Multiple Output) 684.1.1.2 Intelligent Reflecting Surfaces (IRSs) 684.1.1.3 Cell free Massive MIMO 694.1.1.4 Edge Computing 704.1.1.5 Terahertz (THz) Communication 704.1.1.6 Quantum Communication 714.2 Internet-of-Things and its Evolution 714.2.1 Role of 6G IoT 714.2.2 6G IoT Framework 724.3 Enabling 6G Technologies for IoT 734.3.1 Convergence with Other Key Technologies 754.3.1.1 Advancing Beyond Sub-6 GHz Towards THz Communication 764.3.1.2 Artificial Intelligence and Advanced Machine Learning 764.3.1.3 Compressive Sensing 764.3.1.4 Blockchain/Distributed Ledger Technology 774.3.1.5 Digital Twin 774.3.1.6 Intelligent Edge Computing 774.3.1.7 Dynamic Network Slicing 784.3.1.8 Big Data Analytics 784.3.1.9 Wireless Information and Power Transfer (WIPT) 784.3.1.10 Backscatter Communication 794.3.1.11 Communication-Computing-Control Convergence 794.4 Use Case Scenarios 804.4.1 Smart Healthcare 804.4.2 Smart Transportation 814.4.3 Smart Manufacturing 824.4.4 Smart Agriculture 834.4.5 Smart Classrooms 834.4.6 Smart Cities 844.5 Challenges Faced and the Solutions Offered 854.6 Conclusion 86References 875 Securing the Internet of Things: Cybersecurity Challenges, Strategies, and Future Directions in the Era of 5G and Edge Computing 89Geetanshi, Harshit Manocha, Himanshi Babbar, and Cherry Mangla5.1 Introduction 895.1.1 History of IoT and Edge Computing in 5G 945.2 Literature Review 955.3 Applications in IoT and Edge Computing 955.4 Cybersecurity Management System for IoT Environments 975.4.1 Security Layers 975.5 Current Cyber Security Strategies in IoT 995.6 IoT Cybersecurity’s Role in Reshaping Machine Learning 1005.6.1 Role of IoT in Artificial Intelligence 1015.7 Real Life Scenario 1025.8 Conclusions 105References 1056 Autonomous Systems for 5G Networks: A Comprehensive Analysis of Features Toward Generalization and Adaptability 107Durga Shankar Baggam and Shalli Rani6.1 Introduction 1076.2 Survey Method 1096.3 Background and Related Works 1136.3.1 Autonomous System Architecture 1146.3.1.1 Application Layer 1206.3.1.2 Cognitive Layer 1206.3.1.3 Perception Layer 1206.3.1.4 Physical Layer 1206.3.2 Sensors 1216.3.3 Artificial Intelligence Techniques 1216.3.4 Intelligent Transport System (ITS) 1246.3.5 B5G-Based Vehicular Telecommunication 1256.4 Discussion 1266.4.1 Environmental Uncertainties 1286.4.2 Security Challenges and Counter Measures 1296.5 Conclusion 129References 1307 Integrated Trends, Opportunities, and Challenges of 5G and Internet of Things 139Ekta Dixit and Shalli Rani7.1 Introduction 1397.1.1 Overview of 5G 1407.1.2 Evolution from 1G to 5G 1417.1.3 5G Architecture 1417.1.4 Overview of IoT 1437.1.5 Features of IoT 1437.1.5.1 Avalability 1437.1.5.2 Mobility 1437.1.5.3 Scalabilty 1437.1.5.4 Security 1447.1.5.5 Context Awareness 1447.1.6 IoT Architecture 1447.1.6.1 Application Layer 1447.1.6.2 Network Layer 1447.1.6.3 Edge Layer 1457.2 Requirements for Integration of 5G with IoT 1457.2.1 Integrated 5G IoT Layered Architecture 1457.3 Opportunities of 5G integrated IoT 1467.3.1 Smart Cities 1467.3.2 Smart Vehicles 1467.3.3 Device to Device Communications 1477.3.4 Business 1477.3.5 Satelite and Aerial Research 1477.3.6 Video Surveillance 1477.4 Challenges of 5G Integrated IoT 1477.4.1 Insufficient Control over Data Storage and Usage 1487.4.2 Scalability 1487.4.3 Heterogeneity of 5G and IoT Data 1487.4.4 Blockchain Processing Time 1487.4.5 5G mm-Wave Issues 1497.4.6 Threat Protection of 5G IoT 1497.5 Conclusion 149References 1508 Advancement in Resource Allocation for Future Generation of Communications 153Garima Chopra and Suhaib Ahmed8.1 Introduction 1538.2 Current Trends in Multiple Access Techniques 1548.3 Scheduling Algorithms for 5G/Beyond 5G 1558.4 Factors Influencing Scheduling Algorithms 1588.5 Resource Allocation for 5G Ultra-Dense Networks 1608.6 Conclusion 162References 1629 Next-Gen Networked Healthcare: Requirements and Challenges 165Kanica Sachdev and Brejesh Lall9.1 Introduction 1659.2 Applications 1669.2.1 Remote Robotic-Assisted Surgery 1679.2.2 Remote Diagnosis and Teleconsultation 1679.2.3 In-Ambulance Treatment 1689.2.4 Remote Patient Monitoring 1699.2.5 Medical Big Data Management 1709.2.6 Augmented Reality (AR) and Virtual Reality (VR) 1709.2.7 Emergency Response Strategies 1719.3 Technological Prerequisites 1729.4 Challenges in 5G Integration in Healthcare 1759.5 Conclusion 177References 18010 Dynamic Resource Orchestration for Computing, Data, and IoT in Networked Systems: A Data-Centric Approach 185Suresh Limkar, Mohammad Alamgir Hossain, Sherif Tawfik Amin, and Yasir Ahmad10.1 Introduction 18510.1.1 Motivation 18710.1.2 Objectives 18710.2 Dynamic Resource Orchestration: Foundations 18710.2.1 Resource Orchestration Concepts 18710.2.2 Dynamic Resource Orchestration’s Evolution 18810.2.3 Importance of a Data-Centric Perspective 18810.3 Computing in Networked Systems 18910.3.1 Cloud Computing Paradigm 18910.3.2 Edge Computing and Fog Computing 19110.3.3 Integration of Computing Resources 19210.4 Data-Centric Orchestration 19310.4.1 Data-Driven Resource Allocation 19310.4.1.1 Data-Driven Decision-Making 19310.4.1.2 Dynamic Scaling 19410.4.1.3 Perceptive Formulas 19410.4.1.4 Customization and Adaptability 19410.4.2 Data Processing and Management 19410.4.2.1 Data Locality and Optimization 19410.4.2.2 Techniques for Data Movement 19410.4.2.3 Data Lifecycle Management 19410.4.2.4 AI and Data Analytics Integration 19510.4.3 Security and Privacy Considerations 19510.4.3.1 Completely Encryption 19510.4.3.2 Identity and Access Management 19510.4.3.3 Safe Data Processing 19510.4.3.4 Regulatory Standard Compliance 19510.4.3.5 Privacy-Preserving Techniques 19510.4.3.6 Audit Trails and Monitoring 19610.5 IoT Integration 19610.5.1 Overview of IoT Architecture 19610.5.2 IoT Resource Orchestration Challenges 19710.5.2.1 Device Heterogeneity 19710.5.2.2 Scalability and Data Volume 19710.5.2.3 Low-Latency and Real-Time Processing 19710.5.2.4 Compatibility and Standards 19710.5.3 Combining Data and Computing 19710.5.3.1 Data-Centric Orchestration 19810.5.3.2 IoT with Machine Learning and AI 19810.5.3.3 Dynamic Resource Allocation 19810.5.3.4 IoT Security Measures 19910.6 Methodologies for Dynamic Resource Orchestration 20010.6.1 Methods of Machine Learning 20010.6.1.1 Overview of Machine Learning for Resource Management 20010.6.1.2 Predictive Resource 20010.6.1.3 Fault Prediction and Anomaly Detection 20010.6.2 Methods of Optimisation 20110.6.2.1 Introducing Resource Orchestration’s Optimisation Techniques 20110.6.3 Hybrid Models 20110.6.3.1 Optimisation Through Machine Learning Hybrids 20110.6.3.2 Combining Rule-Based and Learning-Based Methods: Advancing Hybrid Approaches 20110.6.3.3 Continual Enhancement Through Responsive Feedback Mechanisms 20210.6.3.4 Harnessing the Power of Adaptive Model Switching 20210.7 Case Studies 20210.7.1 Practical Applications 20210.7.1.1 Aws 20210.7.1.2 Autoscaling of Kubernetes Horizontal Pods 20210.7.2 Achievements and Insights Acquired 20310.7.2.1 Netflix: Using Machine Learning to Deliver Content 20310.7.2.2 Google’s Expansion of Kubernetes: Enhancing Scalability 20310.7.2.3 Achieving Dynamic Scalability with AWS Auto Scaling: An Airbnb Success Story 20310.8 Conclusion 204References 20411 Cognitive Cellular Networks: Empowering Future Connectivity Through Artificial Intelligence 209Mohammad Alamgir Hossain, Suresh Limkar, Sherif Tawfik Amin, and Yasir Ahmad11.1 Introduction 20911.1.1 Background 20911.1.2 Key Objectives of the Chapter 21011.2 Foundations of Cognitive Cellular Networks 21111.2.1 Architecture of Cellular Networks 21111.2.2 Radio Technologies Induced by Cognition 21111.2.3 Artificial Intelligence Integration 21211.3 AI Algorithms for Network Optimization 21311.3.1 Machine Learning Models for Predictive Analysis 21311.3.1.1 Machine Learning in Resource Allocation 21311.3.1.2 Predictive Analytics for Traffic Management 21311.3.1.3 Reinforcement Learning for Self-Optimizing Networks 21311.3.1.4 Anomaly Detection to Strengthen Security 21411.3.1.5 Artificial Neural Networks for Dynamic Optimization 21411.3.1.6 Combining Genetic Algorithms with Cross-Layer Optimization 21411.3.2 Spectrum Utilization and Management 21411.3.2.1 Dynamic Spectrum Access 21411.3.2.2 Brain CRT 21511.3.2.3 Enhancing Spectrum Management with AI-Powered Solutions to Combat Interference 21511.3.2.4 Achieving Regulatory Compliance in Spectrum Sharing 21511.4 Reinforcement Learning in Autonomous Network Management 21511.4.1 Essential Guidelines for Mastering Reinforcement Learning 21611.4.2 Adaptive Decision-Making in Dynamic Environments 21711.4.2.1 Time-Based Learning and the Trade-Off Between Exploration and Exploitation 21711.4.2.2 Dynamic Approaches to State Representation and Policy Adaptation 21811.4.3 Case Studies on Autonomous Network Management 21811.5 Applications of Cognitive Cellular Networks 21911.5.1 Upgraded Mobile Broadband 22011.5.2 Massive Machine-Type Communication 22011.5.3 Ultra-reliable Low-Latency Communication 22111.5.4 Use Cases and Practical Implementations 22111.6 Challenges and Future Directions 22211.6.1 Scalability and Standardization 22211.6.2 Future Trends in Cognitive Cellular Networks 22211.7 Conclusion 223References 22412 Enhancing Scalability and Performance in Networked Applications Through Smart Computing Resource Allocation 227Araddhana Arvind Deshmukh, Shailesh Pramod Bendale, Sheela Hundekari, Abhijit Chitre, Kirti Wanjale, Amol Dhumane, Garima Chopra, and Shalli Rani12.1 Introduction 22712.1.1 Scope and Objectives 22912.1.2 Objectives 22912.1.2.1 Key Goals of This Study 22912.2 Foundations of Smart Computing Resource Allocation 23012.2.1 Key Concepts in Resource Allocation 23212.2.1.1 Dynamic Resource Allocation 23212.2.1.2 Artificial Intelligence (AI) in Resource Management 23212.2.1.3 Using Real-Time Analytics to Track Performance 23212.2.1.4 Scalability and Elasticity Measures 23212.2.1.5 Mechanisms of Adaptive Learning 23312.2.1.6 Security-Driven Resource Allocation 23312.2.2 The Evolution of Scalability and Performance in Networked Applications 23312.2.2.1 Conventional Static Resource Allocation 23312.2.2.2 The Arise of Scalability Issues 23312.2.2.3 The Cloud Paradigm and Dynamic Resource Allocation 23412.2.2.4 Using Smart Computing to Allocate Intelligent Resources 23412.2.2.5 Real-Time Adaptation and Predictive Scaling 23412.2.2.6 Scalability Beyond Traditionally Assigned Limitations 23412.2.2.7 Automation and Autonomy’s Role 23412.3 Dynamic Resource Allocation Strategies 23512.3.1 Static vs. Dynamic Resource Allocation 23712.3.1.1 Static Resource Allocation 23712.3.1.2 Dynamic Resource Allocation 23712.3.2 Adaptive Resource Allocation Algorithms 23712.3.3 Machine Learning Approaches in Resource Allocation 23812.4 Intelligent Load Balancing Techniques 23812.4.1 Load Balancing in Networked Environments 23912.4.2 Importance of Load Balancing in Scalability 24012.4.2.1 Load Balancing with Machine Learning 24012.4.2.2 Adaptive Load Balancing Algorithms 24012.5 Real-Time Monitoring and Feedback Mechanisms 24112.5.1 Proactive Monitoring for Allocation of Resources 24112.5.2 Decision-Making and Feedback Loops 24112.5.3 Real-Time Monitoring 24212.6 Case Studies and Best Practices 24312.6.1 Cloud-Based Resource Allocation 24312.6.2 Edge Computing and Resource Optimization 24312.6.3 High-Performance Computing (HPC) Environments 24412.7 Security and Privacy Considerations 24412.7.1 Ensuring Security in Resource Allocation 24412.7.1.1 Overview of Security 24412.7.2 Privacy Issues with Wise Resource Distribution 24512.7.2.1 Overview of Privacy 24512.7.3 Balancing Security and Performance 24512.7.3.1 Understanding the Art of Balancing Responsibilities 24512.8 Future Trends and Emerging Technologies 24612.8.1 Resource Allocation and Edge AI 24612.8.1.1 Understanding the Basics of Edge AI 24612.8.2 Implications for Quantum Computing 24612.8.2.1 A Comprehensive Look at the World of Quantum Computing 24612.8.3 Allocating Resources with Blockchain 24712.8.3.1 Overview of Blockchain 24712.9 Conclusion 248References 24813 5G-Enabled Fusion: Navigating the Future Landscape of Cloud Computing, Internet of Things, and Recommender Systems 251Sheetal Sharma13.1 Basics of Cloud Computing 25113.2 Internet of Things 25413.3 5G Technology 25713.4 Recommender System 25813.5 Conclusion 262References 26214 Confluence of Cellular IoT and Data Science for Smart Application using 5G 267Shruti and Shalli Rani14.1 Introduction 26714.2 Data Science and Cellular IoT 27014.3 Research Problems in Data Science for Cellular IoT 27214.4 Sensors in Cellular IoT Smart Farming 27314.5 Related Work 27514.6 Data Science for Agriculture 27714.7 Challenges Faced by Cellular IoT Application in Data Science 27814.8 Proposed Model and its Discussion 28014.9 Conclusion 281References 282Index 285