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Spectrum sharing in cognitive radio networks : towards highly connected environments / Prabhat Thakur and Ghanshyam Singh, University of Johannesburg Auckland Park, Johannesburg, South Africa.

By: Thakur, Prabhat [author.].
Contributor(s): Singh, Ghanshyam [author.].
Material type: materialTypeLabelBookPublisher: Hoboken : Wiley, 2021Edition: First edition.Description: 1 online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781119665441; 1119665442; 9781119665434; 1119665434; 9781119665458; 1119665450.Subject(s): Radio resource management (Wireless communications) | Cognitive radio networks | Cognitive radio networks | Radio resource management (Wireless communications)Genre/Form: Electronic books.Additional physical formats: Print version:: Spectrum sharing in cognitive radio networksDDC classification: 621.384 Online resources: Wiley Online Library
Contents:
Preface xiii -- Special Acknowledgements xxi -- List of Acronyms xxiii -- List of Figures xxvii -- List of Tables xxxiii -- List of Symbols xxxv -- 1 Introduction 1 -- 1.1 Introduction 1 -- 1.1.1 Connected Environments 2 -- 1.1.2 Evolution of Wireless Communication 5 -- 1.1.3 Third Generation Partnership Project 10 -- 1.2 Cognitive Radio Technology 10 -- 1.2.1 Spectrum Accessing/Sharing Techniques 13 -- 1.2.1.1 Interweave Spectrum Access 14 -- 1.2.1.2 Underlay Spectrum Access 17 -- 1.2.1.3 Overlay Spectrum Access 17 -- 1.2.1.4 Hybrid Spectrum Access 17 -- 1.3 Implementation of CR Networks 20 -- 1.4 Motivation 22 -- 1.5 Organization of Book 23 -- 1.6 Summary 27 -- References 27 -- 2 Advanced Frame Structures in Cognitive Radio Networks 39 -- 2.1 Introduction 39 -- 2.2 Related Work 40 -- 2.2.1 Frame Structures 40 -- 2.2.2 Spectrum Accessing Strategies 41 -- 2.3 Proposed Frame Structures for HSA Technique 43 -- 2.4 Analysis of Throughput and Data Loss 45 -- 2.5 Simulations and Results 47 -- 2.6 Summary 50 -- References 51 -- 3 Cognitive Radio Network with Spectrum Prediction and Monitoring -- Techniques 55 -- 3.1 Introduction 55 -- 3.2 Related Work 57 -- 3.2.1 Spectrum Prediction 57 -- 3.2.2 Spectrum Monitoring 58 -- 3.3 System Models 59 -- 3.3.1 System Model for Approach-1 59 -- 3.3.2 System Model for Approach-2 60 -- 3.4 Performance Analysis 61 -- 3.4.1 Throughput Analysis Using Approach-1 61 -- 3.4.2 Analysis of Performance Metrics of the Approach-2 65 -- 3.5 Results and Discussion 67 -- 3.5.1 Proposed Approach-1 67 -- 3.5.2 Proposed Approach-2 69 -- 3.6 Summary 72 -- References 72 -- 4 Effect of Spectrum Prediction in Cognitive Radio Networks 77 -- 4.1 Introduction 77 -- 4.1.1 Spectrum Access Techniques 78 -- 4.2 System Model 80 -- 4.3 Throughput Analysis 87 -- 4.4 Simulation Results and Discussion 89 -- 4.5 Summary 93 -- References 94 -- 5 Effect of Imperfect Spectrum Monitoring on Cognitive Radio -- Networks 97 -- 5.1 Introduction 97 -- 5.2 Related Work 99 -- 5.2.1 Spectrum Sensing 99 -- 5.2.2 Spectrum Monitoring 100 -- 5.3 System Model 101 -- 5.4 Performance Analysis of Proposed System Using Imperfect Spectrum -- Monitoring 102 -- 5.4.1 Computation of Ratio of the Achieved Throughput to Data Loss 108 -- 5.4.2 Computation of Power Wastage 108 -- 5.4.3 Computation of Interference Efficiency 109 -- 5.4.4 Computation of Energy Efficiency 109 -- 5.5 Results and Discussion 110 -- 5.6 Summary 115 -- References 116 -- 6 Cooperative Spectrum Monitoring in Homogeneous and -- Heterogeneous Cognitive Radio Networks 121 -- 6.1 Introduction 121 -- 6.2 Background 122 -- 6.3 System Model 124 -- 6.4 Performance Analysis of Proposed CRN 126 -- 6.4.1 Computation of Achieved Throughput and Data Loss 130 -- 6.4.2 Computation of Interference Efficiency 131 -- 6.4.3 Computation of Energy Efficiency 131 -- 6.5 Results and Discussion 132 -- 6.5.1 Homogeneous Cognitive Radio Network 132 -- 6.5.2 Heterogeneous Cognitive Radio Networks 134 -- 6.6 Summary 143 -- References 143 -- 7 Spectrum Mobility in Cognitive Radio Networks Using Spectrum -- Prediction and Monitoring Techniques 147 -- 7.1 Introduction 147 -- 7.2 System Model 151 -- 7.3 Performance Analysis 153 -- 7.4 Results and Discussion 156 -- 7.5 Summary 162 -- References 163 -- 8 Hybrid Self-Scheduled Multichannel Medium Access Control Protocol -- in Cognitive Radio Networks 167 -- 8.1 Introduction 167 -- 8.2 Related Work 169 -- 8.2.1 CR-MAC Protocols 169 -- 8.2.2 Interference at PU 171 -- 8.3 System Model and Proposed Hybrid Self-Scheduled Multichannel -- MAC Protocol 172 -- 8.3.1 System Model 172 -- 8.3.2 Proposed HSMC-MAC Protocol 173 -- 8.4 Performance Analysis 174 -- 8.4.1 With Perfect Spectrum Sensing 176 -- 8.4.2 With Imperfect Spectrum Sensing 178 -- 8.4.3 More Feasible Scenarios 180 -- 8.5 Simulations and Results Analysis 182 -- 8.5.1 With Perfect Spectrum Sensing 182 -- 8.5.2 With Imperfect Spectrum Sensing 185 -- 8.6 Summary 190 -- References 190 -- 9 Frameworks of Non-Orthogonal Multiple Access Techniques in -- Cognitive Radio Networks 195 -- 9.1 Introduction 195 -- 9.1.1 Related Work 196 -- 9.1.2 Motivation 199 -- 9.1.3 Organization 199 -- 9.2 CR Spectrum Accessing Strategies 199 -- 9.3 Functions of NOMA System for Uplink and Downlink Scenarios 204 -- 9.3.1 Downlink Scenario for Cellular-NOMA 204 -- 9.3.2 Uplink Scenario for Cellular-NOMA 207 -- 9.4 Proposed Frameworks of CR with NOMA 208 -- 9.4.1 Framework-1 209 -- 9.4.2 Framework-2 210 -- 9.5 Simulation Environment and Results 212 -- 9.6 Research Potentials for NOMA and CR-NOMA Implementations 213 -- 9.6.1 Imperfect CSI 214 -- 9.6.2 Spectrum Hand-off Management 215 -- 9.6.3 Standardization 215 -- 9.6.4 Less Complex and Cost-Effective Systems 215 -- 9.6.5 Energy-Efficient Design and Frameworks 216 -- 9.6.6 Quality-of-Experience Management 216 -- 9.6.7 Power Allocation Strategy for CUs to Implement NOMA Without -- Interfering PU 217 -- 9.6.8 Cooperative CR-NOMA 217 -- 9.6.9 Interference Cancellation Techniques 217 -- 9.6.10 Security Aspects in CR-NOMA 218 -- 9.6.11 Role of User Clustering and Challenges 218 -- 9.6.12 Wireless Power Transfer to NOMA 219 -- 9.6.13 Multicell NOMA with Coordinated Multipoint Transmission 220 -- 9.6.14 Multiple-Carrier NOMA 221 -- 9.6.15 Cross-Layer Design 221 -- 9.6.16 MIMO-NOMA-CR 222 -- 9.7 Summary 222 -- References 223 -- 10 Performance Analysis of MIMO-Based CR-NOMA Communication -- Systems 229 -- 10.1 Introduction 229 -- 10.2 Related Work for Several Combinations of CR, NOMA, and MIMO -- Systems 231 -- 10.3 System Model 234 -- 10.3.1 Downlink Scenarios 236 -- 10.3.2 Uplink Scenario 238 -- 10.4 Performance Analysis 238 -- 10.4.1 Downlink Scenario 238 -- 10.4.1.1 Throughput Computation for MIMO-CR-NOMA 239 -- 10.4.1.2 Throughput Computation for CR-NOMA Systems 240 -- 10.4.1.3 Sum Throughput for CR-OMA, CR-NOMA, CR-MIMO, and -- CR-NOMA-MIMO Frameworks 240 -- 10.4.2 Uplink Scenario 241 -- 10.4.2.1 Throughput Computation for MIMO-CR-NOMA 241 -- 10.4.2.2 Throughput Calculation for CR-NOMA Systems 242 -- 10.4.2.3 Sum Throughput for CR-OMA, CR-NOMA, CR-MIMO, and -- CR-NOMA-MIMO Frameworks 242 -- 10.4.2.4 Computation of Interference Efficiency of CU-4 In Case of -- CR-MIMO-NOMA 243 -- 10.5 Simulation and Results Analysis 243 -- 10.5.1 Simulation Results for Downlink Scenario 243 -- 10.5.2 Simulation Results for Uplink Scenario 245 -- 10.6 Summary 249 -- References 250 -- 11 Interference Management in Cognitive Radio Networks 255 -- 11.1 Introduction 255 -- 11.1.1 White space 257 -- 11.1.2 Grey Spaces 257 -- 11.1.3 Black Spaces 257 -- 11.1.4 Interference Temperature 257 -- 11.2 Interfering and Non-interfering CRN 258 -- 11.2.1 Interfering CRN 258 -- 11.2.2 Non-Interfering CRN 259 -- 11.3 Interference Cancellation Techniques in the CRN 261 -- 11.3.1 At the CU Transmitter 261 -- 11.3.2 At the CR-Receiver 264 -- 11.4 Cross-Layer Interference Mitigation in Cognitive Radio Networks 268 -- 11.5 Interference Management in Cognitive Radio Networks via Cognitive -- Cycle Constituents 269 -- 11.5.1 Spectrum Sensing 269 -- 11.5.2 Spectrum Prediction 269 -- 11.5.3 Transmission Below PUs' Interference Tolerable Limit 271 -- 11.5.4 Using Advanced Encoding Techniques 271 -- 11.5.5 Spectrum Monitoring 272 -- 11.6 Summary 274 -- References 274 -- 12 Simulation Frameworks and Potential Research Challenges for -- Internet-of-Vehicles Networks 281 -- 12.1 Introduction 281 -- 12.1.1 Consumer IoT 283 -- 12.1.2 Industrial IoT 283 -- 12.2 Applications of CIoT 284 -- 12.2.1 Smart Home and Automation 284 -- 12.2.2 Smart Wearables 284 -- 12.2.3 Home Security and Smart Domestics 285 -- 12.2.4 Smart Farming 285 -- 12.3 Applications of Industrial IoT 285 -- 12.3.1 Smart Industry 286 -- 12.3.2 Smart Grid/Utilities 286 -- 12.3.3 Smart Communication 286 -- 12.3.4 Smart City 287 -- 12.3.5 Smart Energy Management 287 -- 12.3.6 Smart Retail Management 288 -- 12.3.7 Robotics 288 -- 12.3.8 Smart Cars/Connected Vehicles 289 -- 12.4 Communication Frameworks for IoVs 289 -- 12.4.1 Vehicle-to-Vehicle (V2V) Communication 291 -- 12.4.2 Vehicle to Infrastructure (V2I) Communication 292 -- 12.4.3 Infrastructure to Vehicles (I2V) Communication 293 -- 12.4.4 Vehicle-to-Broadband (V2B) Communication 293 -- 12.4.5 Vehicle-to-Pedestrians (V2P) Communication 293 -- 12.5 Simulation Environments for Internet-of-Vehicles 295 -- 12.5.1 SUMO 296 -- 12.5.2 Network Simulator (NetSim) 296 -- 12.5.3 Ns-2 297 -- 12.5.4 Ns-3 297 -- 12.5.5 OMNeT++ 298 -- 12.6 Potential
Research Challenges 299 -- 12.6.1 Social Challenges 299 -- 12.6.2 Technical Challenges 300 -- 12.7 Summary 302 -- References 302 -- 13 Radio Resource Management in Internet-of-Vehicles 311 -- 13.1 Introduction 311 -- 13.1.1 Dedicated Short-Range Communication 313 -- 13.1.2 Wireless Access for Vehicular Environments 314 -- 13.1.3 Communication Access for Land Mobile (CALM) 314 -- 13.2 Cellular Communication 315 -- 13.2.1 3GPP Releases 315 -- 13.2.2 Long-Term Evolution 317 -- 13.2.3 New Radio 317 -- 13.2.4 Dynamic Spectrum Access 318 -- 13.3 Role of Cognitive Radio for Spectrum Management 319 -- 13.4 Effect of Mobile Nature of Vehicles/Nodes on the Networking 320 -- 13.5 Spectrum Sharing in IoVs 322 -- 13.5.1 Spectrum Sensing Scenarios 322 -- 13.5.2 Spectrum Sharing Scenarios 324 -- 13.5.3 Spectrum Mobility/Handoff Scenarios 325 -- 13.6 Frameworks of Vehicular Networks with Cognitive Radio 326 -- 13.6.1 CR-Based IoVs Networks Architecture 327 -- 13.7 Key Potentials and Research Challenges 328 -- 13.7.1 Key Potentials 328 -- 13.7.2 Research Challenges 329 -- 13.8 Summary 333 -- References 333 -- Index 000.
Summary: "The wireless communication world exemplifies the swift transformation towards 5th generation cellular networks, the rapid shift from user-centric to device-centric communication which has created a tremendous impact on service complexity and network requirements. The forthcoming networks present essential demand of ubiquitous throughput, low-latency, and high-reliability and are also intended to provide energy efficiency, spectrum reuse, network scalability, and robustness as well as improved quality of user experience, which proves to be of ultimate importance. Therefore, the government, academic, and industrial institutions are working together to fulfil these challenging issues"-- Provided by publisher.
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Includes bibliographical references and index.

"The wireless communication world exemplifies the swift transformation towards 5th generation cellular networks, the rapid shift from user-centric to device-centric communication which has created a tremendous impact on service complexity and network requirements. The forthcoming networks present essential demand of ubiquitous throughput, low-latency, and high-reliability and are also intended to provide energy efficiency, spectrum reuse, network scalability, and robustness as well as improved quality of user experience, which proves to be of ultimate importance. Therefore, the government, academic, and industrial institutions are working together to fulfil these challenging issues"-- Provided by publisher.

Description based on print version record and CIP data provided by publisher; resource not viewed.

Preface xiii -- Special Acknowledgements xxi -- List of Acronyms xxiii -- List of Figures xxvii -- List of Tables xxxiii -- List of Symbols xxxv -- 1 Introduction 1 -- 1.1 Introduction 1 -- 1.1.1 Connected Environments 2 -- 1.1.2 Evolution of Wireless Communication 5 -- 1.1.3 Third Generation Partnership Project 10 -- 1.2 Cognitive Radio Technology 10 -- 1.2.1 Spectrum Accessing/Sharing Techniques 13 -- 1.2.1.1 Interweave Spectrum Access 14 -- 1.2.1.2 Underlay Spectrum Access 17 -- 1.2.1.3 Overlay Spectrum Access 17 -- 1.2.1.4 Hybrid Spectrum Access 17 -- 1.3 Implementation of CR Networks 20 -- 1.4 Motivation 22 -- 1.5 Organization of Book 23 -- 1.6 Summary 27 -- References 27 -- 2 Advanced Frame Structures in Cognitive Radio Networks 39 -- 2.1 Introduction 39 -- 2.2 Related Work 40 -- 2.2.1 Frame Structures 40 -- 2.2.2 Spectrum Accessing Strategies 41 -- 2.3 Proposed Frame Structures for HSA Technique 43 -- 2.4 Analysis of Throughput and Data Loss 45 -- 2.5 Simulations and Results 47 -- 2.6 Summary 50 -- References 51 -- 3 Cognitive Radio Network with Spectrum Prediction and Monitoring -- Techniques 55 -- 3.1 Introduction 55 -- 3.2 Related Work 57 -- 3.2.1 Spectrum Prediction 57 -- 3.2.2 Spectrum Monitoring 58 -- 3.3 System Models 59 -- 3.3.1 System Model for Approach-1 59 -- 3.3.2 System Model for Approach-2 60 -- 3.4 Performance Analysis 61 -- 3.4.1 Throughput Analysis Using Approach-1 61 -- 3.4.2 Analysis of Performance Metrics of the Approach-2 65 -- 3.5 Results and Discussion 67 -- 3.5.1 Proposed Approach-1 67 -- 3.5.2 Proposed Approach-2 69 -- 3.6 Summary 72 -- References 72 -- 4 Effect of Spectrum Prediction in Cognitive Radio Networks 77 -- 4.1 Introduction 77 -- 4.1.1 Spectrum Access Techniques 78 -- 4.2 System Model 80 -- 4.3 Throughput Analysis 87 -- 4.4 Simulation Results and Discussion 89 -- 4.5 Summary 93 -- References 94 -- 5 Effect of Imperfect Spectrum Monitoring on Cognitive Radio -- Networks 97 -- 5.1 Introduction 97 -- 5.2 Related Work 99 -- 5.2.1 Spectrum Sensing 99 -- 5.2.2 Spectrum Monitoring 100 -- 5.3 System Model 101 -- 5.4 Performance Analysis of Proposed System Using Imperfect Spectrum -- Monitoring 102 -- 5.4.1 Computation of Ratio of the Achieved Throughput to Data Loss 108 -- 5.4.2 Computation of Power Wastage 108 -- 5.4.3 Computation of Interference Efficiency 109 -- 5.4.4 Computation of Energy Efficiency 109 -- 5.5 Results and Discussion 110 -- 5.6 Summary 115 -- References 116 -- 6 Cooperative Spectrum Monitoring in Homogeneous and -- Heterogeneous Cognitive Radio Networks 121 -- 6.1 Introduction 121 -- 6.2 Background 122 -- 6.3 System Model 124 -- 6.4 Performance Analysis of Proposed CRN 126 -- 6.4.1 Computation of Achieved Throughput and Data Loss 130 -- 6.4.2 Computation of Interference Efficiency 131 -- 6.4.3 Computation of Energy Efficiency 131 -- 6.5 Results and Discussion 132 -- 6.5.1 Homogeneous Cognitive Radio Network 132 -- 6.5.2 Heterogeneous Cognitive Radio Networks 134 -- 6.6 Summary 143 -- References 143 -- 7 Spectrum Mobility in Cognitive Radio Networks Using Spectrum -- Prediction and Monitoring Techniques 147 -- 7.1 Introduction 147 -- 7.2 System Model 151 -- 7.3 Performance Analysis 153 -- 7.4 Results and Discussion 156 -- 7.5 Summary 162 -- References 163 -- 8 Hybrid Self-Scheduled Multichannel Medium Access Control Protocol -- in Cognitive Radio Networks 167 -- 8.1 Introduction 167 -- 8.2 Related Work 169 -- 8.2.1 CR-MAC Protocols 169 -- 8.2.2 Interference at PU 171 -- 8.3 System Model and Proposed Hybrid Self-Scheduled Multichannel -- MAC Protocol 172 -- 8.3.1 System Model 172 -- 8.3.2 Proposed HSMC-MAC Protocol 173 -- 8.4 Performance Analysis 174 -- 8.4.1 With Perfect Spectrum Sensing 176 -- 8.4.2 With Imperfect Spectrum Sensing 178 -- 8.4.3 More Feasible Scenarios 180 -- 8.5 Simulations and Results Analysis 182 -- 8.5.1 With Perfect Spectrum Sensing 182 -- 8.5.2 With Imperfect Spectrum Sensing 185 -- 8.6 Summary 190 -- References 190 -- 9 Frameworks of Non-Orthogonal Multiple Access Techniques in -- Cognitive Radio Networks 195 -- 9.1 Introduction 195 -- 9.1.1 Related Work 196 -- 9.1.2 Motivation 199 -- 9.1.3 Organization 199 -- 9.2 CR Spectrum Accessing Strategies 199 -- 9.3 Functions of NOMA System for Uplink and Downlink Scenarios 204 -- 9.3.1 Downlink Scenario for Cellular-NOMA 204 -- 9.3.2 Uplink Scenario for Cellular-NOMA 207 -- 9.4 Proposed Frameworks of CR with NOMA 208 -- 9.4.1 Framework-1 209 -- 9.4.2 Framework-2 210 -- 9.5 Simulation Environment and Results 212 -- 9.6 Research Potentials for NOMA and CR-NOMA Implementations 213 -- 9.6.1 Imperfect CSI 214 -- 9.6.2 Spectrum Hand-off Management 215 -- 9.6.3 Standardization 215 -- 9.6.4 Less Complex and Cost-Effective Systems 215 -- 9.6.5 Energy-Efficient Design and Frameworks 216 -- 9.6.6 Quality-of-Experience Management 216 -- 9.6.7 Power Allocation Strategy for CUs to Implement NOMA Without -- Interfering PU 217 -- 9.6.8 Cooperative CR-NOMA 217 -- 9.6.9 Interference Cancellation Techniques 217 -- 9.6.10 Security Aspects in CR-NOMA 218 -- 9.6.11 Role of User Clustering and Challenges 218 -- 9.6.12 Wireless Power Transfer to NOMA 219 -- 9.6.13 Multicell NOMA with Coordinated Multipoint Transmission 220 -- 9.6.14 Multiple-Carrier NOMA 221 -- 9.6.15 Cross-Layer Design 221 -- 9.6.16 MIMO-NOMA-CR 222 -- 9.7 Summary 222 -- References 223 -- 10 Performance Analysis of MIMO-Based CR-NOMA Communication -- Systems 229 -- 10.1 Introduction 229 -- 10.2 Related Work for Several Combinations of CR, NOMA, and MIMO -- Systems 231 -- 10.3 System Model 234 -- 10.3.1 Downlink Scenarios 236 -- 10.3.2 Uplink Scenario 238 -- 10.4 Performance Analysis 238 -- 10.4.1 Downlink Scenario 238 -- 10.4.1.1 Throughput Computation for MIMO-CR-NOMA 239 -- 10.4.1.2 Throughput Computation for CR-NOMA Systems 240 -- 10.4.1.3 Sum Throughput for CR-OMA, CR-NOMA, CR-MIMO, and -- CR-NOMA-MIMO Frameworks 240 -- 10.4.2 Uplink Scenario 241 -- 10.4.2.1 Throughput Computation for MIMO-CR-NOMA 241 -- 10.4.2.2 Throughput Calculation for CR-NOMA Systems 242 -- 10.4.2.3 Sum Throughput for CR-OMA, CR-NOMA, CR-MIMO, and -- CR-NOMA-MIMO Frameworks 242 -- 10.4.2.4 Computation of Interference Efficiency of CU-4 In Case of -- CR-MIMO-NOMA 243 -- 10.5 Simulation and Results Analysis 243 -- 10.5.1 Simulation Results for Downlink Scenario 243 -- 10.5.2 Simulation Results for Uplink Scenario 245 -- 10.6 Summary 249 -- References 250 -- 11 Interference Management in Cognitive Radio Networks 255 -- 11.1 Introduction 255 -- 11.1.1 White space 257 -- 11.1.2 Grey Spaces 257 -- 11.1.3 Black Spaces 257 -- 11.1.4 Interference Temperature 257 -- 11.2 Interfering and Non-interfering CRN 258 -- 11.2.1 Interfering CRN 258 -- 11.2.2 Non-Interfering CRN 259 -- 11.3 Interference Cancellation Techniques in the CRN 261 -- 11.3.1 At the CU Transmitter 261 -- 11.3.2 At the CR-Receiver 264 -- 11.4 Cross-Layer Interference Mitigation in Cognitive Radio Networks 268 -- 11.5 Interference Management in Cognitive Radio Networks via Cognitive -- Cycle Constituents 269 -- 11.5.1 Spectrum Sensing 269 -- 11.5.2 Spectrum Prediction 269 -- 11.5.3 Transmission Below PUs' Interference Tolerable Limit 271 -- 11.5.4 Using Advanced Encoding Techniques 271 -- 11.5.5 Spectrum Monitoring 272 -- 11.6 Summary 274 -- References 274 -- 12 Simulation Frameworks and Potential Research Challenges for -- Internet-of-Vehicles Networks 281 -- 12.1 Introduction 281 -- 12.1.1 Consumer IoT 283 -- 12.1.2 Industrial IoT 283 -- 12.2 Applications of CIoT 284 -- 12.2.1 Smart Home and Automation 284 -- 12.2.2 Smart Wearables 284 -- 12.2.3 Home Security and Smart Domestics 285 -- 12.2.4 Smart Farming 285 -- 12.3 Applications of Industrial IoT 285 -- 12.3.1 Smart Industry 286 -- 12.3.2 Smart Grid/Utilities 286 -- 12.3.3 Smart Communication 286 -- 12.3.4 Smart City 287 -- 12.3.5 Smart Energy Management 287 -- 12.3.6 Smart Retail Management 288 -- 12.3.7 Robotics 288 -- 12.3.8 Smart Cars/Connected Vehicles 289 -- 12.4 Communication Frameworks for IoVs 289 -- 12.4.1 Vehicle-to-Vehicle (V2V) Communication 291 -- 12.4.2 Vehicle to Infrastructure (V2I) Communication 292 -- 12.4.3 Infrastructure to Vehicles (I2V) Communication 293 -- 12.4.4 Vehicle-to-Broadband (V2B) Communication 293 -- 12.4.5 Vehicle-to-Pedestrians (V2P) Communication 293 -- 12.5 Simulation Environments for Internet-of-Vehicles 295 -- 12.5.1 SUMO 296 -- 12.5.2 Network Simulator (NetSim) 296 -- 12.5.3 Ns-2 297 -- 12.5.4 Ns-3 297 -- 12.5.5 OMNeT++ 298 -- 12.6 Potential

Research Challenges 299 -- 12.6.1 Social Challenges 299 -- 12.6.2 Technical Challenges 300 -- 12.7 Summary 302 -- References 302 -- 13 Radio Resource Management in Internet-of-Vehicles 311 -- 13.1 Introduction 311 -- 13.1.1 Dedicated Short-Range Communication 313 -- 13.1.2 Wireless Access for Vehicular Environments 314 -- 13.1.3 Communication Access for Land Mobile (CALM) 314 -- 13.2 Cellular Communication 315 -- 13.2.1 3GPP Releases 315 -- 13.2.2 Long-Term Evolution 317 -- 13.2.3 New Radio 317 -- 13.2.4 Dynamic Spectrum Access 318 -- 13.3 Role of Cognitive Radio for Spectrum Management 319 -- 13.4 Effect of Mobile Nature of Vehicles/Nodes on the Networking 320 -- 13.5 Spectrum Sharing in IoVs 322 -- 13.5.1 Spectrum Sensing Scenarios 322 -- 13.5.2 Spectrum Sharing Scenarios 324 -- 13.5.3 Spectrum Mobility/Handoff Scenarios 325 -- 13.6 Frameworks of Vehicular Networks with Cognitive Radio 326 -- 13.6.1 CR-Based IoVs Networks Architecture 327 -- 13.7 Key Potentials and Research Challenges 328 -- 13.7.1 Key Potentials 328 -- 13.7.2 Research Challenges 329 -- 13.8 Summary 333 -- References 333 -- Index 000.

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