Wireless Blockchain

Bin Cao, PhD, is an Associate Professor/Researcher in the State Key Laboratory of Network and Switching Technology at Beijing University of Posts and Telecommunications (BUPT). Before that, he was an Associate Professor at Chongqing University of Posts and Telecommunications. He was an international visitor at the Institute for Infocomm Research (I2R), and a research fellow at the National University of Singapore. His research interests include blockchain systems, internet of things, and mobile edge computing.Lei Zhang, PhD, is a Senior Lecturer at the University of Glasgow, UK. He has 19 patents granted/filed in 30+ countries/regions including US/UK/EU/China/Japan/Singapore. He has published 3 books and over 100 peer-reviewed papers. His research was reported by BBC and Bloomberg. Dr Zhang’s research focuses on wireless communications, Blockchain, IoT, data privacy and security. Mugen Peng, PhD, is the Dean of the School of Information and Communication Engineering, and the Deputy Director of State Key Laboratory of Networking and Switching Technology, Beijing, China. He is now or has been on the Editorial/Associate Editorial Board of the IEEE Communications Magazine, the IEEE Internet of Things Journal, the IEEE Transactions on Vehicular Technology, the IEEE Transactions on Network Science and Engineering, and IEEE Network. Muhammad Ali Imran is Dean at the University of Glasgow, UK, UESTC and Professor of Communication Systems and Head of Communications Sensing and Imaging Group in the James Watt School of Engineering at the University of Glasgow, UK. He is a Visiting Professor at the University of Surrey and an Adjunct Professor at the University of Oklahoma, US. He has been awarded 15 patents, has authored/co-authored 8 books, over 400 journal and conference publications, and has been PI/Co-I on over £25 million research grants and contracts.

• List of Contributors xiiiPreface xviiAbbreviations xxiii1 What is Blockchain Radio Access Network? 1Xintong Ling, Yuwei Le, Jiaheng Wang, Zhi Ding, and Xiqi Gao1.1 Introduction 11.2 What is B-RAN 31.2.1 B-RAN Framework 31.2.2 Consensus Mechanism 61.2.3 Implementation 61.3 Mining Model 71.3.1 Hash-Based Mining 71.3.2 Modeling of Hash Trials 71.3.3 Threat Model 101.4 B-RAN Queuing Model 101.5 Latency Analysis of B-RAN 121.5.1 Steady-State Analysis 121.5.2 Average Service Latency 161.6 Security Considerations 181.6.1 Alternative History Attack 181.6.2 Probability of a Successful Attack 191.7 Latency-Security Trade-off 201.8 Conclusions and Future Works 221.8.1 Network Effect and Congest Effect 221.8.2 Chicken and Eggs 221.8.3 Decentralization and Centralization 221.8.4 Beyond Bitcoin Blockchain 22References 232 Consensus Algorithm Analysis in Blockchain: PoW and Raft 27Taotao Wang, Dongyan Huang, and Shengli Zhang2.1 Introduction 272.2 Mining Strategy Analysis for the PoWConsensus-Based Blockchain 302.2.1 Blockchain Preliminaries 302.2.2 Proof ofWork and Mining 302.2.3 Honest Mining Strategy 312.2.4 PoW Blockchain Mining Model 322.2.4.1 State 332.2.4.2 Action 332.2.4.3 Transition and Reward 342.2.4.4 Objective Function 392.2.4.5 Honest Mining 402.2.4.6 Selfish Mining 402.2.4.7 Lead Stubborn Mining 402.2.4.8 Optimal Mining 412.2.5 Mining Through RL 412.2.5.1 Preliminaries for Original Reinforcement Learning Algorithm 412.2.5.2 New Reinforcement Learning Algorithm for Mining 422.2.6 Performance Evaluations 442.3 Performance Analysis of the Raft Consensus Algorithm 522.3.1 Review of Raft Algorithm 522.3.2 System Model 532.3.3 Network Model 532.3.4 Network Split Probability 552.3.5 Average Number of Replies 572.3.6 Expected Number of Received Heartbeats for a Follower 572.3.7 Time to Transition to Candidate 582.3.8 Time to Elect a New Leader 592.3.9 Simulation Results 602.3.10 Discussion 672.3.10.1 Extended Model 672.3.10.2 System Availability and Consensus Efficiency 682.4 Conclusion 69Appendix A.2 69References 703 A Low Communication Complexity Double-layer PBFT Consensus 73Chenglin Feng, Wenyu Li, Bowen Yang, Yao Sun, and Lei Zhang3.1 Introduction 733.1.1 PBFT Applied to Blockchain 743.1.2 From CFT to BFT 743.1.2.1 State Machine Replication 743.1.2.2 Primary Copy 753.1.2.3 Quorum Voting 753.1.3 Byzantine Generals Problem 763.1.4 Byzantine Consensus Protocols 763.1.4.1 Two-Phase Commit 763.1.4.2 View Stamp 763.1.4.3 PBFT Protocol 763.1.5 Motivations 783.1.6 Chapter Organizations 783.2 Double-Layer PBFT-Based Protocol 793.2.1 Consensus Flow 793.2.1.1 The Client 793.2.1.2 First-Layer Protocol 813.2.1.3 Second-Layer Protocol 813.2.2 Faulty Primary Elimination 823.2.2.1 Faulty Primary Detection 823.2.2.2 View Change 833.2.3 Garbage Cleaning 843.3 Communication Reduction 843.3.1 Operation Synchronization 853.3.2 Safety and Liveness 853.4 Communication Complexity of Double-Layer PBFT 853.5 Security Threshold Analysis 863.5.1 Faulty Probability Determined 873.5.2 Faulty Number Determined 893.6 Conclusion 90References 904 Blockchain-Driven Internet of Things 93Bin Cao, Weikang Liu, and Mugen Peng4.1 Introduction 934.1.1 Challenges and Issues in IoT 934.1.2 Advantages of Blockchain for IoT 944.1.3 Integration of IoT and Blockchain 944.2 Consensus Mechanism in Blockchain 964.2.1 PoW 964.2.2 PoS 974.2.3 Limitations of PoWand PoS for IoT 984.2.3.1 Resource Consumption 984.2.3.2 Transaction Fee 984.2.3.3 Throughput Limitation 984.2.3.4 Confirmation Delay 984.2.4 PBFT 984.2.5 DAG 1004.2.5.1 Tangle 1014.2.5.2 Hashgraph 1024.3 Applications of Blockchain in IoT 1024.3.1 Supply Chain 1024.3.1.1 Introduction 1024.3.1.2 Modified Blockchain 1034.3.1.3 Integrated Architecture 1044.3.1.4 Security Analysis 1054.3.2 Smart City 1064.3.2.1 Introduction 1064.3.2.2 Smart Contract System 1074.3.2.3 Main Functions of the Framework 1094.3.2.4 Discussion 1104.4 Issues and Challenges of Blockchain in IoT 1114.4.1 Resource Constraints 1114.4.2 Security Vulnerability 1114.4.3 Privacy Leakage 1124.4.4 Incentive Mechanism 1124.5 Conclusion 112References 1125 Hyperledger Blockchain-Based Distributed Marketplaces for 5G Networks 117Nima Afraz, Marco Ruffini, and Hamed Ahmadi5.1 Introduction 1175.2 Marketplaces in Telecommunications 1185.2.1 Wireless Spectrum Allocation 1195.2.2 Network Slicing 1195.2.3 Passive optical networks (PON) Sharing 1205.2.4 Enterprise Blockchain: Hyperledger Fabric 1215.2.4.1 Shared Ledger 1225.2.4.2 Organizations 1225.2.4.3 Consensus Protocol 1225.2.4.4 Network Peers 1225.2.4.5 Smart Contracts (chaincodes) 1235.2.4.6 Channels 1235.3 Distributed Resource Sharing Market 1235.3.1 Market Mechanism (Auction) 1255.3.2 Preliminaries 1255.4 Experimental Design and Results 1265.4.1 Experimental Blockchain Deployment 1275.4.1.1 Cloud Infrastructure 1275.4.1.2 Container Orchestration: Docker Swarm 1275.4.2 Blockchain Performance Evaluation 1275.4.3 Benchmark Apparatus 1285.4.3.1 Hyperledger Caliper 1305.4.3.2 Data Collection: Prometheus Monitor 1305.4.4 Experimental Results 1315.4.4.1 Maximum Transaction Throughput 1315.4.4.2 Block Size 1315.4.4.3 Network Size 1315.5 Conclusions 133References 1336 Blockchain for Spectrum Management in 6G Networks 137Asuquo A. Okon, Olusegun S. Sholiyi, Jaafar M. H. Elmirghani, and Kumudu Munasighe6.1 Introduction 1376.2 Background 1396.2.1 Rise of Micro-operators 1396.2.2 Case for Novel Spectrum Sharing Models 1406.2.2.1 Blockchain for Spectrum Sharing 1416.2.2.2 Blockchain in 6G Networks 1426.3 Architecture of an Integrated SDN and Blockchain Model 1436.3.1 SDN Platform Design 1436.3.2 Blockchain Network Layer Design 1446.3.3 Network Operation and Spectrum Management 1466.4 Simulation Design 1496.5 Results and Analysis 1526.5.1 Radio Access Network and Throughput 1526.5.2 Blockchain Performance 1546.5.3 Blockchain Scalability Performance 1556.6 Conclusion 156Acknowledgments 156References 1577 Integration of MEC and Blockchain 161Bin Cao, Weikang Liu, and Mugen Peng7.1 Introduction 1617.2 Typical Framework 1627.2.1 Blockchain-Enabled MEC 1627.2.1.1 Background 1627.2.1.2 Framework Description 1627.2.2 MEC-Based Blockchain 1647.2.2.1 Background 1647.2.2.2 Framework Description 1647.3 Use Cases 1667.3.1 Security Federated Learning via MEC-Enabled Blockchain Network 1667.3.1.1 Background 1667.3.1.2 Blockchain-Driven Federated Learning 1677.3.1.3 Experimental Results 1687.3.2 Blockchain-Assisted Secure Authentication for Cross-Domain Industrial IoT 1707.3.2.1 Background 1707.3.2.2 Blockchain-Driven Cross-Domain Authentication 1707.3.2.3 Experimental Results 1727.4 Conclusion 174References 1748 Performance Analysis on Wireless Blockchain IoT System 179Yao Sun, Lei Zhang, Paulo Klaine, Bin Cao, and Muhammad Ali Imran8.1 Introduction 1798.2 System Model 1818.2.1 Blockchain-Enabled IoT Network Model 1818.2.2 Wireless Communication Model 1838.3 Performance Analysis in Blockchain-Enabled Wireless IoT Networks 1848.3.1 Probability Density Function of SINR 1858.3.2 TDP Transmission Successful Rate 1878.3.3 Overall Communication Throughput 1898.4 Optimal FN Deployment 1898.5 Security Performance Analysis 1908.5.1 Eclipse Attacks 1908.5.2 Random Link Attacks 1928.5.3 Random FN Attacks 1928.6 Numerical Results and Discussion 1928.6.1 Simulation Settings 1938.6.2 Performance Evaluation without Attacks 1938.7 Chapter Summary 197References 1979 Utilizing Blockchain as a Citizen-Utility for Future Smart Grids 201Samuel Karumba, Volkan Dedeoglu, Ali Dorri, Raja Jurdak, and Salil S. Kanhere9.1 Introduction 2019.2 DET Using Citizen-Utilities 2049.2.1 Prosumer Community Groups 2049.2.1.1 Microgrids 2059.2.1.2 Virtual Power Plants (VPP) 2069.2.1.3 Vehicular Energy Networks (VEN) 2069.2.2 Demand Side Management 2079.2.2.1 Energy Efficiency 2089.2.2.2 Demand Response 2099.2.2.3 Spinning Reserves 2109.2.3 Open Research Challenges 2119.2.3.1 Scalability and IoT Overhead Issues 2119.2.3.2 Privacy Leakage Issues 2129.2.3.3 Standardization and Interoperability Issues 2129.3 Improved Citizen-Utilities 2139.3.1 Toward Scalable Citizen-Utilities 2139.3.1.1 Challenges 2139.3.1.2 HARB Framework-Based Citizen-Utility 2149.3.2 Toward Privacy-Preserving Citizen-Utilities 2169.3.2.1 Threat Model 2179.3.2.2 PDCH System 2199.4 Conclusions 220References 22110 Blockchain-enabled COVID-19 Contact Tracing Solutions 225Hong Kang, Zaixin Zhang, Junyi Dong, Hao Xu, Paulo Valente Klaine, and Lei Zhang10.1 Introduction 22510.2 Preliminaries of BeepTrace 22810.2.1 Motivation 22810.2.1.1 Comprehensive Privacy Protection 22910.2.1.2 Performance is Uncompromising 22910.2.1.3 Broad Community Participation 22910.2.1.4 Inclusiveness and Openness 23010.2.2 Two Implementations are Based on Different Matching Protocols 23010.3 Modes of BeepTrace 23110.3.1 BeepTrace-Active 23110.3.1.1 Active Mode Workflow 23110.3.1.2 Privacy Protection of BeepTrace-Active 23210.3.2 BeepTrace-Passive 23310.3.2.1 Two-Chain Architecture and Workflow 23310.3.2.2 Privacy Protection in BeepTrace-Passive 23510.4 Future Opportunity and Conclusions 23710.4.1 Preliminary Approach 23710.4.2 Future Directions 23810.4.2.1 Network Throughput and Scalability 23810.4.2.2 Technology for Elders and Minors 23910.4.2.3 Battery Drainage and Storage Optimization 24010.4.2.4 Social and Economic Aspects 24010.4.3 Concluding Remarks 240References 24111 Blockchain Medical Data Sharing 245Qi Xia, Jianbin Gao, and Sandro Amofa11.1 Introduction 24511.1.1 General Overview 24811.1.2 Defining Challenges 24811.1.2.1 Data Security 24811.1.2.2 Data Privacy 24811.1.2.3 Source Identity 24811.1.2.4 Data Utility 24911.1.2.5 Data Interoperability 24911.1.2.6 Trust 24911.1.2.7 Data Provenance 24911.1.2.8 Authenticity 25011.1.3 Sharing Paradigms 25011.1.3.1 Institution-to-Institution Data Sharing 25111.1.3.2 Patient-to-Institution Data Sharing 25611.1.3.3 Patient-to-Patient Data Sharing 25711.1.4 Special Use Cases 26011.1.4.1 Precision Medicine 26111.1.4.2 Monetization of Medical Data 26311.1.4.3 Patient Record Regeneration 26411.1.5 Conclusion 266Acknowledgments 266References 26612 Decentralized Content Vetting in Social Network with Blockchain 269Subhasis Thakur and John G. Breslin12.1 Introduction 26912.2 Related Literature 27012.3 Content Propagation Models in Social Network 27112.4 Content Vetting with Blockchains 27312.4.1 Overview of the Solution 27312.4.2 Unidirectional Offline Channel 27312.4.3 Content Vetting with Blockchains 27512.5 Optimized Channel Networks 27812.6 Simulations of Content Propagation 28012.7 Evaluation with Simulations of Social Network 28612.8 Conclusion 293Acknowledgment 293References 294Index 297