Wireless Blockchain E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
Explore foundational concepts in blockchain theory with an emphasis on recent advances in theory and practice In Wireless Blockchain: Principles, Technologies and Applications, accomplished researchers and editors Bin Cao, Lei Zhang, Mugen Peng, and Muhammad Ali Imran deliver a robust and accessible exploration of recent developments in the theory and practice of blockchain technology, systems, and potential application in a variety of industrial sectors, including manufacturing, entertainment, public safety, telecommunications, public transport, healthcare, financial services, automotive, and energy utilities. The book presents the concept of wireless blockchain networks with different network topologies and communication protocols for various commonly used blockchain applications. You'll discover how these variations and how communication networks affect blockchain consensus performance, including scalability, throughput, latency, and security levels. You'll learn the state-of-the-art in blockchain technology and find insights on how blockchain runs and co-works with existing systems, including 5G, and how blockchain runs as a service to support all vertical sectors efficiently and effectively. Readers will also benefit from the inclusion of: * A thorough introduction to the Byzantine Generals problem, the fundamental theory of distributed system security and the foundation of blockchain technology * An overview of advances in blockchain systems, their history, and likely future trends * Practical discussions of Proof-of-Work systems as well as various Proof-of-"X" alternatives, including Proof-of-Stake, Proof-of-Importance, and Proof-of-Authority * A concise examination of smart contracts, including trusted transactions, smart contract functions, design processes, and related applications in 5G/B5G * A treatment of the theoretical relationship between communication networks and blockchain Perfect for electrical engineers, industry professionals, and students and researchers in electrical engineering, computer science, and mathematics, Wireless Blockchain: Principles, Technologies and Applications will also earn a place in the libraries of communication and computer system stakeholders, regulators, legislators, and research agencies.
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Table of Contents
Cover
Title Page
Copyright
List of Contributors
Preface
Abbreviations
1 What is Blockchain Radio Access Network?*
1.1 Introduction
1.2 What is B‐RAN
1.3 Mining Model
1.4 B‐RAN Queuing Model
1.5 Latency Analysis of B‐RAN
1.6 Security Considerations
1.7 Latency‐Security Trade‐off
1.8 Conclusions and Future Works
References
Notes
2 Consensus Algorithm Analysis in Blockchain: PoW and Raft
2.1 Introduction
2.2 Mining Strategy Analysis for the PoW Consensus‐Based Blockchain
2.3 Performance Analysis of the Raft Consensus Algorithm
2.4 Conclusion
Appendix
References
Notes
3 A Low Communication Complexity Double‐layer PBFT Consensus
3.1 Introduction
3.2 Double‐Layer PBFT‐Based Protocol
3.3 Communication Reduction
3.4 Communication Complexity of Double‐Layer PBFT
3.5 Security Threshold Analysis
3.6 Conclusion
References
4 Blockchain‐Driven Internet of Things
4.1 Introduction
4.2 Consensus Mechanism in Blockchain
4.3 Applications of Blockchain in IoT
4.4 Issues and Challenges of Blockchain in IoT
4.5 Conclusion
References
Note
5 Hyperledger Blockchain‐Based Distributed Marketplaces for 5G Networks
5.1 Introduction
5.2 Marketplaces in Telecommunications
5.3 Distributed Resource Sharing Market
5.4 Experimental Design and Results
5.5 Conclusions
References
6 Blockchain for Spectrum Management in 6G Networks
6.1 Introduction
6.2 Background
6.3 Architecture of an Integrated SDN and Blockchain Model
6.4 Simulation Design
6.5 Results and Analysis
6.6 Conclusion
Acknowledgments
References
7 Integration of MEC and Blockchain
7.1 Introduction
7.2 Typical Framework
7.3 Use Cases
7.4 Conclusion
References
8 Performance Analysis on Wireless Blockchain IoT System
8.1 Introduction
8.2 System Model
8.3 Performance Analysis in Blockchain‐Enabled Wireless IoT Networks
8.4 Optimal FN Deployment
8.5 Security Performance Analysis
8.6 Numerical Results and Discussion
8.7 Chapter Summary
References
Notes
9 Utilizing Blockchain as a Citizen‐Utility for Future Smart Grids
9.1 Introduction
9.2 DET Using Citizen‐Utilities
9.3 Improved Citizen‐Utilities
9.4 Conclusions
References
Notes
10 Blockchain‐enabled COVID‐19 Contact Tracing Solutions
10.1 Introduction
10.2 Preliminaries of BeepTrace
10.3 Modes of BeepTrace
10.4 Future Opportunity and Conclusions
References
11 Blockchain Medical Data Sharing
11.1 Introduction
Acknowledgments
References
12 Decentralized Content Vetting in Social Network with Blockchain
12.1 Introduction
12.2 Related Literature
12.3 Content Propagation Models in Social Network
12.4 Content Vetting with Blockchains
12.5 Optimized Channel Networks
12.6 Simulations of Content Propagation
12.7 Evaluation with Simulations of Social Network
12.8 Conclusion
Acknowledgment
References
Note
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Important variables in the modeling and analysis
Chapter 2
Table 2.1 The state transitions and reward matrices of the MDP mining model.
Table 2.2 The optimal policy for the blockchain environment with
when
and...
Chapter 3
Table 3.1 Frequently used notations.
Chapter 5
Table 5.1 Performance of blockchain frameworks.
Chapter 6
Table 6.1 Simulation parameters.
Chapter 8
Table 8.1 Frequently used notations.
Table 8.2 Simulation parameters.
Chapter 9
Table 9.1 A summary review of proposed systems and framework based on blockc...
Chapter 10
Table 10.1 Comparison between two modes of BeepTrace.
Chapter 11
Table 11.1 Some differences between the cryptocurrency and healthcare applic...
List of Illustrations
Chapter 1
Figure 1.1 Conceptual illustration of self‐organized B‐RAN.
Figure 1.2 Four stages of the access workflow in B‐RAN.
Figure 1.3 The distribution of block time from real data and simulations. (P...
Figure 1.4 State transition graph of
. (
.).
Figure 1.5 State space diagram in B‐RAN.
Figure 1.6 States
rearrangement into a row.
Figure 1.7 The distribution of steady states under different traffic intensi...
Figure 1.8 The analytical and experimental latency for different
and
wit...
Figure 1.9 The impact of the maximal access channels
on service latency (
Figure 1.10 The process of a double spending attack.
Figure 1.11 The analytical and experimental probability of success attack fo...
Figure 1.12 The latency‐security trade‐off (
and
). (a) Under different tr...
Chapter 2
Figure 2.1 Data structure of blockchain.
Figure 2.2 An illustrating example of the state in the adopted MDP.
Figure 2.3 An illustrating example of the state transitions after the execut...
Figure 2.4 An illustrating example of the state transitions after the execut...
Figure 2.5 An illustrating example of the state transitions after the execut...
Figure 2.6 An illustrating example of the state transitions after the execut...
Figure 2.7 An illustrating example of the state transitions after the execut...
Figure 2.8 The agent‐environment interaction process of RL algorithm.
Figure 2.9 Achieved mining gain vs.
for
.
Figure 2.10 Achieved mining gain vs.
for
.
Figure 2.11 Achieved mining gain vs.
for
.
Figure 2.12 Achieved mining gain vs. time step for different
and
,
.
Figure 2.13 Achieved mining gain vs. time step for different
and
,
.
Figure 2.14 Achieved mining gain vs. time step for different
and
,
.
Figure 2.15 Achieved mining gain vs. the
for
and different
.
Figure 2.16 Achieved mining gain when the environment is changing and the va...
Figure 2.17 Achieved mining gain when the environment is changing and the va...
Figure 2.18 Achieved mining gain when the environment is changing and the va...
Figure 2.19 State transition model for the Raft algorithm. Source: Howard et...
Figure 2.20 CDF of network split time,
.
Figure 2.21 CDF of network split time,
.
Figure 2.22 PDF of network split time
,
.
Figure 2.23 Expectation of network split time given different network sizes....
Figure 2.24 Variance of network split time given different network sizes.
Figure 2.25 Expectation of network split time given different packet loss ra...
Figure 2.26 Variance of network split time given different packet loss rates...
Figure 2.27 Average interval to receive a heartbeat for a follower.
Figure 2.28 Probability of the number of elections,
.
Figure 2.29 Probability of the number of elections,
.
Figure 2.30 Expectation of the number of elections.
Chapter 3
Figure 3.1 Single‐layer PBFT consensus processing. Source: Castro et al. [11...
Figure 3.2 Topology of the proposed double‐layer PBFT system. (Note that we ...
Figure 3.3 Implementation flow chart for double‐layer PBFT model.
Figure 3.4 Analytical and simulation results for success rate in the FPD mod...
Figure 3.5 Analytical and simulation results for success rate in the FND mod...
Chapter 4
Figure 4.1 An example of implementing blockchain in the IoT system.
Figure 4.2 Three stages of PBFT consensus processing. Source: Castro et al. ...
Figure 4.3 An example of Tangle.
Figure 4.4 An example of Hashgraph.
Figure 4.5 Architecture of blockchain implementation for FSC.
Figure 4.6 Flowchart of the proposed architecture.
Figure 4.7 The ABAC framework.
Figure 4.8 Access control process.
Chapter 5
Figure 5.1 Access infrastructure sharing models.
Figure 5.2 Hyperledger fabric architecture.
Figure 5.3 Leader election in raft consensus.
Figure 5.4 Distributed market model.
Figure 5.5 Blockchain benchmark tool stack.
Figure 5.6 The experimental Hyperledger blockchain network. (a) Network topo...
Figure 5.7 Transaction throughput vs. latency with different
System under te
...
Figure 5.8 Block size vs. performance.
Figure 5.9 Network size vs. performance.
Chapter 6
Figure 6.1 Classification of indoor network deployments based on the busines...
Figure 6.2 Architecture for supporting multi‐operator small‐cell deployments...
Figure 6.3 Formation of blockchain – each block carries a list of transactio...
Figure 6.4 Logical flow of information between blockchain and SDN.
Figure 6.5 Smart contract implemented in blockchain layer as an overlay on S...
Figure 6.6 Network management signaling diagram using smart contract.
Figure 6.7 RAN simulation scenario in an ns‐3 simulator.
Figure 6.8 RSRP measurements received by the SDN controller as the UE moves ...
Figure 6.9 Average throughput from UEs: (a) without blockchain and (b) with ...
Figure 6.10 Number of transactions across different users on MNO
2
.
Figure 6.11 Average number of transactions per second at different instances...
Chapter 7
Figure 7.1 Architecture for blockchain‐enabled MEC system.
Figure 7.2 Architecture for MEC‐based blockchain system.
Figure 7.3 (a) The structure of traditional FL and (b) the structure of the ...
Figure 7.4 The one‐epoch operation of BlockFL with and without forking.
Figure 7.5 Best accuracy of BlockFL, traditional FL, and standalone without ...
Figure 7.6 Average learning completion latency versus the number of devices ...
Figure 7.7 Layered architecture of the proposed cross‐domain authentication ...
Figure 7.8 Data fields indicating domain‐specific information encapsulated i...
Figure 7.9 Overview of cross‐domain authentication process [43]. Entity
in...
Figure 7.10 Time consumption of BASA with varying parameters (AU: Authentica...
Chapter 8
Figure 8.1 Blockchain‐enabled IoT network model.
Figure 8.2 Interference area for a specific TN.
Figure 8.3 Comparisons of
vs. TN density (FN density is per 320
.
Figure 8.4 Comparisons of
vs. FN density (TN density is
per
.
Figure 8.5 Comparisons of overall throughput vs. TN density (the FN density ...
Figure 8.6 Comparisons of overall throughput vs. FN density (the TN density ...
Figure 8.7 Comparisons of optimal FN density vs. TN density.
Chapter 9
Figure 9.1 Classification of blockchain applications in the energy sector....
Figure 9.2 Blockchain‐based DET system (citizen‐utility).
Figure 9.3 Prosumer community groups in the smart grid.
Figure 9.4 Illustration of a Duck curve.
Figure 9.5 Categories of DSM.
Figure 9.6 Centralized management of demand response.
Figure 9.7 Overview scenario for two inter‐operating citizen‐utilities in a ...
Figure 9.8 High‐level overview of the HARB framework. On the left, we have t...
Figure 9.9 DSM citizen‐utility system overview.
Figure 9.10 Privacy‐preserving citizen‐utility system overview.
Figure 9.11 Threat model of privacy attacks against blockchain‐based systems...
Figure 9.12 Overview of the proposed frameworks.
Chapter 10
Figure 10.1 Diagram showing the operation of both BeepTrace modes. Source: A...
Figure 10.2 Framework of BeepTrace‐active. Source: Klaine et al. [21] / with...
Figure 10.3 BeepTrace‐active framework for blockchain. Source: Klaine et al....
Figure 10.4 Workflow and framework of BeepTrace‐passive. Source: Onireti et ...
Figure 10.5 TraceCode construction of BeepTrace‐passive. Source: Onireti et ...
Figure 10.6 Computing resource requirement for contact tracing geodata. Sour...
Chapter 11
Figure 11.1 Medical data sharing strategic steps (
which ensures feasibility,
...
Figure 11.2 An overview of secure architecture for exchange of sensitive med...
Figure 11.3 Data retrieval from storage must satisfy specified policies for ...
Figure 11.4 Conceptually the blockchain is constructed as a peer to peer net...
Figure 11.5 A secure blockchain‐based architecture for the sharing medical d...
Figure 11.6 Transactions are submitted to a pool of requests where the proce...
Figure 11.7 Traditional medical data sharing models request and access raw d...
Figure 11.8 Blockchain‐based data sharing protects data and privacy through ...
Figure 11.9 Overview of patient‐to‐patient medical data sharing using blockc...
Figure 11.10 Blockchain medical data sharing involving multiple institutions...
Figure 11.11 Precision medicine combines aspects of current healthcare with ...
Figure 11.12 By addressing difficulties posed by ownership of data, access p...
Chapter 12
Figure 12.1 Content propagation model.
Figure 12.2 Behavior of an adversarial user.
Figure 12.3 Overview of the decentralized vetting procedure.
Figure 12.4 Procedure of creating unidirectional offline channels.
Figure 12.5 Content vetting procedure.
Figure 12.6 Optimized content vetting procedure.
Figure 12.7 Workflow of each miner of the blockchain network.
Figure 12.8 Propagation of incorrect information with increasing number of u...
Figure 12.9 Propagation of incorrect information with increasing number of u...
Figure 12.10 Propagation of incorrect information with increasing threshold ...
Figure 12.11 Propagation of incorrect information with increasing threshold ...
Figure 12.12 Propagation of correct information with increasing threshold of...
Figure 12.13 Propagation of correct information with increasing threshold of...
Figure 12.14 Propagation of correct information with increasing number of us...
Figure 12.15 Propagation of correct information with increasing number of us...
Guide
Cover Page
Table of Contents
Title Page
Copyright
List of Contributors
Preface
Abbreviations
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Wireless Blockchain
Principles, Technologies and Applications
Edited by
Bin Cao
Beijing University of Posts and TelecommunicationsBeijing, China
Lei Zhang
University of GlasgowGlasgow, UK
Mugen Peng
Beijing University of Posts and TelecommunicationsBeijing, China
Muhammad Ali Imran
University of GlasgowGlasgow, UK
Copyright © 2022 by John Wiley & Sons Ltd. All rights reserved.
Published by John Wiley & Sons Ltd., Chichester, United Kingdom.
Published simultaneously in Canada.
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Library of Congress Cataloging‐in‐Publication Data
Names: Cao, Bin, editor. | Zhang, Lei, editor. | Peng, Mugen, editor. |
Imran, Muhammad Ali, editor.
Title: Wireless blockchain : principles, technologies and applications /
Bin Cao, Beijing University of Posts and Telecommunications, Beijing,
China, Lei Zhang, University of Glasgow, Glasgow, UK, Mugen Peng,
Beijing University of Posts and Telecommunications, Beijing, China,
Muhammad Ali Imran, University of Glasgow, Glasgow, UK.
Description: Chichester, United Kingdom ; Hoboken : Wiley‐IEEE Press,
[2022] | Includes bibliographical references and index.
Identifiers: LCCN 2021034990 (print) | LCCN 2021034991 (ebook) | ISBN
9781119790808 (cloth) | ISBN 9781119790815 (adobe pdf) | ISBN
9781119790822 (epub)
Subjects: LCSH: Blockchains (Databases) | Wireless communication
systems–Industrial applications. | Personal communication service
systems.
Classification: LCC QA76.9.B56 W57 2022 (print) | LCC QA76.9.B56 (ebook)
| DDC 005.74–dc23
LC record available at https://lccn.loc.gov/2021034990
LC ebook record available at https://lccn.loc.gov/2021034991
Cover Design: Wiley
Cover Image: © phive/Shutterstock
List of Contributors
Nima Afraz
CONNECT Center, Trinity College
Dublin
Ireland
and
School of Computer Science University College Dublin
Dublin
Ireland
Hamed Ahmadi
Department of Electronic Engineering University of York
York
UK
Sandro Amofa
University of Electronic Science and Technology of China
Chengdu
China
John G. Breslin
National University of Ireland
Galway
Ireland
Bin Cao
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications
Beijing
China
Volkan Dedeoglu
Data61, CSIRO
Brisbane
Australia
Zhi Ding
Department of Electrical and Computer Engineering, University of California
Davis, CA
USA
Junyi Dong
James Watt School of Engineering University of Glasgow
Glasgow
UK
Ali Dorri
School of Computer Science, QUT
Brisbane
Australia
Jaafar M. H. Elmirghani
School of Electronic and Electrical Engineering, University of Leeds
Leeds
UK
Chenglin Feng
College of Science and Engineering University of Glasgow
Glasgow
UK
Jianbin Gao
University of Electronic Science and Technology of China
Chengdu
China
Xiqi Gao
National Mobile Communications Research Laboratory, Southeast University
Nanjing
China
and
Purple Mountain Laboratories
Nanjing, Jiangsu
China
Dongyan Huang
College of Information and Communications, Guilin University of Electronic Technology
Guilin
China
Muhammad Ali Imran
James Watt School of Engineering University of Glasgow
Glasgow
UK
Raja Jurdak
School of Computer Science, QUT
Brisbane
Australia
and
Data61, CSIRO
Brisbane
Australia
Hong Kang
James Watt School of Engineering University of Glasgow
Glasgow
UK
Salil S. Kanhere
School of Computer Science and Engineering, UNSW
Sydney
Australia
Samuel Karumba
School of Computer Science and Engineering, UNSW
Sydney
Australia
Paulo Valente Klaine
James Watt School of Engineering University of Glasgow
Glasgow
UK
Yuwei Le
National Mobile Communications Research Laboratory, Southeast University
Nanjing
China
Wenyu Li
College of Science and Engineering University of Glasgow
Glasgow
UK
Xintong Ling
National Mobile Communications Research Laboratory, Southeast University
Nanjing
China
and
Purple Mountain Laboratories
Nanjing, Jiangsu
China
Weikang Liu
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications
Beijing
China
Kumudu Munasighe
Faculty of Science and Technology University of Canberra
Canberra
Australia
Asuquo A. Okon
Faculty of Science and Technology University of Canberra
Canberra
Australia
Mugen Peng
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications
Beijing
China
Marco Ruffini
CONNECT Center, Trinity College
Dublin
Ireland
Olusegun S. Sholiyi
National Space Research and Development Agency, Obasanjo Space Centre
Abuja
Nigeria
Yao Sun
James Watt School of Engineering College of Science and Engineering University of Glasgow
Glasgow
UK
Subhasis Thakur
National University of Ireland
Galway
Ireland
Jiaheng Wang
National Mobile Communications Research Laboratory, Southeast University
Nanjing
China
and
Purple Mountain Laboratories
Nanjing, Jiangsu
China
Taotao Wang
College of Electronics and Information Engineering, Shenzhen University
Shenzhen
China
Qi Xia
University of Electronic Science and Technology of China
Chengdu
China
Hao Xu
James Watt School of Engineering University of Glasgow
Glasgow
UK
Bowen Yang
James Watt School of Engineering College of Science and Engineering University of Glasgow
Glasgow
UK
Lei Zhang
James Watt School of Engineering College of Science and Engineering University of Glasgow
Glasgow
UK
Shengli Zhang
College of Electronics and Information Engineering, Shenzhen University
Shenzhen
China
Zaixin Zhang
James Watt School of Engineering University of Glasgow
Glasgow
UK
Preface
Originally proposed as the backbone technology of Bitcoin, Ethereum, and many other cryptocurrencies, blockchain has become a revolutionary decentralized data management framework that establishes consensuses and agreements in trustless and distributed environments. Thus, in addition to its soaring popularity in the finance sector, blockchain has attracted much attention from many other major industrial sectors ranging from supply chain, transportation, entertainment, retail, healthcare, information management to financial services, etc.
Essentially, blockchain is built on a physical network that relies on the communications, computing, and caching, which serves the basis for blockchain functions such as incentive mechanism or consensus. As such, blockchain systems can be depicted as a two‐tier architecture: an infrastructure layer and a blockchain layer. The infrastructure layer is the underlying entity responsible for maintaining the P2P network, building connection through wired/wireless communication, and computing and storing data. On the other hand, the top layer is the blockchain that is responsible for trust and security functions based on the underlying exchange of information. More specifically, blockchain features several key components that are summarized as transactions, blocks, and the chain of blocks. Transactions contain the information requested by the client and need to be recorded by the public ledger; blocks securely record a number of transactions or other useful information; using a consensus mechanism, blocks are linked orderly to constitute a chain of blocks, which indicates logical relation among the blocks to construct the blockchain.
As a core function of the blockchain, the consensus mechanism (CM, also referred to as consensus algorithm or consensus protocol) works in the blockchain layer in order to ensure a clear sequence of transactions and the integrity and consistency of the blockchain across geographically distributed nodes. The CM largely determines the blockchain system performance in terms of security level (fault tolerance level), transaction throughput, delay, and node scalability. Depending on application scenarios and performance requirements, different CMs can be used. In a permissionless public chain, nodes are allowed to join/leave the network without permission and authentication. Therefore, proof‐based algorithms (PoX), such as proof‐of‐work (PoW), proof‐of‐stake (PoS), and their variants, are commonly used in many public blockchain applications (e.g. Bitcoin and Ethereum). PoX algorithms are designed with excellent node scalability performance through node competition; however, they could be very resource demanding. Also, these CMs have other limitations such as long transaction confirmation latency and low throughput. Unlike public chains, private and consortium blockchains prefer to adopt lighter protocols such as Raft and practical Byzantine fault tolerance (PBFT) to reduce computational power demand and improve the transaction throughput. A well‐known example of PBFT implementation is the HyperLedger Fabric, part of HyperLedger business blockchain frameworks. However, such CMs may require heavy communication resources.
Today, most state‐of‐the‐art blockchains are primarily designed in stable wired communication networks running in advanced devices with sufficient communication resource provision. Hence, the blockchain performance degradation caused by communication is negligible. Nevertheless, this is not the case for the highly dynamic wireless connected digital society that is mainly composed of massive wireless devices encompassing finance, supply chain, healthcare, transportation, and energy. Especially through the upcoming 5G network, the majority of valuable information exchange may be through a wireless medium. Thus, it is critically important to answer one question, how much communication resource is needed to run a blockchain network (i.e. communication for blockchain).
From another equally important aspect when combining blockchain with communication (especially wireless communication), many works have focused on how to use blockchain to improve the communication network performance (i.e. blockchain for communication). This integration between wireless networks and blockchain allows the network to monitor and manage communication resource utilization in a more efficient manner, reducing its administration costs and improving the speed of communication resource trading. In addition, because it is the blockchain's inherit transparency, it can also record real‐time spectrum utilization and massively improve spectrum efficiency by dynamically allocating spectrum bands according to the dynamic demands of devices. Moreover, it can also provide the necessary incentive for spectrum and resource sharing between devices, fully enabling new technologies and services that are bound to emerge. The resource coordination and optimization between resource requesters and providers can be automatically completed through smart contracts, thus improving the efficiency of resource optimization. Furthermore, with future wireless networks shifting toward decentralized solutions, with thousands of mobile cells deployed by operators and billions of devices communicating with each other, fixed spectrum allocation and operator‐controlled resource sharing algorithms will not be scalable nor effective in future networks. As such, by designing a communications network coupled with blockchain as its underlying infrastructure from the beginning, the networks can be more scalable and provide better and more efficient solutions in terms of spectrum sharing and resource optimization, for example.
The book falls under a broad category of security and communication network and their transformation and development, which itself is a very hot topic for research these years. The book is written in such a way that it offers a wide range of benefits to the scientific community: while beginners can learn about blockchain technologies, experienced researchers and scientists can understand the extensive theoretical design and architecture development of blockchain, and industrial experts can learn about various perspectives of application‐driven blockchains to facilitate different vertical sectors. Therefore, this feature topic can attract graduate/undergraduate level students, as well as researchers and leading experts from both academia and industry. In particular, some blockchain‐enabled use cases included in the book are suitable for audiences from healthcare, computer, telecommunication, network, and automation societies.
In Chapter 1, the authors provide an overview of blockchain radio access network (B‐RAN), which is a decentralized and secure wireless access paradigm. It leverages the principle of blockchain to integrate multiple trustless networks into a larger shared network and benefits multiple parties from positive network effects. The authors start from the block generation process and develop an analytical model to characterize B‐RAN behaviors. By defining the work flow of B‐RAN and introducing an original queuing model based on a time‐homogeneous Markov chain, the steady state of B‐RAN is characterized and the average service latency is derived. The authors then use the probability of a successful attack to define the safety property of B‐RAN and evaluate potential factors that influence its security. Based on the modeling and analysis, the authors uncover an inherent trade‐off relationship between security and latency and develop an in‐depth understanding regarding the achievable performance of B‐RAN. Finally, the authors verify the efficiency of the model through an innovative B‐RAN prototype.
Chapter 2 theoretically and experimentally analyses different consensus algorithms in blockchains. The chapter firstly analyses the PoW consensus algorithm. The authors employ reinforcement learning (RL) to dynamically learn a mining strategy with the performance approaching that of the optimal mining strategy. Because the mining Markov decision process (MDP) problem has a non‐linear objective function (rather than linear functions of standard MDP problems), the authors design a new multi‐dimensional RL algorithm to solve the problem. Experimental results indicate that, without knowing the parameter values of the mining MDP model, the proposed multi‐dimensional RL mining algorithm can still achieve optimal performance over time‐varying blockchain networks. Moreover, the chapter analyzes the Raft consensus algorithm that is usually adopted in consortium/private blockchains. The authors investigate the performance of Raft in networks with non‐negligible packet loss rate. They propose a simple but accurate analytical model to analyze the distributed network split probability. The authors conclude the chapter by providing simulation results to validate the analysis.
Chapter 3 describes a PBFT‐based blockchain system, which makes it possible to break the communication complexity bottleneck of traditional PoW‐ or BFT‐based systems. The authors discuss a double‐layer PBFT‐based consensus mechanism, which re‐distributes nodes into two layers in groups. The analysis shows that this double‐layer PBFT significantly reduces communication complexity. The authors then prove that the complexity is optimal when the nodes are evenly distributed in each group in the second layer. Further, the security threshold is analyzed based on faulty probability‐determined (FPD) and the faulty number‐determined (FND) models in the chapter. Finally, the chapter provides a practical protocol for the proposed double‐layer PBFT system with a review of how PBFT is developed.
In Chapter 4, the authors start by introducing the basic concepts of blockchain and illustrating why a consensus mechanism plays an indispensable role in a blockchain‐enabled Internet of Things (IoT) system. Then, the authors discuss the main ideas of two famous consensus mechanisms, PoW and PoS, and list their limitations in IoT. After that, the authors introduce PBFT and direct acyclic graph (DAG)‐based consensus mechanisms as an effective solution. Next, several classic scenarios of blockchain applications in the IoT are introduced. Finally, the chapter is concluded with the discussion of potential issues and challenges of blockchain in IoT to be addressed in the future.
Chapter 5 addresses the issues associated with centralized marketplaces in 5G networks. The authors firstly study how a distributed alternative based on blockchain and smart contract technology could replace the costly and inefficient third‐party‐based trust intermediaries. Next, the authors propose a smart contract based on a sealed‐bid double auction to allow resource providers and enterprise users to trade resources on a distributed marketplace. In addition, the authors explain the implementation of this marketplace application on HyperLedger Fabric permissioned blockchain while deploying the network using a pragmatic scenario over a public, commercial cloud. Finally, the authors evaluated the distributed marketplace's performance under different transaction loads.
In Chapter 6, the authors describe an integrated blockchain and software‐defined network (SDN) architecture for multi‐operator support in 6G networks. They present a unified SDN and blockchain architecture with enhanced spectrum management features for enabling seamless user roaming capabilities between mobile network operators (MNOs). The authors employ the smart contract feature of blockchain to enable the creation of business and technical agreements between MNOs for intelligent and efficient management of spectrum assets (i.e. the radio access network). The study shows that by integrating blockchain and SDN, the foundation for creating trusted interactions in a trustless environment can be established, and users can experience no disruption in service with very minimal delay as they traverse between operators.
Chapter 7 investigates and discusses the integration of blockchain and mobile edge computing (MEC). The authors firstly provide an overview of the MEC, which sinks computing power to the edge of networks and integrates mobile access networks and Internet services in 5G and beyond. Next, the authors introduce the typical framework for blockchain‐enabled MEC and MEC‐based blockchain, respectively. The authors further show that blockchain can be employed to ensure the reliability and irreversibility of data in MEC systems, and in turn, MEC can also solve the major challenge in the development of blockchain in IoT applications.
Chapter 8 establishes an analytical model for PoW‐based blockchain‐enabled wireless IoT systems by modeling their spatial and temporal characteristics as Poisson point processes (PPP). The authors derive the distribution of signal‐to‐interference‐plus‐noise ratio (SINR), blockchain transaction successful rate, as well as its overall throughput. Based on this performance analysis, the authors design an algorithm to determine the optimal full function node deployment for blockchain systems under the criterion of maximizing transaction throughput. In addition, the security performance of the proposed system is analyzed in the chapter considering three different types of malicious attacks. The chapter ends with a series of numerical results to validate the accuracy of the theoretical analysis and optimal node deployment algorithm.
In Chapter 9, the authors examine the factors governing successful deployment of blockchain‐based distributed energy trading (DET) applications and their technical challenges. The chapter walks through the fundamentals of “citizen‐utilities,” primarily assessing its impact on efforts to manage distributed generation, storage, and consumption on the consumer side of the distribution network, while intelligently coordinating DET without relying on trusted third parties. Additionally, the chapter highlights some of the open research challenges including scalability, interoperability, and privacy that hinder the mainstream adoption of “citizen‐utilities” in the energy sector. Then, to address these research challenges, the authors propose a scalable citizen‐utility that supports interoperability and a Privacy‐preserving Data Clearing House (PDCH), which is a blockchain‐based data management tool for preserving on‐ledger and off‐ledger transactions data privacy. The chapter is finished with outlines of future research directions of PDCH.
In Chapter 10, the authors introduce a blockchain‐enabled COVID‐19 contact tracing solution named BeepTrace. This novel technology inherits the advantages of digital contract tracing (DCT) and blockchain, ensuring the privacy of users and eliminating the concerns about the third‐party trust while protecting the population's health. Then, based on different sensing technologies, i.e. Bluetooth and GPS, the authors categorize BeepTrace into BeepTrace‐active mode and BeepTrace‐passive mode, respectively. In addition, the authors summarize and compare the two BeepTrace modes and indicate their working principles and privacy preservation mechanisms in detail. After that, the authors demonstrate a preliminary approach of BeepTrace to prove the feasibility of the scheme. At last, further development prospects of BeepTrace or other decentralized contact tracing applications are discussed, and potential challenges are pointed out.
Chapter 11 looks at the infusion of blockchain technology into medical data sharing. The chapter provides an overview of medical data sharing and defines the challenges in this filed. The authors revisit some already established angles of blockchain medical data sharing in order to properly contextualize it and to highlight new perspectives on the logical outworking of blockchain‐enabled sharing arrangements. Then, the authors present three cases that are especially suited to blockchain medical data sharing. They also present an architecture to support each paradigm presented and analyze medical data sharing to highlight privacy and security benefits to data owners. Finally, the authors highlight some new and emerging services that can benefit from the security, privacy, data control, granular data access, and trust blockchain medical data sharing infuses into healthcare.
In Chapter 12, the authors propose a blockchain‐based decentralized content vetting for social networks. The authors use Bitcoin as the underlying blockchain model and develop an unidirectional channel model to execute the vetting procedure. In this vetting procedure, all users get a chance to vote for and against a content. Content with sufficient positive votes is considered as vetted content. The authors then optimize the offline channel network topology to reduce computation overhead because of using blockchains. At last, the authors prove the efficiency of the vetting procedure with experiments using simulations of content propagation in social network.
Bin Cao, Lei Zhang, Mugen Peng, Muhammad Ali Imran
August 2021
Abbreviations
3GPP
third‐Generation Partnership Project
4G
fourth generation
5G
fifth generation
6G
sixth generation
AAS
Authentication Agent Server
ABAC
Attribute‐Based Access Control
ABIs
Application Binary Interfaces
ABM
Adaptive Blockchain Module
Abstract
Abortable Byzantine faulT toleRant stAte maChine replicaTion
ACC
Access Control Contract
AI
artificial intelligence
API
application programming interface
APs
access points
BaaS
Blockchain as a Service
BAS
Blockchain Agent Server
BASA
Blockchain‐assisted Secure Authentication
BFT
Byzantine fault tolerance
BLE
Bluetooth low energy
BMap
Bandwidth Map
BN
blockchain network
BPL
building penetration losses
BPM
Business Process Management
bps
bits per second
B‐RAN
Blockchain radio access network
BTC
bitcoin
CA
Certification Authority
CAGR
Compound annual growth rate
CAPEX
Capital expenditure
CBRS
Citizens Broadband Radio Services
CDC
Center for Diseases Control
CDF
cumulative distribution function
CFT
crash fault tolerance
CM
consensus mechanism
CoAP
Constrained application protocol
COVID‐19
Coronavirus Disease 2019
CPU
Central Processing Unit
DAG
direct acyclic graph
DAS
distributed antenna systems
DCT
digital contact tracing
DDoS
Distributed Denial of Service
DEPs
Distributed Energy Prosumers
DER
Distributed Energy Resources
DET
Distributed Energy Trading
DIS
Data integrity verification systems
DLT
Distributed ledger technology
DoS
Denial of Service
DPoS
Delegate Proof‐of‐Stake
DR
demand response
DS
Directory Service
DSM
Demand Side Management
DSO
distributed system operator
dTAM
data Tagging and Anonymization Module
DTLS
Datagram Transport Layer Security
E2E
end to end
ECO
Energy Company Obligation
EE
energy efficiency
EMR
electronic medical record
eNBs
eNodeBs
ESPs
Edge computing service providers
ESS
energy storage systems
EV
electric vehicles
EVN
Electric Vehicle Networks
FAPs
femtocell access points
FCC
Federal Communications Commission
FDI
false data injection
FeGW
Femtocell gateways
FiT
Feedin Tariff
FL
Federated learning
FND
faulty number determined
FNs
function nodes
FPD
faulty probability determined
FSC
food supply chain
FSCD
fast smart contract deployment
FTTH
Fiber‐to‐the‐Home
G2V
grid‐to‐vehicle
Gb/s
gigabyte per second
GDPR
General Data Protection Regulation
Geth
go‐Ethereum
GPS
Global Position System
GTP
GPRS tunneling protocol
HARB
Hypergraph‐based Adaptive Consortium Blockchain
HARQ
Hybrid Automatic Repeat Request
HeNB
home eNB
HLF
Hyperledger fabric
HSS
home subscriber server
HTLC
hash time‐locked contract
HVAC
heating, ventilation, cooling, and air conditioning
IaaS
Infrastructure as a Service
IBC
Identity‐based Cryptography
IBS
Identity‐based Signature
IDC
International Data Corporation
IDE
Integrated development environment
IFA
Dentifier for advertisers
IIoT
Industrial Internet of things
IMDs
Internet of things/mobile devices
IMEI
International mobile equipment identity
IMT
International Mobile Telecommunications
InPs
Infrastructure Providers
IoT
Internet of things
IoVs
Internet of vehicles
KGC
Key Generation Center
KPIs
Key performance indicators
LAN
Local area network
LRSig
Linkable Ring Signatures
LSA
Licensed shared access
LTE
long‐term evolution
MAC
Media access control
MadIoT
Manipulation of demand via IoT
MBS
Macrocell base station
MCMC
Markov Chain Monte Carlo
MDP
Markov decision process
MEC
mobile edge computing
MIMO
multiple‐input, multiple‐output
MME
mobility management entity
MNOs
mobile network operators
MOCN
multi‐operator core network
μOs
micro‐operators
MSP
Membership Service Providers
MSP
multi‐sided platform
MTT
maximum transaction throughput
MVNO
Mobile Virtual Network Operator
MW
megawatts
Naas
Network as a Service
NAT
nucleic acid testing
NFV
Network Function Virtualization
NGN
next‐generation network
NHS
National Health Service
NPI
Non‐pharmaceutical intervention
ns‐3
Network simulator 3
OAMC
Object Attribute Management Contract
ODN
Optical Distribution Network
OFSwitch
open‐flow switch
OPEX
Operating expenditure
OSN
Online Social Network
OTP
one time programmable
OTT
over‐the‐top
P2P
peer‐to‐peer
PaaS
Platform as a Service
PBFT
Practical Byzantine Fault Tolerance
PBN
public blockchain network
PCG
Prosumer Community Groups
PCRF
Policy Charging and Rules Function
PDCH
Privacy‐preserving Data Clearing House
probability density function
P‐GW
packet data network gateway
PHY
physical
PKI
Public key infrastructure
PMC
Policy Management Contract
PoD
proof‐of‐device
PONs
Passive optical networks
PoO
proof‐of‐object
PoS
proof of stake
PoW
proof of work
PPP
Poisson point processes
QoS
Quality of Service
RAN
Radio access network
RES
renewable energy sources
RL
reinforcement learning
RMG
relative mining gain
RSRP
reference signal received power
RSRQ
reference signal received quality
SaaS
software as a Service
SAMC
Subject Attribute Management Contract
SARS‐CoV‐2
Severe Acute Respiratory Syndrome Coronavirus 2
SBCs
single‐board computers
SBSs
small base stations
SDN
software‐defined network
SEMC
Smart Energy Management Controller
S‐GW
Serving gateway
SHeNB
Serving HeNB
SINR
signal‐to‐interference‐plus‐noise ratio
SLA
service‐level agreement
SM
supermassive
SPF
single point of failure
SPs
service providers
SR
spinning reserve
SUTs
System under tests
Tb/s
terabyte per second
TDP
transaction data packet
THeNB
target HeNB
TNs
transaction nodes
TPA
Third Party Auditor
TPS
transactions per second
TTI
transmission time interval
TTP
Trusted Third Party
TTT
time to trigger
UE
user equipment
UE RRC
UE radio resource control
URI
Uniform Resource Identifier
URL
Uniform Resource Locator
UUID
Universally Unique Identifier
V2G
vehicle‐to‐grid
V2V
vehicle‐to‐vehicle
vCPUs
Virtual Central Processing Units
VEN
Vehicular energy networks
VLC
visible light communications
VM
virtual machine
VNO
Virtual Network Operator
VPP
Virtual Power Plants
WAN
Wide Area Network
WHO
World Health Organization
ZKP
zero‐knowledge‐proofs
1What is Blockchain Radio Access Network?*
Xintong Ling1,2, Yuwei Le1, Jiaheng Wang1,2, Zhi Ding3, and Xiqi Gao1,2
1National Mobile Communications Research Laboratory, Southeast University, Nanjing, 210096, China
2Purple Mountain Laboratories, Nanjing, Jiangsu, 211111, China
3Department of Electrical and Computer Engineering, University of California, 95616, Davis, CA, USA
1.1 Introduction
The past decade has witnessed tremendous growth in emerging wireless technologies geared toward diverse applications [1]. Radio access networks (RANs) are becoming more heterogeneous and highly complex. Without well‐designed inter‐operation, mobile network operators (MNOs) must rely on their independent infrastructures and spectra to deliver data, often leading to duplication, redundancy, and inefficiency. A huge number of currently deployed business or individual access points (APs) have not been coordinated in the existing architecture of RANs and are therefore under‐utilized. Meanwhile, user equipments (UEs) are not granted to access to APs of operators other than their own, even though some of them may provide better link quality and economically sensible. The present state of rising traffic demands coupled with the under‐utilization of existing spectra and infrastructures motivates the development of a novel network architecture to integrate multiple parties of service providers (SPs) and clients to transform the rigid network access paradigm that we face today.
Recently, blockchain has been recognized as a disruptive innovation shockwave [2–4]. Federal Communications Commission (FCC) has been suggested that blockchain may be integrated into wireless communications for the next‐generation network (NGN) in the Mobile World Congress 2018. Along the same line, the new concept of blockchain radio access network (B‐RAN) was formally proposed and defined in [5, 6]. In a nutshell, B‐RAN is a decentralized and secure wireless access paradigm that leverages the principle of blockchain to integrate multiple trustless networks into a larger shared network and benefits multiple parties from positive network effects [6]. It is a new architecture that integrates both characteristics of wireless networks and distributed ledger technologies. As revealed in [5, 6], B‐RAN can improve the overall throughput through simplified inter‐operator cooperation in the network layer (rather than increasing the channel capacity in the physical layer). B‐RAN can enhance the data delivery capability by connecting these RANs into a big network and leveraging the power of multi‐sided platform (MSP). The positive network effect can help B‐RAN recruit and attract more players, including network operators, spectral owners, infrastructure manufacturers, and service clients alike [6]. The subsequent expansion of such a shared network platform would make the network platform more valuable, thereby generating a positive feedback loop. In time, a vast number of individual APs can be organized into B‐RAN and commodified to form a sizable and ubiquitous wireless network, which can largely improve the utility of spectra and infrastructures. In practice, rights, responsibilities, and obligations of each participant in B‐RAN can be flexibly codified as smart contracts executed by blockchain.
Among the existing studies on leveraging blockchain in networks, most have focused on Internet of Things (IoT) [7–11], cloud/edge computing [12–15], wireless sensor networks [16], and consensus mechanisms [17–19]. Only a few considered the future integration of blockchain in wireless communications [20–26]. Weiss et al. [20] discussed several potentials of blockchain in spectrum management. Kuo et al. [21] summarized some critical issues when applying blockchain to wireless networks and pointed out the versatility of blockchain. Pascale et al. [22] adopted smart contracts as an enabler to achieve service level agreement (SLA) for access. Kotobi and Bilen [23] proposed a secure blockchain verification protocol associated with virtual currency to enable spectrum sharing. Le et al. [27] developed an early prototype to demonstrate the functionality of B‐RAN.
Despite the growing number of papers and heightened interests to blockchain‐based networking, works including fundamental analysis are rather limited. A number of critical difficulties remain unsolved. (i) Existing works have not assessed the impact of decentralization on RANs after introducing blockchain. Decentralization always comes with a price that should be characterized and quantified. (ii) Very few papers have noticed that service latency will be a crucial debacle for B‐RAN as a price of decentralization [22]. Unfortunately, the length of such delay and its controllability are still open issues. (iii) Security is yet another critical aspect of blockchain‐based protocols. In particular, alternative history attack, as an inherent risk of decentralized databases, is always possible and must be assessed. (iv) A proper model is urgently needed to exploit the characteristics of B‐RAN (such as latency and security) and to further provide insights and guidelines for real‐world implementations.
To address the aforementioned open issues, this chapter establishes a framework to concretely model and evaluate B‐RAN. We start from the block generation process and develop an analytical model to characterize B‐RAN behaviors. We shall evaluate the performance in terms of latency and security in order to present a more comprehensive view of B‐RAN. We further verify the efficacy of our model through an innovative B‐RAN prototype. The key contributions are summarized as follows:
We define the workflow of B‐RAN and introduce an original queuing model based on a time‐homogeneous Markov chain, the first known analytical model for B‐RAN.
From the queuing model, we analytically characterize the steady state of B‐RAN and further derive the average service latency.
We use the probability of a successful attack to define the safety property of B‐RAN and evaluate potential factors that influence the security.
Table 1.1 Important variables in the modeling and analysis
Symbols
Explanations
Symbols
Explanations
Required service time of request
Arrival epoch of request
Request arrival rate
Average inter‐arrival time
Block generation rate
Average block time
Service rate
Average service time
Number of required confirmations
Number of access links
Relative mining rate of an attacker
Traffic intensity
Basic configuration of B‐RAN
Based on the modeling and analysis, we uncover an inherent trade‐off relationship between security and latency, and develop an in‐depth understanding regarding the achievable performance of B‐RAN.
Finally, we build a B‐RAN prototype that can be used in comprehensive experiments to validate the accuracy of our analytical model and results.
We organize this chapter as follows. Section 1.2 presents the B‐RAN framework and the prototype. Section 1.3 provides the mining model to describe the block generation process. In Section 1.4, we establish the B‐RAN queuing model, with which we analyze and evaluate the B‐RAN performance concerning latency and security in Sections 1.5 and 1.6, respectively. We demonstrate the latency‐security trade‐off in Section 1.7 and provide some in‐depth insights into B‐RAN. Section 1.8 concludes this chapter. Given the large number of symbols to be used, we summarize the important variables in Table 1.1.
1.2 What is B‐RAN
1.2.1 B‐RAN Framework
B‐RAN offers a decentralized and secure wireless access paradigm for large‐scale, heterogeneous, and trustworthy wireless networks [5, 6]. B‐RAN unites multilateral inherently trustless network entities without any trusted middleman and manages network access, authentication, authorization, and accounting via direct interactions. As an open unified framework for diverse applications to achieve resource pooling and sharing across sectors, B‐RAN presents an attractive solution for future 6G networking.
With the help of blockchain, B‐RAN can form an expansive cooperative network including not only telecommunication giants but also small contract holders or individual MNOs to deliver excellent quality services at high spectrum efficiency. B‐RAN can integrate multiple networks across SPs for diverse applications. As illustrated in Figure 1.1, B‐RAN is self‐organized by APs belonging to multiple SPs, massive UEs, and a blockchain maintained by miners. In B‐RAN, a confederacy of SPs (organizations or individuals) act as a virtual SP (VSP) to provide public wireless access under shared control. These SPs in B‐RAN allow the greater pool of UEs to access their APs and networks by receiving payment or credit for reciprocal services. Blockchain acts as a public ledger in B‐RAN for recording, confirming, and enforcing digital actions in smart contracts to protect the interests of all participants.
Figure 1.1 Conceptual illustration of self‐organized B‐RAN.
B‐RAN is envisioned to be broadly inter‐operative and to support diverse advanced wireless services and standards. In this chapter, we focus on the fundamental access approach for which the procedure is shown in Figure 1.2.
In preparation for access, UEs and SPs should first enter an SLA containing the details including service types, compensation rates, among other terms. (For example, SPs can first publish their service quality and charge standard, and UEs select suitable SPs according to the expenditure and quality of service.) The service terms and fees will be explicitly recorded in a smart contract authorized by the digital signatures of both sides.
In phase 1, the smart contract with the access request is committed to the mining network and is then verified by miners. The verified contracts are assembled into a new block, which is then added at the end of the chain.
In phase 2, the block is accepted into the main chain after sufficient blocks as confirmations built on top of it.
In phase 3, the request is waiting for service in the service queue.
In phase 4, the access service is delivered according to the smart contract.
The above procedure can be viewed as a process of trust establishment between clients and SPs, similar to negotiating and signing monthly contracts between users and MNOs. Thus, in B‐RAN, clients can obtain access services more conveniently through the above process instead of signing contracts with a specific MNO in advance. The service duration in B‐RAN is flexible and can be as short as a few minutes or hours, which is different from typical long‐term plans (e.g. monthly plans). UEs can prolong access services by renewing the contract earlier before the previous one expires in order to continue the connection status. Therefore, service latency in this context refers to the delay when a UE accesses an unknown network for the first time and can be viewed as the time establishing trust between two trustless parties, which is significantly different from the transmission delay in the physical layer.
Figure 1.2 Four stages of the access workflow in B‐RAN.
Mathematically, we can describe the request structure by shown in Figure 1.2, where and are the arrival epoch and the service duration of request , respectively. Assume that the access requests are mutually independent and arrive as a Poisson process with rate . Equivalently, the inter‐arrival time between two requests follows exponential distribution with mean . Based on well‐known studies such as [28], the random service time is also expected to be exponential with mean . Note that in this chapter, we consider a tract covered by multiple trustless SPs (organizations or individuals). Usually, the block size limit is much larger than the request rate of a single tract and thus can be ignored in this case.
In the context of B‐RAN, we introduce the concept of “virtual link” instead of the physical channel, where one virtual link represents a tunnel providing access services to one client at a time.1 Hence, the number of links means the maximum number of UEs that can receive access services simultaneously from the APs belonging to these SPs in the tract and reflects the access capability of a network. In the considered tract, we assume the maximum number of links to be .
It is worth pointing out that the efficiency improvement of B‐RAN roots in the network pooling principle, which requires a flexible offloading and sharing between subnetworks. Inspired by the Schengen Agreement, B‐RAN adopts the mechanism that, if a client establishes trust with specific SPs via the procedure in Figure 1.2, the client may use resources pooled by SPs in B‐RAN, e.g. a frequency band belonging to another SP. The miners can use some intelligent algorithms2 to allocate and distribute the pooled resources for higher network efficiency [6]. As a result, mobile devices may access suitable APs belonging to the SPs, which likely provide higher quality coverage for the UEs in their current locations. The trading among SPs caused by roaming would be calculated and settled via blockchain periodically. Based on the above mechanism, B‐RAN can take advantage of pooling and sharing across subnetworks. Please refer to Figure 1.9 in this chapter and [6, 10] for more details and evidence for the pooling effect in B‐RAN.
1.2.2 Consensus Mechanism
B‐RAN, as a decentralized system, requires proper consensus mechanisms for consistency [18]. Proof‐of‐Work (PoW) has been widely used in practice and proven to be secure in cryptocurrencies such as Bitcoin. In PoW, network maintainers, also known as miners, need to obtain a hash value below a given target by repeatedly guessing a random variable named nonce. However, PoW‐based consensus mechanisms consume a tremendous amount of energy, which is likely unbearable for energy‐limited mobile devices.
Consequently, proof‐of‐device (PoD) is proposed for B‐RAN as a low‐ cost alternative [5]. PoD utilizes the fact that wireless access usually depends on a hardware device associated with a unique identifier in order to elevate the cost of creating new identities. To be more specific, PoD can create new identifiers or rely on some existing ones, such as the international mobile equipment identity (IMEI) and identifier for advertisers (IFAs), in one time programmable (OTP) memory to distinguish different network entities and prevent identity fraud. Also, because of variations during manufacture, every device has multiple hardware‐dependent features, which could constitute a unique RF fingerprinting for each device and can be identified from the transmitted RF signal [29, 30]. Forging an identity of a device is often costly in the real world, whereas creating multiple identities is almost costless in cryptocurrencies. Therefore, PoD can safeguard the security of B‐RAN without expending immense computing power and is thus suitable for wireless networks. Notably, PoW, PoD, and other alternatives can be put in the same class since all of them are based on hash puzzles. We will further discuss and model the block generation process of a hash‐based consensus mechanism in Section 1.3.
1.2.3 Implementation
In order to evaluate our established model, we will provide demonstrative experimental results from a home‐built prototype throughout the whole article. We implement this version of B‐RAN prototype on four single board computers (SBCs) and use a workstation with Intel Core CPU I7‐8700K and 32GB RAM in order to provide sufficient computing power. Our prototype consists of a standard file system for data storage, a key‐value database for file index, and the core modules written in Python. The prototype supports both PoW and PoD consensus mechanisms as two available options and can adopt an appropriate one according to the specific environment and requirements. We configure different SBCs as UEs and APs and set up the integrated development environment (IDE), wherein UEs propose access requests according to the input configurations, and APs provide services based on the workflow given in Section 1.2.1. During tests, the prototype can track running statistics and provide them as output results.
The B‐RAN prototype is a hierarchical architecture with a number of modules and components [6]. For example, the fast smart contract deployment (FSCD) was proposed in [27] to accelerate the service deployment, and the hash time‐locked contract (HTLC) is designed in [6] to enforce the contract and secure the trading process. This chapter is important by modeling and assessing B‐RAN, and thus, we cannot include all the technical details here. Please see these citations for more details. Note that, in this chapter, the average service time as time unit is set to unity without loss of generality, so time is measured as relative variables in terms of time unit .
In Sections 1.5–1.7, we will assess the performance of B‐RAN from different points of view and verify our model step by step through prototype verifications. Although these verifications focus on different aspects, all of them are obtained from the same B‐RAN prototype described above.
