How Much Communication Resource is Needed to Run a Wireless Blockchain Network?

Blockchain is built on a peer-to-peer network that relies on frequent communications among the distributively located nodes. In particular, the consensus mechanisms (CMs), which play a pivotal role in blockchain, are communication resource-demanding and largely determines blockchain security bound and other key performance metrics such as transaction throughput, latency and scalability. Most blockchain systems are designed in a stable wired communication network running in advanced devices under the assumption of sufficient communication resource provision. However, it is envisioned that the majority of the blockchain node peers will be connected through the wireless network in the future. Constrained by the highly dynamic wireless channel and scarce frequency spectrum, communication can significantly affect blockchain's key performance metrics. Hence, in this paper, we present wireless blockchain networks (WBN) under various commonly used CMs and we answer the question of how much communication resource is needed to run such a network. We first present the role of communication in the four stages of the blockchain procedure. We then discuss the relationship between the communication resource provision and the WBNs performance, for three of the most used blockchain CMs namely, Proof-of-Work (PoW), practical Byzantine Fault Tolerant (PBFT) and Raft. Finally, we provide analytical and simulated results to show the impact of the communication resource provision on blockchain performance

Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence

Distributed Resource Allocation in Blockchain-based Video Streaming Systems with Mobile Edge Computing

Blockchain-based video streaming systems aim to build decentralized peer-to-peer networks with flexible monetization mechanisms for video streaming services. On these blockchain-based platforms, video transcoding, which is computational intensive and time consuming, is still a major challenge. Meanwhile, the block size of the underlying blockchain has significant impacts on the system performance. Therefore, this paper proposes a novel blockchain-based framework with adaptive block size for video streaming with mobile edge computing (MEC). First, we design an incentive mechanism to facilitate the collaborations among content creators, video transcoders and consumers. In addition, we present a block size adaptation scheme for blockchain-based video streaming. Moreover, we consider two offloading modes, i.e., offloading to the nearby MEC nod

Study on quantitative design for dynamic blockchain-based computing

This research proposes novel embedded Markovian queueing model-based quantitative models in order to establish a theoretical foundation to design a dynamic blockchain-based computing system with a specific interest in Ethereum. The proposed models commonly assume variable bulk arrivals of transactions in Poisson distribution, i.e., M^(1,n), where n the number of slots across all the mined transactions to be posted in a block or the current block. Firstly, a baseline model is proposed to have a static bulk service of transactions in exponential time, i.e., M^n, for posting the transactions in the current block, referred to as Variable Bulk Arrival and Static Bulk Service (VBASBS) queueing model of the M^(1,n)/M^n/1 type, in which note that n is fixed in order to demonstrate a static chain in terms of the size of the block. Secondly, an adaptive chain model, as a solution of dynamic blockchain in a reactive manner, is proposed based on a Variable Bulk Arrival and Variable Bulk Service (VBAVBS) queueing model of the M^(1,n)/M^(1,i,n)/1 type to provide a quantitative approach to design an adaptive chain that dynamically adapts the size of the block to varying performance trends, in which a state transitions from i back to 0, where 0<i</=n, are tracked in order to demonstrate the dynamically adaptive size of the block. Lastly, an asynchronous chain model, as a solution of dynamic blockchain in a proactive manner, is proposed based on a Variable Bulk Arrival and Asynchronous Bulk Service (VBAABS) queueing model is developed and presented to study and demonstrate the fully asynchronous and staged asynchronous chains. The analytical models are simulated extensively to compare the basic performances of the proposed models such as the average transaction waiting time, the average number of slots per block, and throughput. Further, extensive experiments are conducted in order to validate the analytical results by redesigning the source code of Ethereum to implement and demonstrate each of the proposed chains such as the baseline, the adaptive, the fully-asynchronous and the staged-asynchronous chains. The analytical results and the experimental results will be compared and discussed extensively