HybridChain: Fast, Accurate, and Secure Transaction Processing with Distributed Learning

In order to fully unlock the transformative power of distributed ledgers and blockchains, it is crucial to develop innovative consensus algorithms that can overcome the obstacles of security, scalability, and interoperability, which currently hinder their widespread adoption. This paper introduces HybridChain that combines the advantages of sharded blockchain and DAG distributed ledger, and a consensus algorithm that leverages decentralized learning. Our approach involves validators exchanging perceptions as votes to assess potential conflicts between transactions and the witness set, representing input transactions in the UTXO model. These perceptions collectively contribute to an intermediate belief regarding the validity of transactions. By integrating their beliefs with those of other validators, localized decisions are made to determine validity. Ultimately, a final consensus is achieved through a majority vote, ensuring precise and efficient validation of transactions. Our proposed approach is compared to the existing DAG-based scheme IOTA and the sharded blockchain Omniledger through extensive simulations. The results show that IOTA has high throughput and low latency but sacrifices accuracy and is vulnerable to orphanage attacks especially with low transaction rates. Omniledger achieves stable accuracy by increasing shards but has increased latency. In contrast, the proposed HybridChain exhibits fast, accurate, and secure transaction processing, and excellent scalability

Performance-Based Analysis of Blockchain Scalability Metric

Cryptocurrencies like Bitcoin and Ethereum, are widely known applications of blockchain technology, have drawn much attention and are largely recognized in recent years. Initially Bitcoin and Ethereum processed 7 and 15 Transactions Per Second (TPS) respectively, whereas VISA and Paypal process 1700 and 193 TPS respectively. The biggest challenge to blockchain adoption is scalability, defined as the capacity to change the block size to handle the growing amount of load. This paper attempts to present the existing scalability solutions which are broadly classified into three layers: Layer 0 solutions focus on optimization of propagation protocol for transactions and blocks, Layer 1 solutions are based on the consensus algorithms and data structure, and Layer 2 solutions aims to decrease the load of the primary chain by implementing solutions outside the chain. We present a classification and comparison of existing blockchain scalability solutions based on performance along with their pros and cons

Intelligent Block Assignment for Blockchain Based Wireless IoT Systems

In legacy blockchain based systems, each involved node has to store a complete blockchain to ensure the system security without any central authoritative controller. However, it is usually impossible for a wireless IoT node to store a complete blockchain, especially for those simple sensor nodes without sufficient storage and computing resources. In this paper, we propose a block assignment scheme for blockchain based wireless IoT systems with aim to tackle the blockchain storage problem. Specifically, we propose to maintain a complete blockchain by a set of IoT nodes in a collaborative way on the premise of ensuring that each node can check every transaction. On the other hand, we should save the storage space of IoT nodes to the greatest extent for saving more blocks so as to maximize the lifetime of IoT nodes. We formulate this optimal block assignment problem as a 0-1 mixed integer-programming problem. We propose to incorporate Chaotic optimized algorithm into Genetic algorithm to provide an efficient near-optimal solution. Compared with the brute-force and conventional Genetic algorithms, our proposed algorithm can achieve the minimum storage occupancy to store blocks. Meanwhile, the proposed algorithm has the lowest computational complexity

Adaptive Storage Optimization Scheme for Blockchain-IIoT Applications Using Deep Reinforcement Learning

Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment

Contemporary global challenges in geopolitics, security policy and world economy

This is the second time that the International PhD Conference has been organized by the International Relations Multidisciplinary Doctoral School of Corvinus University of Budapest. We hope this is a sign that we have created a tradition, and that the conference will be organized in the future as well. As in the previous year, most of the presentations given at this year’s conference will again be published in a collected volume in the form of edited studies, with the aim of promoting the publication performance of PhD students.The comprehensive profile of the Doctoral School, the diversity of its three subprograms – International and Security Studies, World Economy and Geopolitics – is reflected in the various topics of the studies in this volume. These include e.g. security and defence policy, challenges the world economy is facing nowadays, the institutions and policies of the European Union, the emerging powers of Asia, as well as sustainability and other important, highly relevant issues. The regions examined in the studies range from the EU through the Arab world to Latin America and Asia, and countries such as the United States, Russia, Ukraine, China, India, Jordan and Tunisia are analysed, to name just a few.The multidisciplinary nature of the Doctoral School has long been expressed in its name, mainly due to the fact that it is entitled to award degrees in two disciplines (economics and political science). Multidisciplinarity is also manifested in the diversity of the topics of this volume. Not only multidisciplinarity, but also interdisciplinarity, the presence of “frontiers” in the field of mutually interdependent disciplines can be traced in the articles, as the authors refer to e.g. law, history, security policy as well as theories of international relations

An Empirical Investigation Of Information Technology Mediated Customer Services In China

Information technology mediated customer service is a reality of the 21st century. More and more companies have moved their customer services from in store and in person to online through computer or mobile devices. Using 208 respondents collected from two Chinese universities, this paper investigates customer preference over two service delivery model (either in store or online) on five type of purchasing (retail, eating-out, banking, travel and entertainment) and their perception difference in customer service quality between those two delivery model. Results show that a majority of Chinese students prefer in store and in person for eating out. For ordering tickets for travel and entertainment, they prefer computer/mobile device. For retail purchasing and banking, less than half of the students prefer in person services. In general, the results show that ordering through computer/mobile devices has become more popular in China and has received higher rating for most of customer service quality except security compared to ordering in store. In addition, it is found that there exist a gender difference in purchasing preference and perception in service delivery quality in China