Ensuring software quality in the Web3 ecosystem presents unique challenges due to its decentralized architecture and evolving technical landscape. While international standards such as the SQuaRE (Systems and software Quality Requirements and Evaluation) framework offer structured approaches for quality assurance, they are often perceived as overly theoretical and not directly applicable to blockchain-based applications. This study aims to translate these standards into actionable practices suitable for Web3 environments, thereby supporting compliance and fostering stakeholder trust. Using the Design Science Research methodology, complemented by Lean Startup principles, a practical quality assurance guide was co-developed through collaboration between VOH.CoLAB researchers and the Exeedme project team and inspired by the practical experience in gaming and digital assets trading blockchain-based platforms. The resulting guide includes a structured framework comprising eight testing domains, 16 sub-domains and 108 targeted tests, with the domains addressing critical features of blockchain software, including, functional suitability, integration, security, performance, usability, portability, recoverability and resilience. This work contributes to the operationalization of international quality standards in decentralized technology, promoting more resilient and trustworthy blockchain applications.
In blockchain networks, so-called "full nodes" serve data to and relay transactions from clients through an RPC interface. This serving layer enables integration of "Web3" data, stored on blockchains, with "Web2" mobile or web applications that cannot directly participate as peers in a blockchain network. In practice, the serving layer is dominated by a small number of centralized services ("node providers") that offer permissioned access to RPC endpoints. Clients register with these providers because they offer reliable and convenient access to blockchain data: operating a full node themselves requires significant computational and storage resources, and public (permissionless) RPC nodes lack financial incentives to serve large numbers of clients with consistent performance. Permissioned access to an otherwise permissionless blockchain network raises concerns regarding the privacy, integrity, and availability of data access. To address this, we propose a Permissionless Accountable RPC Protocol (PARP). It enables clients and full nodes to interact pseudonymously while keeping both parties accountable. PARP leverages "light client" schemes for essential data integrity checks, combined with fraud proofs, to keep full nodes honest and accountable. It integrates payment channels to facilitate micro-payments, holding clients accountable for the resources they consume and providing an economic incentive for full nodes to serve. Our prototype implementation for Ethereum demonstrates the feasibility of PARP, and we quantify its overhead compared to the base RPC protocol.
Adrian Victor VEVERA, Andreea Cătălina CRĂCIUN, Mihail Dumitrache, Ionut SANDU · 6 authors
The Domain Name System (DNS) remains a foundational component of the Internet infrastructure, which is frequently exploited by cybercriminals through increasingly diverse and sophisticated attack vectors.This paper explores the evolving cybersecurity challenges faced by domain name systems (DNSs) and their decentralized counterparts in the Web3 ecosystem, particularly the Ethereum Name Service (ENS), as such, it surveys both the established and novel attack patterns.Furthermore, it explores the implications of decentralized naming systems like the ENS, which introduced novel cybersecurity challenges within the Web3 environments and it highlights the future risks and possible research directions related to the transition to decentralized web services.This study provides a comparative analysis of the cyberattacks targeting the DNS and the ENS, highlighting the evolving threat landscape for each of the two ecosystems.By examining the architectural differences between the DNS and ENS, their common attack vectors, and their security mechanisms, it underscores both the distinct vulnerabilities inherent to each ecosystem and the overlapping risks they share.
This paper investigates the global awareness and understanding of Decentralized Finance (DeFi) by analyzing the perceptions of diverse demographic groups. Using a qualitative methodology based on interviews with blockchain professionals and users from multiple countries, this study identifies knowledge gaps, regional differences, and educational factors impacting DeFi adoption. The findings indicate that while awareness is increasing in technologically advanced regions, substantial educational and infrastructural barriers persist in developing countries. Participants shared varied experiences ranging from comprehensive DeFi usage to initial confusion regarding its core concepts, highlighting the uneven distribution of blockchain literacy. Moreover, cultural perceptions, local economic conditions, and trust in traditional financial systems significantly influence the degree of DeFi adoption. Key factors such as language accessibility, availability of localized content, community engagement, and government stance on digital assets also contribute to shaping user attitudes. This paper contributes to the growing body of literature on DeFi by offering practical insights into how global awareness initiatives can support equitable adoption and engagement with decentralized financial systems. It further emphasizes the importance of coordinated efforts between industry stakeholders, educators, and regulators to design scalable and inclusive educational programs that address global disparities in DeFi comprehension and foster informed participation in the decentralized financial landscape.
A common assumption in cryptocurrency markets is a positive relationship between total-value-locked (TVL) and cryptocurrency returns. To test this hypothesis we examine whether the returns of TVL-sorted portfolios can be explained by common cryptocurrency factors. We find evidence that portfolios formed on TVL exhibit returns that are linear functions of aggregate crypto market returns, that is they can be replicated with appropriate weights on the crypto market portfolio. Thus, strategies based on TVL can be priced with standard asset pricing tools. This result holds true both for total TVL and a simple TVL measure that removes a number of ways TVL may be overstated.
While Large Language Models (LLMs) have demonstrated remarkable progress in generating functionally correct Solidity code, they continue to face critical challenges in producing gas-efficient and secure code, which are critical requirements for real-world smart contract deployment. Although recent advances leverage Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) for code preference alignment, existing approaches treat functional correctness, gas optimization, and security as independent objectives, resulting in contracts that may achieve operational soundness but suffer from prohibitive execution costs or dangerous vulnerabilities. To address these limitations, we propose PrefGen, a novel framework that extends standard DPO beyond human preferences to incorporate quantifiable blockchain-specific metrics, enabling holistic multi-objective optimization specifically tailored for smart contract generation. Our framework introduces a comprehensive evaluation methodology with four complementary metrics: Pass@k (functional correctness), Compile@k (syntactic correctness), Gas@k (gas efficiency), and Secure@k (security assessment), providing rigorous multi-dimensional contract evaluation. Through extensive experimentation, we demonstrate that PrefGen significantly outperforms existing approaches across all critical dimensions, achieving 66.7% Pass@5, 58.9% Gas@5, and 62.5% Secure@5, while generating production-ready smart contracts that are functionally correct, cost-efficient, and secure.
A significant body of research in decentralized federated learning focuses on combining the privacy-preserving properties of federated learning with the resilience and transparency offered by blockchain-based systems. While these approaches are promising, they often lack flexible tools to evaluate system robustness under adversarial conditions. To fill this gap, we present FedBlockParadox, a modular framework for modeling and evaluating decentralized federated learning systems built on blockchain technologies, with a focus on resilience against a broad spectrum of adversarial attack scenarios. It supports multiple consensus protocols, validation methods, aggregation strategies, and configurable attack models. By enabling controlled experiments, FedBlockParadox provides a valuable resource for researchers developing secure, decentralized learning solutions. The framework is open-source and built to be extensible by the community.
With the development of the Internet, the amount of data generated by the medical industry each year has grown exponentially. The Electronic Health Record (EHR) manages the electronic data generated during the user's treatment process. Typically, an EHR data manager belongs to a medical institution. This traditional centralized data management model has many unreasonable or inconvenient aspects, such as difficulties in data sharing, and it is hard to verify the authenticity and integrity of the data. The decentralized, non-forgeable, data unalterable and traceable features of blockchain are in line with the application requirements of EHR. This paper takes the most common COVID-19 as the application scenario and designs a COVID-19 health system based on blockchain, which has extensive research and application value. Considering that the public and transparent nature of blockchain violates the privacy requirements of some health data, in the system design stage, from the perspective of practical application, the data is divided into public data and private data according to its characteristics. For private data, data encryption methods are adopted to ensure data privacy. The searchable encryption technology is combined with blockchain technology to achieve the retrieval function of encrypted data. Then, the proxy re-encryption technology is used to realize authorized access to data. In the system implementation part, based on the Hyperledger Fabric architecture, some functions of the system design are realized, including data upload, retrieval of the latest data and historical data. According to the environment provided by the development architecture, Go language chaincode (smart contract) is written to implement the relevant system functions.
Ahmed Hassanein, Kameleddine Benameur, Mohamed M. Mostafa, Wasim Al-Shattarat · 5 authors
This study maps the evolution of scholarly research on blockchain technology in accounting and auditing by conducting comprehensive bibliometric analyses of 359 peer-reviewed research articles authored by 639 scholars from 44 countries. Our analysis identifies the most productive authors, journals, institutions, and countries, highlighting the key contributors to the field. We also use keyword co-occurrence techniques to analyze citation trends, collaboration networks, and thematic structures. Our findings show a compound annual growth rate (CAGR) of 38% in research publications from 2017 to 2024, indicating rapid growth in this area. The collaboration network reveals a technology-centralized pattern, with developed countries leading in cross-border collaborations while developing countries exhibit limited international cooperation. Furthermore, we observe a ‘homophily impact’ among leading authors, where frequent co-authorship occurs around common research topics in blockchain for accounting and auditing. The ‘Matthew Effect’ is evident, as a small group of authors disproportionately dominate the collaboration networks, suggesting that a few influential scholars shape the direction of research. This study provides insights into the thematic evolution of blockchain research in accounting and auditing and suggests a roadmap for future research in this field.
Volatilitas pasar yang tinggi serta potensi keuntungan besar dari cryptocurrency menjadikan prediksi harga sebagai topik penelitian yang menarik. Penelitian ini bertujuan untuk memprediksi harga cryptocurrency Stellar (XLM) dengan menerapkan metode Deep Learning, yaitu Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Data yang digunakan mencakup harga harian XLM selama beberapa tahun terakhir, serta indikator teknikal dan aktivitas perdagangan. Model LSTM dan GRU dievaluasi berdasarkan akurasi dalam memprediksi harga XLM menggunakan metrik MAPE, RMSE, dan MSE. Hasil menunjukkan bahwa meskipun keduanya mampu menangkap pola tren jangka pendek, model GRU memberikan hasil yang lebih unggul. GRU mencatat MAPE sebesar 3.6164%, RMSE sebesar 0.0206, dan MSE sebesar 0.0004. Sementara itu, LSTM mencatat MAPE sebesar 4.5638%, RMSE sebesar 0.0244, dan MSE sebesar 0.0005. Temuan ini menunjukkan bahwa GRU lebih efektif dalam memodelkan kompleksitas dan non-linearitas data harga XLM dibandingkan LSTM. Dengan demikian, GRU dapat dipertimbangkan sebagai metode yang lebih unggul dalam prediksi harga cryptocurrency. Hasil penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan model prediksi yang lebih akurat serta membantu pengambilan keputusan investasi yang lebih bijak. The high market volatility and significant profit potential of cryptocurrencies have made price prediction a compelling area of research. This study aims to predict the price of Stellar (XLM), a widely recognized cryptocurrency, by applying deep learning methods, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The dataset includes daily XLM prices over the past few years, along with technical indicators and trading activity data. The LSTM and GRU models are evaluated based on their accuracy in predicting XLM prices using metrics such as MAPE, RMSE, and MSE. The results show that while both models are capable of capturing short-term trends, the GRU model outperforms LSTM. GRU achieved a MAPE of 3.6164%, RMSE of 0.0206, and MSE of 0.0004, whereas LSTM recorded a MAPE of 4.5638%, RMSE of 0.0244, and MSE of 0.0005. These findings indicate that GRU is more effective in modeling the complexity and non-linearity of XLM price data compared to LSTM. Therefore, GRU can be considered a superior approach for cryptocurrency price prediction. This study is expected to contribute to the development of more accurate forecasting models and to support better investment decision-making.
The rapid integration of artificial intelligence (AI) into high-stakes domains such as healthcare, finance, defense, and governance has created an urgent demand for transparent, auditable, and tamper-resistant decision-making frameworks. While AI models, particularly deep learning architectures, provide unparalleled predictive power, their opaque "black-box" nature often results in accountability gaps, regulatory non-compliance, and ethical challenges. Traditional logging mechanisms fail to capture the complexity and sensitivity of AI-driven decisions, especially in multi-stakeholder ecosystems. Blockchain technology, with its inherent features of immutability, decentralization, and verifiability, presents itself as a transformative solution to this problem. This manuscript proposes and evaluates blockchain-based logging systems for AI auditing, highlighting how distributed ledgers can establish immutable trails of model inputs, intermediate reasoning, and final outputs. The study conducts a comprehensive literature review on AI auditability, trust mechanisms, and blockchain applications, followed by a methodological framework integrating permissioned blockchains with explainable AI (XAI). A statistical analysis is presented to compare blockchain-logging versus traditional logging systems in terms of latency, transparency, energy consumption, scalability, and regulatory compliance. Results indicate that blockchain-based logging improves transparency by 78%, strengthens compliance traceability by 65%, and reduces auditing disputes by 52%, albeit at a moderate computational cost. The paper concludes that blockchain-based logging is not merely a technical enhancement but a regulatory and ethical necessity for next-generation AI systems. Future research directions include hybrid blockchain models, privacy-preserving logging protocols, and AI-governed adaptive consensus mechanisms.
Traditional portfolio optimization techniques predominantly rely on the classical mean–variance framework introduced by Markowitz, which focuses on balancing expected returns against risk, typically measured by variance. However, in volatile and structur-ally unstable markets such as cryptocurrencies, this approach often fails to capture the full spectrum of uncertainty and diversification potential. This paper introduces an al-ternative methodology grounded in entropy, a fundamental concept in information theory that quantifies uncertainty and disorder. By incorporating entropy into the portfolio optimization process, we offer a more generalizable, distribution-free approach that enhances diversification and resilience.We develop and analyze three distinct en-tropy-based models: the maximum Shannon entropy model, the second-order entropy (Tsallis) model, and the maximum weighted Shannon entropy model. These formula-tions extend the traditional mean–variance approach by integrating nonlinear uncer-tainty measures, enabling a richer representation of investor preferences and asset in-terdependencies. Analytical solutions to the proposed models are derived using the method of Lagrange multipliers, ensuring mathematical rigor and interpretability.The proposed models are empirically validated using a portfolio composed of four leading cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—with market data from January to March 2025. The case studies demonstrate how entropy-based optimization leads to well-diversified portfolios, robust under market turbulence and heavy-tailed return distributions. Notably, the models facilitate dynamic adjustments in asset allocation in response to shifts in return–risk characteristics and entropy levels. This study contributes to the ongoing generalization of portfolio theory by positioning entropy as both a diversification enhancer and a structural risk measure. It provides theoretical insight, practical tools for asset allocation in high-volatility environments, and paves the way for future research in entropy-driven financial optimization frameworks.
Ensuring privacy and security in healthcare data sharing is critical due to the sensitive nature of patient information and the growing threat of cyber attacks. This paper explores the development of privacy-preserving data-sharing protocols for healthcare systems by integrating cryptographic techniques and blockchain technology. The study aims to establish a secure framework that facilitates seamless data exchange among healthcare stakeholders while maintaining data integrity, confidentiality, and access control. Key cryptographic mechanisms, including homomorphic encryption, zero-knowledge proofs, and attribute-based encryption, are employed to ensure that only authorized entities can access patient records without exposing sensitive details. Blockchain technology is leveraged to create a decentralized and tamper-resistant ledger, ensuring transparency and auditability in data-sharing transactions. Smart contracts are utilized to enforce predefined access policies automatically, enhancing security and compliance with regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). The findings indicate that the proposed framework significantly mitigates risks associated with unauthorized access, data breaches, and single points of failure. Comparative analysis with traditional centralized systems demonstrates improved efficiency, scalability, and security in healthcare data management. The integration of blockchain and cryptographic techniques ensures robust privacy-preserving mechanisms without compromising accessibility or interoperability. This research provides a novel approach to secure data sharing in healthcare, fostering trust among stakeholders while ensuring compliance with privacy regulations. Future work will focus on optimizing computational efficiency and addressing scalability challenges to facilitate widespread adoption in real-world healthcare ecosystems.
Iis Kurnia Nurhayati, Mohamad Ibrani Shahrimin Adam Assim, Nia Kurniasih, Harry Nuriman · 6 authors
Blockchain technology (BT) has the potential to transform a number of industries, including education. This bibliometric study examines the state of the field of research in education regarding blockchain, highlighting key developments, influential publications and emerging themes. The study uses a comprehensive dataset from Web of Science and Scopus to examine important trends, prominent writers, organizations, publications and cooperative networks to identify critical areas, including credential verification, academic record management and decentralized learning platforms, where blockchain offers unexplored opportunities. By analyzing 134 articles, we uncover a growing interest in blockchain’s applications within the education sector, with research clustered around technology, strategy, finance, broader implications and educational transformation. This study provides a roadmap for future research. The implications provide useful insights for policymakers, educators, and developers of technology. This study draws attention to the potential of blockchain to redefine educational paradigms by bridging technological innovation with pedagogical needs, and invites stakeholders to take part in shaping its transformative journey. However, limitations include data bias and the absence of temporal analysis. Future research should explore ethical and practical challenges, assess blockchain’s impact on education and contribute to a deeper understanding of its role in higher education.
Smart contracts are computer codes that represent contract terms and are designed to run on a blockchain platform, automatically enforced upon receiving predetermined inputs. This technological innovation, a key component of the Fourth Industrial Revolution, provides an advanced and innovative approach to executing contract terms. Incorporating this technology into Shariah-compliant contracts within Islamic Banks (IBs) holds the potential to reduce Shariah non-compliance risk (SNC) and enhance operational transparency, ensuring compatibility with contemporary technological applications. In particular, blockchain-based smart contracts have the potential to be integrated into the operations of IBs’ products that are based on tawarruq contracts. This study aims to investigate the potential application of blockchain-based smart contract technology in tawarruq contract operations within IBs and to suggest directions for future research. This study adopted a qualitative approach, drawing on relevant literature. The findings indicated that blockchain-based smart contracts can address Shariah Non-Compliance (SNC) issues in IB’s tawarruq operations while enhancing transaction transparency. This paper discussed the Shariah and operational challenges associated with blockchain technology and posits that blockchain-based smart contracts can improve the practices of tawarruq contracts within Islamic Banks. This paper offers insights for IB entities and regulatory authorities to evaluate the potential and impact of blockchain-based smart contracts within their operations and the broader financial system.
Luis Blanco-Cocom, Selva Vía, Cristian J. Vaca-Rubio, Engin Zeydan · 26 authors
This paper proposes a highly sustainable and scalable integrated AI-native architecture defining UNified archITecture for Open RAN-enabled Distributed, Scalable and SustainabilitY-enhanced 6G Networks (UNITY-6G) project that can support the diverse requirements of 6G networks by relying on advanced technologies, such as distributed ledger technology, semantic communications, digital network twinning to enhance the performance, cost-efficiency and trustworthiness of integrated 6G network services and applications. The focus is on scalability and sustainability for integrated networks (Non-Terrestrial and Terrestrial Networks, xHaul, Open RAN, Non-Public Networks, Edge, Core and Cloud). Furthermore, we aim to evolve to realtime distributed and network state-aware Open RAN that can leverage the integration of distributed applications in the integrated architecture. This will enable fine-grained data-driven management and control via incorporating dApps, distributed applications that complement existing xApps/rApps and use cases with stricter timing requirements in an integrated network. Common interfaces and protocols will be defined so that different heterogenous domains can communicate seamlessly. To better guide the design, This paper also use the principles of service based architecture for integrated networks and leverage digital twins for network evaluation and considers four use cases targeting: i) Sustainable networks for disaster handling, (ii) Immersive Experience with Real-time XR/holographic communications, (iii) Digital Twin for Integrated 6G Network Evaluation, (iv) MultiRAT O-RAN enabled NPN for supporting time sensitive applications for Industry 4.0.
Sami Mejri, Francisco Jareño, Nasir Khan, Arturo Leccadito
This study examines the impact of geopolitical risk (GPR) on black and green cryptocurrencies during crisis times, focusing on their potential as hedging instruments and safe havens. Using daily data on nine cryptocurrencies (Bitcoin, Ethereum, Binance, Litecoin, Ripple, EOS, IOTA, Stellar and Tezos) and the Geopolitical Risk Index from January 3rd, 2019, to January 20th, 2025, the research employs a Regime-Switching Global Vector Autoregressive (RSGVARX) model and a quantile-on-quantile (QQ) approach to capture heterogeneous responses across market states and quantiles. In addition, the Dynamic Conditional Correlation (DCC) GARCH copula and Dynamic Gerber Correlation (DGC) models assess the hedging effectiveness and optimal portfolio weights of various cryptocurrency pairs. The study uniquely combines the RSGVARX and QQ methods to provide a comprehensive understanding of the dynamic interactions between GPR and cryptocurrency returns and introduces robust portfolio optimisation analysis using advanced econometric models. The results show that the impact of GPR on black cryptocurrencies is generally negative and statistically insignificant in Regime 1, with mixed effects in Regime 2, while green cryptocurrencies show similar heterogeneous responses. Several cryptocurrencies show resilience to GPR shocks in certain scenarios, highlighting their potential as reliable assets in times of geopolitical instability. The portfolio optimisation analysis identifies Bitcoin paired with Ethereum, Binance and Litecoin as the most effective combination for hedging throughout the sample period and during the stressful Russia-Ukraine war and Israeli-Palestinian conflict. These results suggest that investors should consider market states and transition probabilities when developing portfolio strategies involving cryptocurrencies, providing valuable insights for managing risk and ensuring financial stability during geopolitical crises.
In this paper, we design and implement a web crawler system based on the Solana blockchain for the automated collection and analysis of market data for popular non-fungible tokens (NFTs) on the chain. Firstly, the basic information and transaction data of popular NFTs on the Solana chain are collected using the Selenium tool. Secondly, the transaction records of the Magic Eden trading market are thoroughly analyzed by combining them with the Scrapy framework to examine the price fluctuations and market trends of NFTs. In terms of data analysis, this paper employs time series analysis to examine the dynamics of the NFT market and seeks to identify potential price patterns. In addition, the risk and return of different NFTs are evaluated using the mean-variance optimization model, taking into account their characteristics, such as illiquidity and market volatility, to provide investors with data-driven portfolio recommendations. The experimental results show that the combination of crawler technology and financial analytics can effectively analyze NFT data on the Solana blockchain and provide timely market insights and investment strategies. This study provides a reference for further exploration in the field of digital currencies.
Non-fungible tokens (NFTs) are an emerging application of blockchain technology, with the potential to transform various industries, including tourism. Despite conceptual discussions that have highlighted opportunities and challenges associated with using NFTs—such as in digital souvenirs, ticketing systems, loyalty programmes, and conservation initiatives—there is a critical gap in the literature consisting of the lack of a structured methodological framework to empirically evaluate the impact of real-world NFT implementations. This study addresses this gap by proposing a conceptual model and methodological framework designed to assess NFT projects in the tourism sector. The framework integrates diverse data collection methods, advanced analytical techniques (including econometric analysis, natural language processing, and machine learning), and a technological workbench for tracking key performance indicators (KPIs). To demonstrate its applicability, the framework is applied to the Dalmatia NFT project, an exploratory application in cultural tourism. The considered example highlights the potential of NFTs to enhance tourism experiences while addressing challenges such as scalability, sustainability, and user engagement. This study concludes with insights into the framework’s practical implications for stakeholders and outlines future research directions for empirical validation. By bridging the gap between theory and practice, this study aims to provide a robust foundation for effectively integrating NFTs into the tourism industry.
Blockchain technology relies on decentralization to resist faults and attacks while operating without trusted intermediaries. Although industry experts have touted decentralization as central to their promise and disruptive potential, it is still unclear whether the crypto ecosystems built around blockchains are becoming more or less decentralized over time. As crypto plays an increasing role in facilitating economic transactions and peer-to-peer interactions, measuring their decentralization becomes even more essential. We thus propose a systematic framework for measuring the decentralization of crypto ecosystems over time and compare commonly used decentralization metrics. We applied this framework to seven prominent crypto ecosystems, across five distinct subsystems and across their lifetime for over 15 years. Our analysis revealed that while crypto has largely become more decentralized over time, recent trends show a shift toward centralization in the consensus layer, NFT marketplaces, and developers. Our framework and results inform researchers, policymakers, and practitioners about the design, regulation, and implementation of crypto ecosystems and provide a systematic, replicable foundation for future studies.
Atefeh Zareh Chahoki, Maurice Herlihy, Marco Roveri
Smart contracts, the cornerstone of blockchain technology, enable secure, automated distributed execution. Given their role in handling large transaction volumes across clients, miners, and validators, exploring concurrency is critical. This includes concurrent transaction execution or validation within blocks, block processing across shards, and miner competition to select and persist transactions. Concurrency and parallelism are a double-edged sword: while they improve throughput, they also introduce risks like race conditions, non-determinism, and vulnerabilities such as deadlock and livelock. This paper presents the first survey of concurrency in smart contracts, offering a systematic literature review organized into key dimensions. First, it establishes a taxonomy of concurrency levels in blockchain systems and discusses proposed solutions for future adoption. Second, it examines vulnerabilities, attacks, and countermeasures in concurrent operations, emphasizing the need for correctness and security. Crucially, we reveal a flawed concurrency assumption in a major research category, which has led to widespread misinterpretation. This work aims to correct that and guide future research toward more accurate models. Finally, we identify gaps in each category to outline future research directions and support blockchain's advancement.
This paper introduces a Machine Learning (ML) approach for scalability of UTXO-based blockchains, such as Bitcoin. Prior approaches to UTXO set sharding struggle with distributing UTXOs effectively across validators, creating substantial communication overhead due to child-parent transaction dependencies. This overhead, which arises from the need to locate parent UTXOs, significantly hampers transaction processing speeds. Our solution uses ML to optimize not only UTXO set sharding but also the routing of incoming transactions, ensuring that transactions are directed to shards containing their parent UTXOs. At the heart of our approach is a framework that combines contrastive and unsupervised learning to create an embedding space for transaction outputs. This embedding allows the model to group transaction outputs based on spending relationships, making it possible to route transactions efficiently to the correct validation microservices. Trained on historical transaction data with triplet loss and online semi-hard negative mining, the model embeds parent-child spending patterns directly into its parameters, thus eliminating the need for costly, real-time parent transaction lookups. This significantly reduces cross-shard communication overhead, boosting throughput and scalability.
Dinh-Hieu Tran, Nazar Waheed, Yuris Mulya Saputra, Xingqin Lin · 15 authors
With the rapid development of technology, the number of smart mobile users is increasing, accompanied by growing demands from applications such as virtual/augmented reality (VR/XR), remote surgery, autonomous vehicles, and real-time holographic communications, all of which require high transmission rates and ultra-low latency in 6G and beyond networks (6G+). This poses enormous challenges in efficiently deploying large-scale networks, including network design, planning, troubleshooting, optimization, and maintenance, without affecting the user experience. Network Digital Twin (NDT) has emerged as a potential solution, enabling the creation of a virtual model that reflects the actual network, supporting the simulation of various network designs, applying diverse operating policies, and reproducing complex fault scenarios under real-world conditions. This motivate us for this study, where we provide a comprehensive survey of NDT in the context of 6G+, covering areas such as radio access networks (RAN), transport networks, 5G core networks and beyond (5GCORE+), cloud/edge computing, applications (blockchain, health system, manufacturing, security, and vehicular networks), non-terrestrial networks (NTNs), and quantum networks, from both academic and industrial perspectives. In particular, we are the first to provide an in-depth guide and usage of RAN and 5GCORE+ for NDT. Then, we provide an extensive review of foundation technologies such as transport networks, cloud/edge computing, applications, NTNs, and quantum networks in NDT. Finally, we discuss the key challenges, open issues, and future research directions for NDT in the context of 6G+.