Journal of Theoretical and Applied Information Technology
The insurance sector is being transformed through the combination of artificial intelligence (AI) and blockchain technologies. This study proposes the AI-Blockchain Hybrid Smart Contract Model (AIBSCM), which combines AI-based fraud detection with blockchain-based smart contracts to allow for automated insurance claim processing. A synthetic dataset of 1,000 insurance claims was used to train a random forest model, which achieved 92% accuracy on training data; however, real-world testing revealed difficulty in detecting fraudulent claims from under-represented categories. A blockchain simulation was conducted to demonstrate the secure storage and automated execution of claims, with smart contracts giving transparency and immutability. The architecture integrates decentralised oracles, zero-knowledge proofs (ZKPs), federated learning, and a DAO governance mechanism to provide a privacy-conscious, decentralised, and robust solution for the insurance business. Subsequent study will look at real-world deployment and integration with regulations. The integration of these technologies seeks to address traditional insurance systems' issues, such as data privacy concerns and a lack of transparency. By investigating real-world deployment and regulatory compliance, this model has the potential to transform the insurance business by delivering a safe and efficient method for dealing with false claims. This innovative method has the potential to boost client trust while also streamlining insurance company operations. Overall, the combination of blockchain and privacy-conscious technology might result in increased reliability and a transparent insurance sector.
The goal of this research is to analyze political uncertainty's short- and long-term impact on the volatility of Bitcoin throughout the US presidential election period (2023-2024), and test its value as a hedge asset in the face of rising political tensions. The GARCH-MIDAS model used here selects high-frequency daily returns on Bitcoin and low-frequency macroeconomic and political data, such as the Economic Policy Uncertainty Index (EPU), the Volatility Implied Index (VIX), and an irregular dummy variable for political events (POL_EVT). The empirical evidence depicts how Bitcoin is highly sensitive to political shocks, both sudden (short-run) and institutional (long-run), with its volatility speeding up as uncertainty increases. In contrast to traditional safe-haven securities such as gold or government bonds, Bitcoin does not exhibit hedging behavior during times of political turmoil. Instead, it is a high-risk speculation asset, responding in real-time but destabilizing to evolving political events. Moreover, the GARCH-MIDAS model proved to be outstanding in capturing the time and non-linear impacts of uncertainty compared to standard models, buttressing the importance of including political factors when studying the volatility dynamics of cryptocurrencies.
Gabriel Babatunde Iwasokun, Oluwaseyi Segun, Samuel Oluwatayo Ogunlana, Michael Adegoke · 6 authors
The integration of Internet of Things (IoT) devices into modern payment systems has introduced innovative functionalities, but also significant security and performance challenges. IoT devices, such as smart sensors, wearables, and automated vending machines, are typically resource-constrained yet handle sensitive financial transactions that demand robust security mechanisms. Conventional cryptographic solutions are often unsuitable for these environments due to their high computational and memory requirements. This paper presents the design of a lightweight blockchain-based model to secure IoT payment systems by leveraging the Ethereum blockchain and AES-128 encryption. The blockchain token is encrypted with AES-128 to add layer of security before being stored in a database. The model is designed to employ a decentralised digital ledger to record and validate transactions without a central authority, and the transaction is grouped into a block and linked to the preceding block through cryptographic hashes. The chain of blocks forms an immutable record that enhances transparency and security, and the distributed nature of blockchain networks, wherein multiple participants validate each transaction, minimises the risk of fraudulent activities while ensuring consensus is achieved through predefined protocols. Analysis of results from the implementation established the minimization of computational overhead and robust security measures, and was particularly beneficial where the scalability of decentralized systems is required alongside heightened security protocols.
Meeting global forest restoration targets by 2030 requires a transition from labor-intensive and opaque practices to scalable, intelligent, and verifiable systems. This paper introduces a cyber–physical digital twin architecture for forest restoration, structured across four layers: (i) a Physical Layer with drones and IoT-enabled sensors for in situ environmental monitoring; (ii) a Data Layer for secure and structured transmission of spatiotemporal data; (iii) an Intelligence Layer applying AI-driven modeling, simulation, and predictive analytics to forecast biomass, biodiversity, and risk; and (iv) an Application Layer providing stakeholder dashboards, milestone-based smart contracts, and automated climate finance flows. Evidence from Dronecoria, Flash Forest, and AirSeed Technologies shows that digital twins can reduce per-tree planting costs from USD 2.00–3.75 to USD 0.11–1.08, while enhancing accuracy, scalability, and community participation. The paper further outlines policy directions for integrating digital MRV systems into the Enhanced Transparency Framework (ETF) and Article 5 of the Paris Agreement. By embedding simulation, automation, and participatory finance into a unified ecosystem, digital twins offer a resilient, interoperable, and climate-aligned pathway for next-generation forest restoration.
Purpose: This study explores the transformative impact of financial technology (fintech) on the global financial services industry, focusing on innovations, regulatory implications, and challenges. The research aims to identify key technological disruptions, examine the regulatory landscape, and highlight opportunities and risks introduced by fintech. Methodology/approach: A Systematic Literature Review (SLR) was conducted using SCOPUS, IEEE Xplore, and ScienceDirect. Following a structured protocol, 153 peer-reviewed articles (2014–2019) were analysed through thematic and meta-analytical approaches. The study adopted an interpretative philosophy and used the PICOC framework to refine search precision and synthesis. Results/findings: The analysis reveals fintech’s disruptive innovations in financing and payment systems, such as peer-to-peer (P2P) lending, crowdfunding, blockchain-enabled transactions, and mobile payments. These services have enhanced financial inclusion, operational efficiency, and customer accessibility. Regulatory frameworks have evolved in parallel, though challenges remain in addressing moral hazard, cybersecurity, and compliance. Geographically, Asia, particularly China and Indonesia, leads fintech research and implementation. Conclusion: Fintech has significantly reshaped financial ecosystems by enabling decentralized financial services, accelerating digital transactions, and fostering inclusivity. However, cybersecurity risks, limited regulatory clarity, and uneven global adoption continue to impede its sustainable integration. Limitations: The study is limited to English-language literature from 2014–2019 and may not capture recent post-pandemic developments or region-specific innovations in Islamic or informal economies. Contribution: This paper contributes a comprehensive synthesis of fintech’s evolution, identifies existing gaps, and offers insights for policymakers, financial institutions, and researchers to foster a balanced, secure, and innovative financial environment.
With the rise of cryptocurrencies, illicit activities such as money laundering, fraud, and Ponzi schemes have gained attention. Traditional methods using graph neural networks (GNNs) to detect illicit transactions treat the entire transaction network as input, which works well on small networks but struggles with large-scale blockchain data. To address this limitation, the authors propose a neighborhood subgraph-based method that combines GCN and LSTM. The GCN captures information from neighboring nodes for each transaction, enhancing the understanding of the network structure, while the LSTM tracks the sequence and variations of fund flows. Experimental results show that by using 3-hop neighborhood subgraphs, the method outperforms other baseline models while requiring data from only an average of 80 nodes, thereby significantly improving efficiency compared to methods that process the entire transaction network.
This Research paper attempts to examine and analyse the legal nature and law which govern virtual property, covering the concept of ownership, transfer, and regulatory challenges within the metaverse. This Research paper aims to set-out the struggles of traditional legal framework to adapt to the new digital environment consisting of technologies such as blockchain, artificial intelligence (AI), augmented and virtual reality (AR/VR), 3D modelling, and edge computing converge to form the metaverse. The study explains blockchain technology, as it reinforces non-fungible tokens (NFTs) which is the key standard for virtual ownership. It also attempts to analyse how existing legal framework in India for property laws, such as the Transfer of Property Act 1882[1] and the Sale of Goods Act 1930[2], could bring virtual assets under its legal parameters. A comparative analysis of the UK, US, EU, and Indian legal frameworks shows how different legal approaches helps in classification of digital assets. The UK Law Commission’s recommendation demonstrates a progressive shift toward recognising virtual property rights by introducing a new category of “digital objects”.[3] The Research paper highlights the inadequacy of existing property laws for resolving the exclusive cross-jurisdictional and ownership challenges posed by digital environments, concluding that just providing conceptual foundation is not enough. It advocates for a harmonised global governance framework integrating statutory law, soft law principles like the UNIDROIT Principles of International Commercial Contracts[4], and platform-specific regulation to ensure certainty, accountability, and protection of digital ownership.
Jean Baudrillard's (1929–2007) theoretical writings are applied to an examination of today's global virtual economy and society. New advances in virtual culture, which flourished during and after the COVID-19 pandemic—esp., metaverses (immersive virtual worlds), non-fungible tokens, and deepfakes (synthetic media)—are discussed to show the prescience of Baudrillard's theory for how our global consumer society of the image is now defined by the problem of simulation. Baudrillard is shown to have theorized important trends and phenomenon in our contemporary global hyperculture that have hitherto been neglected: non-communication, anti-work, and anti-consumption are, among others, explained as developing phenomena because they are pathologies of a new nihilism, a hatred of capitalism, that is not realized through destruction, but through the simulation and deterrence that now defines contemporary global culture and society.
This systematic review examines how elite athletes are leveraging digital platforms, generative artificial intelligence (AI), and blockchain to build autonomous brands, bypass traditional sport gatekeepers, and develop athlete-owned business models. Drawing on 47 peer-reviewed studies (2016-2025), we synthesise evidence across five domains: athlete branding and self-production, disintermediation, platform-enabled empowerment, AI-driven content innovation, and emerging commercial structures. The findings reveal a decisive shift in sport's power balance, with athletes acting as media producers, cultural influencers, and entrepreneurial actors. Digital platforms enable direct-to-fan engagement, while AI tools lower content production costs whilst personalising interactions and extend global reach. Blockchain facilitates decentralised monetisation and data sovereignty, supporting ventures such as athlete-owned leagues and non-fungible tokens. However, these developments embed new dependencies on platform algorithms and volatile digital markets. From a platform capitalism perspective, athlete autonomy is constrained by corporate-controlled infrastructures; from a value co-creation lens, fan relationships become participatory spaces for shared cultural and commercial value creation. The review highlights governance challenges, including ethical implications of synthetic media, data ownership, and the regulation of AI-enabled branding ecosystems. We argue that sport governance must evolve from a control-oriented model to one that positions athletes as co-creators of value and strategic partners in decision-making. Future research should address equity in digital visibility and sustainable athlete-led business ecosystems. Governance mechanisms that reconcile technological opportunity with autonomy protection should be explored as well. Athletes are no longer peripheral actors in sport's commercial order, they are emerging as its architects, with significant implications for the future of sport governance.
Zero-knowledge proofs (ZKPs) are increasingly deployed in domains such as privacy-preserving authentication, verifiable computation, and secure finance. However, authoring ZK programs remains challenging: unlike conventional software development, ZK programming manifests a fundamental paradigm shift from \textit{imperative computation} to \textit{declarative verification}. This process requires rigorous reasoning about finite field arithmetic and complex constraint systems (which is rare in common imperative languages), making it knowledge-intensive and error-prone. While large language models (LLMs) have demonstrated strong code generation capabilities in general-purpose languages, their effectiveness for ZK programming, where correctness hinges on both language mastery and constraint-level reasoning, remains unexplored. To address this gap, we propose \textsc{ZK-Eval}, a domain-specific evaluation pipeline that probes LLM capabilities on ZK programming at three levels: language knowledge, algebraic primitive competence, and end-to-end program generation. Our evaluation of four state-of-the-art LLMs reveals that while models demonstrate strong proficiency in language syntax, they struggle when implementing and composing algebraic primitives to specify correct constraint systems, frequently producing incorrect programs. Based on these insights, we introduce \textsc{ZK-Coder}, an agentic framework that augments LLMs with constraint sketching, guided retrieval, and interactive repair. Experiments with GPT-o3 on Circom and Noir show substantial gains, with success rates improving from 20.29\% to 87.85\% and from 28.38\% to 97.79\%, respectively. With \textsc{ZK-Eval} and \textsc{ZK-Coder}, we establish a new basis for systematically measuring and augmenting LLMs in ZK code generation to lower barriers for practitioners and advance privacy computing.
Open access
2 source records
Mathematics, Computing, and Information Processing
Blockchain technology has emerged as a transformative force across a multitude of sectors, offering decentralized, transparent, and tamper-proof solutions to conventional problems in data management, finance, supply chain, healthcare, and beyond.Initially popularized through cryptocurrencies, blockchain has since evolved into a broader infrastructure supporting smart contracts, decentralized applications (dApps), and Web3 ecosystems.This survey provides a comprehensive overview of blockchain technology, outlining its fundamental principles including distributed ledgers, consensus mechanisms, cryptographic security, and decentralization.We critically examine various blockchain architectures such as public, private, and consortium blockchains, and explore their relative strengths and limitations.The paper further delves into current trends, emerging use cases, scalability challenges, interoperability issues, and security concerns.By synthesizing recent academic and industry developments, this survey aims to provide researchers and practitioners with a holistic understanding of blockchain's capabilities, current limitations, and future directions.
Web3 applications require execution platforms that maintain confidentiality and integrity without relying on centralized trust authorities. While Trusted Execution Environments (TEEs) offer promising capabilities for confidential computing, current implementations face significant limitations when applied to Web3 contexts, particularly in security reliability, censorship resistance, and vendor independence. This paper presents dstack, a comprehensive framework that transforms raw TEE technology into a true Zero Trust platform. We introduce three key innovations: (1) Portable Confidential Containers that enable seamless workload migration across heterogeneous TEE environments while maintaining security guarantees, (2) Decentralized Code Management that leverages smart contracts for transparent governance of TEE applications, and (3) Verifiable Domain Management that ensures secure and verifiable application identity without centralized authorities. These innovations are implemented through three core components: dstack-OS, dstack-KMS, and dstack-Gateway. Together, they demonstrate how to achieve both the performance advantages of VM-level TEE solutions and the trustless guarantees required by Web3 applications. Our evaluation shows that dstack provides comprehensive security guarantees while maintaining practical usability for real-world applications.
Muhammad Haroon Tariq, Uswa Ihsan, Zaenal Alamsyah
Ownership rights related to land and property represent a highly contentious matter in areas across Pakistan because female inheritors struggle to assert their property rights due to cultural practices along with unclear procedures and traditional document systems. The present government-controlled systems demonstrate inadequate proficiency along with safety protocols to execute fair inheritance distribution, mainly impacting marginalized populations. This research introduces a blockchain system known as the Land Registration and Inheritance Automation System (LRIAS) which prioritizes the female protection of inheritance privileges. The proposed system includes digitalizing the traditional paper-based land registration and inheritance process. The system ensures blockchain security through the implementation of MetaMask together with Web3.js for Ethereum transactions. The blockchain system distributes inheritances through programmed agreements which follow Shariah validation rules. The LRIAS establishes permanent and free-version records that show who owns land and who the legal heirs are. The system enables women to access their inheritance records through verifiable reliable data which cannot be altered. Through the system, authorities can verify inheritance claims and execute them without bureaucratic interference, which minimizes both legal disputes and family conflicts. Experimental tests show that the LRIAS succeeds in safeguarding women’s land inheritance claims and increasing confidence in legal inheritance procedures.
Haruki Kurisaka, Yue Su, Phi Le Nguyen, Kien Nguyen · 5 authors
Abstract The integration of IoT with blockchain technology enhances security and privacy through decentralized, trust-based systems, addressing challenges like single points of failure and limited scalability in traditional IoT architectures. This study evaluates the performance of Ethereum-based IoT systems using resource-constrained devices (Raspberry Pi 4 and Raspberry Pi 3) on a private blockchain. Performance metrics, including CPU, memory, disk usage, power consumption, and latency, were analyzed across three consensus mechanisms: Proof-of-Work (PoW), Proof-of-Authority (PoA), and Proof-of-Stake (PoS). To address the blockchain’s latency performance, we introduced the metrics Transaction-oriented latency (ToL) and Block-oriented latency (BoL) to characterize latency under PoS, capturing the distinctive dynamics of PoS. Our findings show that PoA achieves the lowest resource consumption, with CPU usage reduced by 98% compared to PoW and 20% compared to PoS, and power consumption decreased by 50% from PoW and 14% from PoS. Further, to assess blockchain scalability, we varied transaction transmission rates under PoA, identifying its impact on performance. These findings provide practical guidance for optimizing consensus mechanisms in resource-constrained IoT-blockchain systems.
Majd AbedRabbo, Zeina AlMalak, Fiona Ellis‐Chadwick, Jοãο S. Oliveira
ABSTRACT This paper explores consumers' drivers and motivations behind luxury‐fashion non‐fungible tokens (NFTs) ownership and the implications of the potential ownership of these NFTs on the purchase intentions of physical luxury products of the same brand. Hitherto, little research has been conducted on the consumer's perception of ownership and its effect on physical product purchases. Following the Self Determination Theory (SDT), a two‐step qualitative research approach is implemented due to the lack of empirical research in this area. This study focuses on luxury fashion NFTs and targets millennials and generation Z consumers. A total of 4 focus groups (25 participants) and 6 semi‐structured interviews were conducted to address the objectives of this research. Using thematic analysis, the study identifies 5 key drivers behind NFTs ownership: authenticity, exclusivity, scalability, affordability, and digital literacy. Scalability of luxury fashion NFTs valuation is found to be a critical driver of consumers' ownership intentions. Similarly, digital literacy was identified as a new driver of intentions of ownership of luxury NFTs considering its effect on consumers' social status. Finally, depending on consumers' lifestyle, ownership of luxury fashion NFTs is argued to have a mixed effect on the intentions of ownership of physical luxury products. This research contributes to the development of the understanding of the emerging concept of luxury NFTs and their profound influence on consumers' perceptions of ownership and purchase intentions for physical luxury products.
Open access
Consumer Behavior in Brand Consumption and Identification
This study discusses the behavior of decentralized decision-making of investment in Web3 environment, and the primary factors affecting the decision of investors, including governance with transparence and fair process, opinion of the community, fluctuations of markets, and trends of social networks. From DeFi platforms and markets of NFT, this study finds the inclination of investors towards governance with transparence and fair process when selecting projects, and decisive impacts of opinion of the community on decision. This study also finds significant impacts of social network and fluctuations of markets on short-term investment, and greater risk appetite of investors under more fluctuations of markets. This study verifies the impacts of these factors on the Web3 environment of investment with data simulation under a virtual environment, provides in-depth understanding of behavior of investment under decentralized finance and markets of NFT, and provides valuable references for related projects' design and operation.
Benjamin Appiah, Daniel Commey, Winful Bagyl-Bac, Laurene Adjei · 5 authors
Maximal Extractable Value (MEV) presents a significant challenge to the fairness and efficiency of decentralized finance (DeFi). This paper provides a game-theoretic analysis of the strategic interactions within the MEV supply chain, involving searchers, builders, and validators. A three-stage game of incomplete information is developed to model these interactions. The analysis derives the Perfect Bayesian Nash Equilibria for primary MEV attack vectors, such as sandwich attacks, and formally characterizes attacker behavior. The research demonstrates that the competitive dynamics of the current MEV market are best described as Bertrand-style competition, which compels rational actors to engage in aggressive extraction that reduces overall system welfare in a prisoner’s dilemma-like outcome. To address these issues, the paper proposes and evaluates mechanism design solutions, including commit–reveal schemes and threshold encryption. The potential of these solutions to mitigate harmful MEV is quantified. Theoretical models are validated against on-chain data from the Ethereum blockchain, showing a close alignment between theoretical predictions and empirically observed market behavior.
Decentralized data-feed systems enable blockchain-based smart contracts to access off-chain information by aggregating values from multiple oracles. To improve accuracy, these systems typically use an aggregation function, such as majority voting, to consolidate the inputs they receive from oracles and make a decision. Depending on the final decision and the values reported by the oracles, the participating oracles are compensated through shared rewards. However, such incentive mechanisms are vulnerable to mirroring attacks, where a single user controls multiple oracles to bias the decision of the aggregation function and maximize rewards. This paper analyzes the impact of mirroring attacks on the reliability and dependability of majority voting-based data-feed systems. We demonstrate how existing incentive mechanisms can unintentionally encourage rational users to implement such attacks. To address this, we propose a new incentive mechanism that discourages Sybil behavior. We prove that the proposed mechanism leads to a Nash Equilibrium in which each user operates only one oracle. Finally, we discuss the practical implementation of the proposed incentive mechanism and provide numerical examples to demonstrate its effectiveness.
Regan Meloche, Durga Sivakumar, Amal A. Anda, Sofana Alfuhaid · 7 authors
Monitoring the compliance of contract performance against legal obligations is important in order to detect violations, ideally, as soon as they occur. Such monitoring can nowadays be achieved through the use of smart contracts, which provide protection against tampering as well as some level of automation in handling violations. However, there exists a large gap between natural language contracts and smart contract implementations. This paper introduces a Web-based environment that partly fills that gap by supporting the user-assisted refinement of Symboleo specifications corresponding to legal contract templates, followed by the automated generation of monitoring smart contracts deployable on the Hyperledger Fabric platform. This environment, illustrated using a sample contract from the transactive energy domain, shows much potential in accelerating the development of smart contracts in a legal compliance context.
The paper presents an opposing rule-based signed Friedkin-Johnsen (SFJ) model for the evolution of opinions in arbitrary network topologies with signed interactions and stubborn agents. The primary objective of the paper is to analyse the emergent behaviours of the agents under the proposed rule and to identify the key agents which contribute to the final opinions, characterised as influential agents. We start by presenting some convergence results which show how the opinions of the agents evolve for a signed network with any arbitrary topology. Throughout the paper, we classify the agents as opinion leaders (sinks in the associated condensation graph) and followers (the rest). In general, it has been shown in the literature that opinion leaders and stubborn agents drive the opinions of the group. However, the addition of signed interactions reveals interesting behaviours wherein opinion leaders can now become non-influential or less influential. Further, while the stubborn agents always continue to remain influential, they might become less influential owing to signed interactions. Additionally, the signed interactions can drive the opinions of the agents outside of the convex hull of their initial opinions. Thereafter, we propose the absolute influence centrality measure, which allows us to quantify the overall influence of all the agents in the network and also identify the most influential agents. Unlike most of the existing measures, it is applicable to any network topology and considers the effect of both stubbornness and signed interactions. Finally, simulations are presented for the Bitcoin Alpha dataset to elaborate the proposed results.
Aim . To reveal the ideological nature of digital decentralization as a systemic challenge to traditional state sovereignty and to identify risks for modern states amid technological transformation. Methodology . The core of the study comprises an analysis of key digital decentralization ideologies (crypto-anarchism, cyber-syndicalism, cypherpunk), their technological foundations, and implementation practices. A comparative analysis of foundational manifestos by crypto-anarchists and cypherpunks (T. May, E. Hughes) was conducted, and the evolution of decentralized movements was synthesized. Results . The analysis demonstrated that the synergy of technologies and extra-systemic ideologies creates parallel governance systems undermining the state’s monopoly on regulating finance, information, law, and the exercise of power. Threats to modern states include: erosion of trust in institutions, use of decentralized digital resources for protest mobilization, sanctions evasion via cryptocurrencies, and increased citizen registrations in virtual jurisdictions operating beyond national law. Research implications . Proposals for state adaptation are formulated: shifting from technology bans to dialogue with IT communities and developing preventive measures. The author introduces an original interpretation of digital decentralization as “engineering autocracy”, where algorithmic power replaces political-legal mechanisms. The study reframes issues of state sovereignty in the context of competition with decentralized anti-systems.
This study presents a comprehensive empirical comparison between quantum machine learning (QML) and classical machine learning (CML) approaches in Automated Market Makers (AMM) and Decentralized Finance (DeFi) trading strategies through extensive backtesting on 10 models across multiple cryptocurrency assets. Our analysis encompasses classical ML models (Random Forest, Gradient Boosting, Logistic Regression), pure quantum models (VQE Classifier, QNN, QSVM), hybrid quantum-classical models (QASA Hybrid, QASA Sequence, QuantumRWKV), and transformer models. The results demonstrate that hybrid quantum models achieve superior overall performance with 11.2\% average return and 1.42 average Sharpe ratio, while classical ML models show 9.8\% average return and 1.47 average Sharpe ratio. The QASA Sequence hybrid model achieves the highest individual return of 13.99\% with the best Sharpe ratio of 1.76, demonstrating the potential of quantum-classical hybrid approaches in AMM and DeFi trading strategies.
Blockchain technology offers decentralization and security but struggles with scalability, particularly in enterprise settings where efficiency and controlled access are paramount. Sharding is a promising solution for private blockchains, yet existing approaches face challenges in coordinating shards, ensuring fault tolerance with limited nodes, and minimizing the high overhead of consensus mechanisms like PBFT. This paper proposes the Range-Based Sharding (RBS) Protocol, a novel sharding mechanism tailored for enterprise blockchains, implemented on Quorum. Unlike traditional sharding models such as OmniLedger and non-sharding Corda framework, RBS employs a commit-reveal scheme for secure and unbiased shard allocation, ensuring fair validator distribution while reducing cross-shard transaction delays. Our approach enhances scalability by balancing computational loads across shards, reducing consensus overhead, and improving parallel transaction execution. Experimental evaluations demonstrate that RBS achieves significantly higher throughput and lower latency compared to existing enterprise sharding frameworks, making it a viable and efficient solution for largescale blockchain deployments.
The Open Network (TON) is a high-performance blockchain platform designed for scalability and efficiency, leveraging an asynchronous execution model and a multi-layered architecture. While TON's design offers significant advantages, it also introduces unique challenges for smart contract development and security. This paper introduces a comprehensive audit checklist for TON smart contracts, based on an empirical analysis of 34 professional audit reports containing 233 real-world vulnerabilities. The checklist addresses TON-specific challenges, such as asynchronous message handling, and provides actionable insights for developers and auditors. We also present detailed case studies of vulnerabilities in TON smart contracts, highlighting their implications and offering lessons learned. To validate practical utility, we conducted a practitioner survey (n=11 complete responses), confirming the checklist's value alongside automated tools. By adopting this checklist, developers and auditors can systematically identify and mitigate vulnerabilities, enhancing the security and reliability of TON-based projects. Our work bridges the gap between Ethereum's mature audit methodologies and the emerging needs of the TON ecosystem, fostering a more secure and robust blockchain environment.