Blockchain Papers

Ahlem Lamine

This study provides an in-depth analysis of the dynamic connectedness between G7 stock market indices, traditional cryptocurrencies (Bitcoin, Ethereum), gold, digital gold (PAXG, XAUT), and companies specializing in artificial intelligence (AI). Covering the period from 2020 to 2024, the analysis focuses on four distinct periods: the COVID-19 pandemic, the Russia-Ukraine conflict, the banking crisis triggered by the collapse of Silicon Valley Bank in March 2023 and the speculative rise in the gold markets in 2024. The methodology employs a Quantile Vector Autoregressive (QVAR) connectivity approach, starting with the median quantile and systematically extending to various quantiles to capture the entire distribution of connectedness under different market conditions. Our results reveal significant fluctuations in the Total Connectivity Index (TCI) during the studied crises and demonstrate how the roles of key assets—Bitcoin, Ethereum, gold, PAXG, XAUT, and AI firms—shift between being net emitters and receivers of shocks. These shifts underscore the importance of asset selection in crafting effective hedging strategies. Specifically, we observe that G7 investors adopt varying diversification strategies depending on their domestic market conditions and the specific crisis period. The study highlights that assets for diversification and risk reduction vary by country and crisis. Traditional cryptocurrencies and AI companies in general emerge as promising diversification tools, especially in times of technological disruption and economic uncertainty. Several financial implications for investors and policymakers are proposed, providing insights for optimizing portfolio resilience in the face of global market volatility.

Florentin Şerban, Silvia Dedu

Portfolio optimization is a cornerstone of modern financial decision-making, tradition-ally based on the mean–variance model introduced by Markowitz. However, this framework relies on restrictive assumptions—such as normally distributed returns and symmetric risk preferences—that often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return max-imization, mean absolute deviation (MAD) minimization, and entropy-based diversifi-cation into a unified optimization structure: the Mean–Deviation–Entropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average mag-nitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are considered—Shannon entropy, Tsallis entropy, and cumulative residual Sharma–Taneja–Mittal entropy (CR-STME)—to explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and en-tropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical mean–variance and mean–MAD models indicate that the MDE model achieves improved di-versification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allo-cation, and decentralized finance, especially under high-uncertainty market conditions.

Yating Liu, Xing Su, Hao Wu, Sijin Li · 7 authors

Adversarial smart contracts, mostly on EVM-compatible chains like Ethereum and BSC, are deployed as EVM bytecode to exploit vulnerable smart contracts for financial gain. Detecting such malicious contracts at the time of deployment is an important proactive strategy to prevent losses from victim contracts. It offers a better cost-benefit ratio than detecting vulnerabilities on diverse potential victims. However, existing works are not generic with limited detection types and effectiveness due to imbalanced samples, while the emerging LLM technologies, which show their potential in generalization, have two key problems impeding its application in this task: hard digestion of compiled-code inputs, especially those with task-specific logic, and hard assessment of LLM's certainty in its binary (yes-or-no) answers. Therefore, we propose a generic adversarial smart contracts detection framework FinDet, which leverages LLM with two enhancements addressing the above two problems. FinDet takes as input only the EVM bytecode contracts and identifies adversarial ones among them with high balanced accuracy. The first enhancement extracts concise semantic intentions and high-level behavioral logic from the low-level bytecode inputs, unleashing the LLM reasoning capability restricted by the task input. The second enhancement probes and measures the LLM uncertainty to its multi-round answering to the same query, improving the LLM answering robustness for binary classifications required by the task output. Our comprehensive evaluation shows that FinDet achieves a BAC of 0.9374 and a TPR of 0.9231, significantly outperforming existing baselines. It remains robust under challenging conditions including unseen attack patterns, low-data settings, and feature obfuscation. FinDet detects all 5 public and 20+ unreported adversarial contracts in a 10-day real-world test, confirmed manually.

Andry Alamsyah, Said Fikri Naufal Ramdhani

• Blockchain-based system enables policy-grade traceability for plastic credits. • Smart contracts enforce automated compliance with EPR and ESG frameworks. • DApp supports decentralized oversight, reducing audit burden on regulators • System logs immutable offset records aligned with circular economy targets. • Architecture offers scalable digital infrastructure for waste policy integration. Plastic credit schemes are increasingly adopted to mitigate plastic pollution, yet existing systems remain centralized, opaque, and prone to double counting and fraud. This study proposes and validates a plastic credit system that leverages blockchain technology aimed at enhancing transparency, traceability, and accountability in plastic recovery efforts. A modular three-layer architecture was implemented, comprising a user interaction layer, a blockchain execution layer, and a utility layer for metadata and analytics integration. The system employs two smart contracts on the Polygon Proof-of-Stake (PoS) mainnet using Ethereum standards: ERC-20 for fungible tokenization of plastic credits and ERC-721 for non-fungible certificate issuance. Functional testing confirmed successful execution of token lifecycle operations. Stress testing across 5000 sequential transactions yielded stable performance, with average confirmation times of 5.29 s for fungible token operations and 5.59 s for non-fungible processes. A decentralized application (DApp) was developed to support role-based interaction, credit traceability, and certificate validation. User evaluation returned a high usability score (86.4%), while benchmarking against existing platforms demonstrated improved auditability, automation, and stakeholder control. These findings indicate that blockchain infrastructure can enable decentralized, tamper-resistant plastic credit systems. The proposed model provides a scalable foundation for Extended Producer Responsibility (EPR) compliance and plastic waste traceability, which could potentially support the credibility of Environmental, Social, and Governance (ESG) reporting and supporting circular economy transitions across diverse policy and economic contexts.

Jitendra Sharma, Jigyasu Dubey

Transaction-Ordering Dependence (TOD) is a potential vulnerability of blockchain-based smart contracts, which allows malicious actors to exploit the order of transactions to obtain financial profit through front-running and back-running strategies.The purpose of this paper is to present ZkDelay, a new framework that jointly uses commitment schemes and Verifiable Delay Functions (VDFs) to counter TOD in decentralized applications.ZkDelay introduces a two-step transaction scheme: a user makes a cryptographic commitment to a transaction without announcing its purpose, and then, upon completing a verifiable delay with a VDF, the intended transaction can be revealed and carried out.This temporal discontinuity, combined with cryptographic acknowledgments, prevents adversaries from interfering with actionable knowledge in real-time, thereby eliminating any orderingbased attack possibilities.Moreover, ZkDelay is transparent and trustless, as it can be used to verify both commitments and delay execution through zero-knowledge proofs, without leaking sensitive data.Additional sections dedicated to rigorous security analysis and performance analysis in Ethereum-like environments are provided in the paper, demonstrating that ZkDelay incurs only a low amount of computational overhead and that it exponentially improves resistance to TOD attacks.The solution can be deployed in existing smart contract systems and adapted to DeFi protocols, order-sensitive auctions, and other mechanisms.ZkDelay addresses the challenge of integrating privacy-preserving mechanisms with the fairness of execution by providing a scalable and practical solution to one of the most prevalent security issues in smart contract environments.

Mohammed M. Alenazi, Fawwad Hassan Jaskani

The volatile nature of cryptocurrency markets demands real-time analytical capabilities that traditional centralized computing architectures struggle to provide. This paper presents a novel hybrid cloud–edge computing framework for cryptocurrency market forecasting, leveraging distributed systems to enable low-latency prediction models. Our approach integrates machine learning algorithms across a distributed network: edge nodes perform real-time data preprocessing and feature extraction, while the cloud infrastructure handles deep learning model training and global pattern recognition. The proposed architecture uses a three-tier system comprising edge nodes for immediate data capture, fog layers for intermediate processing and local inference, and cloud servers for comprehensive model training on historical blockchain data. A federated learning mechanism allows edge nodes to contribute to a global prediction model while preserving data locality and reducing network latency. The experimental results show a 40% reduction in prediction latency compared to cloud-only solutions while maintaining comparable accuracy in forecasting Bitcoin and Ethereum price movements. The system processes over 10,000 transactions per second and delivers real-time insights with sub-second response times. Integration with blockchain ensures data integrity and provides transparent audit trails for all predictions.

Marcin Wątorek, Marija Bezbradica, Martin Crane, Jarosław Kwapień · 5 authors

Based on the cryptocurrency market dynamics, this study presents a general methodology for analyzing evolving correlation structures in complex systems using the $q$-dependent detrended cross-correlation coefficient ρ(q,s). By extending traditional metrics, this approach captures correlations at varying fluctuation amplitudes and time scales. The method employs $q$-dependent minimum spanning trees ($q$MSTs) to visualize evolving network structures. Using minute-by-minute exchange rate data for 140 cryptocurrencies on Binance (Jan 2021-Oct 2024), a rolling window analysis reveals significant shifts in $q$MSTs, notably around April 2022 during the Terra/Luna crash. Initially centralized around Bitcoin (BTC), the network later decentralized, with Ethereum (ETH) and others gaining prominence. Spectral analysis confirms BTC's declining dominance and increased diversification among assets. A key finding is that medium-scale fluctuations exhibit stronger correlations than large-scale ones, with $q$MSTs based on the latter being more decentralized. Properly exploiting such facts may offer the possibility of a more flexible optimal portfolio construction. Distance metrics highlight that major disruptions amplify correlation differences, leading to fully decentralized structures during crashes. These results demonstrate $q$MSTs' effectiveness in uncovering fluctuation-dependent correlations, with potential applications beyond finance, including biology, social and other complex systems.

Md. Zaki Muzahid

In the Ethereum blockchain network, high transaction fees due to limited block space and high demand necessitate scalable solutions.Layer 2 (L2) scaling solutions, particularly rollups, offer a promising approach by processing transactions offchain and posting compressed data to the main chain (Layer 1).However, current L2 rollups rely heavily on centralized sequencer nodes, which introduces centralization risks and single points of failure.Thus, to address these concerns, this paper explores the existing issues associated with centralized sequencers exemplified by real-life incidents.Consequently, reviews the existing decentralized sequencer models by describing their operations.In addition, this study proposes a novel approach of decentralizing sequencers leveraging the Delegated Proof of Stake (DPoS) consensus mechanism depicting its' components and step by step procedures.Finally, providing comparison among the novel approach and the existing decentralized sequencer frameworks along with their limitations.

Bence Soóki-Tóth, István András Seres, Kamilla Kara, Ábel Nagy · 6 authors

The long-term success of cryptocurrencies largely depends on the incentive compatibility provided to the validators. Bribery attacks, facilitated trustlessly via smart contracts, threaten this foundation. This work introduces, implements, and evaluates three novel and efficient bribery contracts targeting Ethereum validators. The first bribery contract enables a briber to fork the blockchain by buying votes on their proposed blocks. The second contract incentivizes validators to voluntarily exit the consensus protocol, thus increasing the adversary's relative staking power. The third contract builds a trustless bribery market that enables the briber to auction off their manipulative power over the RANDAO, Ethereum's distributed randomness beacon. Finally, we provide an initial game-theoretical analysis of one of the described bribery markets.

Stefanos Chaliasos, Conner Swann, Sina Pilehchiha, Nicolas Mohnblatt · 6 authors

Rollups have become the de facto scalability solution for Ethereum, securing more than $55B in assets. They achieve scale by executing transactions on a Layer 2 ledger, while periodically posting data and finalizing state on the Layer 1, either optimistically or via validity proofs. Their fees must simultaneously reflect the pricing of three resources: L2 costs (e.g., execution), L1 DA, and underlying L1 gas costs for batch settlement and proof verification. In this work, we identify critical mis-pricings in existing rollup transaction fee mechanisms (TFMs) that allow for two powerful attacks. Firstly, an adversary can saturate the L2's DA batch capacity with compute-light data-heavy transactions, forcing low-gas transaction batches that enable both L2 DoS attacks, and finality-delay attacks. Secondly, by crafting prover killer transactions that maximize proving cycles relative to the gas charges, an adversary can effectively stall proof generation, delaying finality by hours and inflicting prover-side economic losses to the rollup at a minimal cost. We analyze the above attack vectors across the major Ethereum rollups, quantifying adversarial costs and protocol losses. We find that the first attack enables periodic DoS on rollups, lasting up to 30 minutes, at a cost below 2 ETH for most rollups. Moreover, we identify three rollups that are exposed to indefinite DoS at a cost of approximately 0.8 to 2.7 ETH per hour. The attack can be further modified to increase finalization delays by a factor of about 1.45x to 2.73x, compared to direct L1 blob-stuffing, depending on the rollup's parameters. Furthermore, we find that the prover killer attack induces a finalization latency increase of about 94x. Finally, we propose comprehensive mitigations to prevent these attacks and suggest how some practical uses of multi-dimensional rollup TFMs can rectify the identified mis-pricing attacks.

Chidananda Ningthoujam, Basanta Thoudam, Mutum Bıdyaranı Devi

The global carbon credit trading market faces significant challenges including lack of real-time verification, double-spending issues, and insufficient transparency in emission measurements. This paper presents a novel blockchain-enabled framework integrating Internet of Things (IoT) sensors for automated carbon credit generation and trading. Our proposed system combines tamper-proof IoT sensor networks with smart contract automation to address current limitations in carbon credit systems. The methodology employs distributed sensor nodes equipped with CO2, temperature, and humidity sensors connected to an Ethereum-based blockchain network. Through extensive simulation and real-world testing, our system demonstrates 99.2% accuracy in emission measurement and real-time carbon credit generation. The framework reduces verification time by 87% compared to traditional manual verification processes while ensuring immutable transaction records. Key contributions include: (1) a decentralized IoT-blockchain architecture for carbon monitoring, (2) smart contract protocols for automated credit generation, (3) a novel consensus mechanism for sensor data validation, and (4) comprehensive security analysis demonstrating resistance to common blockchain attacks. Results indicate significant potential for transforming carbon credit markets through enhanced transparency, reduced fraud, and improved environmental monitoring accuracy.

Mihaela Gavrilă, Mădălina-Giorgiana Murariu, Delia-Elena Bărbuță, Marin Fotache · 6 authors

This paper presents the design and implementation of a blockchain-secured system for monitoring driver sobriety and real-time geolocation. The proposed platform integrates a Modular Sensor Battery (MSB) for detecting alcohol concentration in exhaled air, a centralized Data Collection Platform (DC Platform) for real-time data visualization and storage, and a complementary physiological monitoring device—the IoT Fit-Bit Smart Band (IFSB)—which captures heart rate and blood oxygen saturation as alternative indicators when breath-based sensing may be compromised. The MSB, the DC Platform, integration with the IoT FitBit Smart Band, and the blockchain-based data management architecture represent the authors’ direct contribution to both the conceptual design and technical implementation. These elements are introduced as part of a unified, fully integrated system designed to enable non-invasive sobriety monitoring and secure data integrity in vehicular contexts. To ensure data authenticity, a custom Ethereum smart contract stores cryptographic hashes of sensor readings, enabling decentralized, tamper-evident verification without exposing sensitive medical information. The system was validated in a controlled experimental environment, confirming its operational robustness and demonstrating its potential to improve road safety through secure, real-time sobriety detection and geolocation tracking.

Esmeide Leal

Non-Fungible Tokens (NFTs) have emerged as a transformative blockchain-based technology, enabling unique digital ownership and novel applications across art, gaming and Decentralized Finance (DeFi).However, the rapid evolution of NFT ecosystems has exposed critical challenges in scalability, security and interoperability, driven by the underlying blockchain architectures and software design paradigms.This article presents a systematic review of state-of-the-art blockchain architectures supporting NFTs, analyzing Layer-1 and Layer-2 solutions, consensus mechanisms and smart contract design patterns.We further explore software design best practices for NFT platforms, including gas optimization, upgradeability and anti-fraud mechanisms.Through a comparative analysis of Ethereum, Solana, Flow and Layer-2 frameworks like Polygon, we identify trade-offs in decentralization, throughput and cost.Finally, we highlight open challenges and future directions, such as cross-chain interoperability and energy-efficient NFT minting.This work serves as a comprehensive reference for researchers and practitioners aiming to advance NFT infrastructure.

Vabuk Pahari, Andrea Canidio

We analyze 15,097 blocks proposed for inclusion in Ethereum's blockchain over an eight-minute window on December 3, 2024, during which 38 blocks were added to the chain. We classify transactions as exclusive -- appearing only in blocks from a single builder -- or private -- absent from the public mempool but included in blocks from multiple builders. We find that, depending on the methodology, exclusive transactions account for between 77.2% and 84% of the total fees paid by transactions in winning blocks. Moreover, we show that exclusivity cannot be fully attributed to persistent relationships between senders and builders: only between 7% and 8.4% of all on-chain exclusive transaction value originates from senders who route exclusively to one builder. Finally, we observe that transaction exclusivity is dynamic. Some transactions are exclusive at the start of a bidding cycle but later appear in blocks from multiple builders. Other transactions remain exclusive to a losing builder for two or three cycles before appearing in the public mempool. These transactions are therefore delayed and then exposed to potential attacks.

Qian’ang Mao, Jiaxin Wang, Feng, Zhiqi, Yi Zhang · 5 authors

Cryptocurrencies and Web3 applications based on blockchain technology have flourished in the blockchain research field. Unlike Bitcoin and Ethereum, due to its unique architectural designs in consensus mechanisms, resource management, and throughput, TRON has developed a more distinctive ecosystem and application scenarios centered around stablecoins. Although it is popular in areas like stablecoin payments and settlement, research on analyzing on-chain data from the TRON blockchain is remarkably scarce. To fill this gap, this paper proposes a comprehensive data extraction and exploration framework for the TRON blockchain. An innovative high-performance ETL system aims to efficiently extract raw on-chain data from TRON, including blocks, transactions, smart contracts, and receipts, establishing a research dataset. An in-depth analysis of the extracted dataset reveals insights into TRON's block generation, transaction trends, the dominance of exchanges, the resource delegation market, smart contract usage patterns, and the central role of the USDT stablecoin. The prominence of gambling applications and potential illicit activities related to USDT is emphasized. The paper discusses opportunities for future research leveraging this dataset, including analysis of delegate services, gambling scenarios, stablecoin activities, and illicit transaction detection. These contributions enhance blockchain data management capabilities and understanding of the rapidly evolving TRON ecosystem.

Md. Hassan, Mst. Umme Habiba, Mst. Yesmine Akter, Md. Alamgir Kobir · 5 authors

Cryptocurrency research is vital for promoting safe, informed, and responsible adoption, benefiting governments, businesses, and individuals alike. With its growing impact on finance, technology, and the global economy, further study is essential. This research reviews literature on Bitcoin and Ethereum, focusing on their features, similarities, advantages, and disadvantages. Sources from journals, reports, proceedings, and other materials were examined to synthesize existing knowledge. The study highlights that while cryptocurrencies offer opportunities, their volatility and regulatory uncertainties pose significant risks. Therefore, cautious and informed decision-making is necessary for users and investors. Overall, this study provides valuable insights for policymakers, practitioners, and researchers, contributing to better strategies for the responsible adoption and regulation of cryptocurrencies.

R.I. Mordvinov

The article presents a comprehensive overview of zero-knowledge proof (ZKP) protocols as a fundamental concept of modern cryptography. The historical background of their emergence and the main properties ensuring reliability and confidentiality, i.e., completeness, soundness, and zero-knowledge — are considered. A classification of protocols into interactive and non-interactive ones is provided, with a special focus on modern solutions such as the zk-SNARK and the zk-STARK. The mathematical foundations of ZKPs are described in detail, including discrete logarithm proofs, the use of homomorphic encryption, polynomial commitments, hashing, and elliptic curves. Practical application areas are analyzed, including cryptocurrencies (Zcash, Ethereum), authentication systems, digital identity, and electronic voting. The advantages of using ZKPs are shown, such as enhanced privacy, reduced need for trusted intermediaries, and strengthened security. At the same time, key challenges are outlined, including scalability, implementation complexity, the problem of trusted setup, and potential vulnerability to quantum computing. It is concluded that zero-knowledge proof protocols are a powerful tool for ensuring confidentiality and reliability of digital systems, while further research is aimed at creating more efficient and quantum-resistant solutions.

Sajid Safeer, Pierluigi Gallo, Cataldo Pulvento

Modern agriculture faces critical challenges such as climate change, food security and supply chain inefficiencies, which demand innovative solutions. Traditional farming systems often lack real time monitoring, data security and transparency, leading to wastefulness and quality concerns. To address these, we present a comprehensive precision agriculture framework that integrates Internet of Things (IoT) sensors, Raspberry Pi (R-Pi) edge computing, blockchain based data management and computer vision (CV) assisted statistical modeling. The system collects environmental data via a sensor network, processes it at the edge using R-Pi, and records summarized outputs on a secure Ethereum based blockchain using smart contracts. Simultaneously, CV modules perform real time quality assessment and anomaly detection. A Markov chain based stochastic model is employed to track quality degradation in high value crops. The methodology is validated through a saffron use case, demonstrating effectiveness in monitoring filament degradation and detecting potential fraud. This integration enhances real time decision making, ensures traceability and promotes sustainability in climate smart agriculture.

Imtiaz Sifata

We quantify the economic consequences of Ethereum’s transition from Proof-of-Work to Proof-of-Stake. We document a structural break in inflation dynamics, shifting to an ARIMA(2,1,1) process with deflationary tendencies. The relationship between inflation and staking returns weakens post-Merge, challenging assumptions about incentive structures in Proof-of-Stake systems. Analysis reveals significant changes in market microstructure, including reduced spot trading volume and altered futures market behavior. We identify complex feedback loops between on-chain metrics and market variables, defying traditional equilibrium models. Our results suggest the need for new economic models to understand Proof-of-Stake systems and their market implications.

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.

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.

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.

Yury Yanovich, Sergey Sobolev, Yash Madhwal, Kirill Ziborov · 9 authors

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.