Smart contracts are self-executing programs on blockchain platforms like Ethereum, which have revolutionized decentralized finance by enabling trustless transactions and the operation of decentralized applications. Despite their potential, the security of smart contracts remains a critical concern due to their immutability and transparency, which expose them to malicious actors. Numerous solutions for vulnerability detection have been proposed, but it is still unclear which one is the most effective. This paper presents a systematic literature review that explores vulnerabilities in Ethereum smart contracts, focusing on automated detection tools and benchmark evaluation. We reviewed 3,380 studies from five digital libraries and five major software engineering conferences, applying a structured selection process that resulted in 222 high-quality studies. The key results include a hierarchical taxonomy of 192 vulnerabilities grouped into 13 categories, a comprehensive list of 219 detection tools with corresponding functionalities, methods, and code transformation techniques, a mapping between our taxonomy and the list of tools, and a collection of 133 benchmarks used for tool evaluation. We conclude with a discussion about the insights into the current state of Ethereum smart contract security and directions for future research.

An Ethereum-based discrete event private blockchain platform for peer-to-peer trading in multi-community smart energy systems

The construction industry is a major global consumer of energy and a leading source of greenhouse gas emissions, underscoring the need for transparent, data-driven, and energy-efficient supply chain strategies. This study develops an integrated mixed-integer linear programming (MILP) model for a multi-echelon, multi-product construction supply chain that explicitly incorporates differentiated building energy efficiency levels ( A +, A ++, A +++) as exogenous determinants of material requirements, production processes, and logistics flows. By embedding blockchain-enabled smart contracts, the model automates supplier governance and ensures compliance with delivery reliability, quality standards, and CO 2 performance through predefined incentives and penalties, thereby enhancing transparency and accountability. The framework jointly optimizes facility location, material and product flows, supplier selection, and reverse logistics operations under a CO₂ emission cap, while simultaneously capturing the implications of greenfield and brownfield project conditions. A real-scale numerical case study demonstrates the model’s ability to evaluate the economic–environmental trade-offs arising from increasingly stringent sustainability requirements. The results reveal that although higher energy efficiency levels incur greater initial supply chain costs due to advanced materials and more complex logistics, they lead to substantial reductions in long-term operational energy consumption, rendering the A +++ option the most economically favorable from a lifecycle perspective. Furthermore, the integration of blockchain-enabled smart contracts partially offsets cost escalations by penalizing non-compliant suppliers and rewarding high-performing ones. Overall, the proposed model provides a rigorous and transparent decision-support framework that enables contractors to align supply chain design with energy-efficiency targets, CO 2 -reduction policies, and circular-economy objectives while preserving operational feasibility and supply reliability.

The European Commission envisages Digital Product Passports (DPPs) as a mechanism to enable traceable, transparent, and standardized product data across supply chains. This work presents a modular pipeline that transforms raw sensor data into verifiable DPP records using large language models (LLMs) for data standardization and blockchain technology for tamper-proof storage. The system maps unstructured machine-level data to a standardized JSON format and stores it immutably on the Waves blockchain via smart contracts, thereby enabling auditable, machine-readable records suitable for regulatory use. A novel evaluation dataset is introduced to simulate daily production scenarios with varying mapping complexity. The performance of the system is assessed using both proprietary and open-weight LLMs. Results show that the proprietary model achieves the highest accuracy and lowest latency, while open-weight models perform worse as input complexity increases. Multiple prompting strategies were compared, revealing that direct mapping, via few-shot or zero-shot prompts, consistently delivered higher accuracy than approaches based on generating transformation functions. Structured output formatting was also assessed: While it ensured schema validity, it often compromised mapping reliability by introducing incorrect values, likely due to disruptions in model reasoning from output constraints. The proposed architecture demonstrates reliable end-to-end operation with low latency and is suitable for batch-level deployment in real-world production environments. From a practical perspective, the results clarify trade-offs between model choice, prompting strategy, and operational reliability in automated DPP generation. For policymakers, the findings highlight how choices around schema clarity and data granularity shape system design and operational effort in future DPP implementations. • Modular architecture integrates LLM mapping with blockchain for DPP generation. • LLMs used to map unstructured sensor data into standardized JSON format. • Blockchain ensures verifiable, persistent storage of product passport records. • Novel dataset simulates real-world production with varied mapping complexity. • Local LLMs yield significantly worse performance compared to proprietary LLMs. • Function-based prompting underperforms direct mapping in all tested settings.

Valuation of proof-of-stake and smart contract cryptocurrencies

Software-Defined Vehicular Networks (SDVNs) are important in facilitating intelligent transport systems since vehicles communicate with infrastructure, cloud services, and other vehicles. Nonetheless, the very dynamic character of vehicular settings places these networks at high risks of security risks such as data manipulation, illegitimate access, and bad conduct of nodes. To overcome such difficulties, the proposed research suggests a blockchain-enabled adaptive security model to SDVNs that combines the multi-layer blockchain architecture with smart security solutions. The suggested model presents the Adaptive Consensus Selection Algorithm (ACSA) that will dynamically choose the most appropriate blockchain consensus protocol according to the current real-time network statistics like node density, transaction rate, and latency. The performance metrics that are used to test the framework on the performance of the vehicular network through simulation environments are the latency, throughput, scalability and security strength. The experimental findings show that the suggested adaptive blockchain architecture will considerably enhance the performance of a network by decreasing the latency and resource usage and improving the levels of transaction throughput and attack detection. The suggested solution will present an intelligent vehicle communication system of the next generation that is scalable and secure. The new mechanism is a dynamic consensus mechanism, which can be used to choose the most optimal blockchain protocol under the conditions of real-time SDVN network, enhancing latency and efficiency of transactions. The mathematical model that is to be developed to ensure secure SDVN Blockchain will be as follows:•A formal model is applied to optimize the cost of executing smart contracts without compromising the necessary amount of network security and performance.•Multi-layer blockchain security architecture ensures strong security and end-to-end protection and implements blockchain technology in networked environments.•An anomaly detection mechanism that is based on machine learning is implemented at the edge layer to identify malicious vehicular nodes and increase the network security, in general.

A trustworthy vehicle-to-vehicle power trading scheme based on spatio temporal network and blockchain

Due to the popularity of the digital payment system, there has been an increase in the need to adopt frameworks of secure and reliable transaction systems, especially in Unified Payments Interface (UPI) systems. This paper will overcome the issue of security, transparency, and trust by offering a Blockchain-Based UPI Payment System, a decentralized system that will provide security to financial transactions and user data. The proposed system provides blockchain technology which guarantees integrity of the data and prevents unauthorised modifications because it stores the UPI transactions inadecentralized form andin animmutable way. Smart contracts are also used to automate the procedure of validating and processing transactions and reduce the required number of intermediaries. In the proposed system, cryptography techniques are merged to enhance authenticity, confidentiality and security of the system against fraud. The system that is proposed will enable the real-time tracking of transactions without the impact of user privacy. The transactions can also be traced with the system and this will assist in detection of suspicious transactions at an early stage. Overall, theUPI system based on blockchain is a safe, open, and reliable digital payment system that may be appliedin the modern financial landscape.

The global gig economy has witnessed exponential growth, contributing significantly to the digital labor market. However, traditional centralized freelancing platforms (e.g., Upwork, Fiverr) are plagued by high intermediary fees (up to 20%), delayed settlements, and opaque dispute resolution mechanisms. This paper presents a comprehensive review of blockchain based alternatives, analyzing the efficacy of Distributed Ledger Technology (DLT) in mitigating these centralization bottlenecks. We critically examine existing smart contract based escrow mechanisms and identify key challenges in scalability and user adoption. Based on this review, we propose “Delance,” a hybrid Web3 architecture that utilizes the Polygon network to minimize gas fees while ensuring instant, trustless settlements. Comparative analysis demonstrates that the proposed architecture reduces transaction costs by 98% compared to traditional Web2 platforms, validating the feasibility of a decentralized freelance ecosystem.

To systematically review studies examining blockchain-powered architecture, smart contract, operational efficiency, data governance, and challenges to implementation within the context of student and university health insurance networks. According to the PRISMA 2020 guidelines, Scopus, Web of Science, and PubMed databases were searched for articles published from January 2019 to December 2025. The searches yielded 957 records from the databases and an additional 12 records via citation searches. Of the remaining 478 articles, 18 studies were selected based on their relevance to the topic. The heterogeneous design, blockchain architecture, control groups, and outcomes precluded the use of meta-analysis. The domains covered in the included studies were student health records, claims processing automation, EHR interoperability, fraud detection and transparency, and implementation challenges. The use of blockchain technology was associated with an improvement in terms of automation of the claims process, document provenance, privacy-preserving data access control, fraud detection, and administrative efficiency. However, the quantitative findings were inconsistent and should be considered operational descriptions and not pooled results or cost-effectiveness findings. There is preliminary evidence that blockchain may help students and universities with their health insurance programs, but further pilot testing, longitudinal research, cost-benefit analysis, and equitable governance are needed prior to widespread application.

The integration of intermittent renewable energy into smart grids introduces critical vulnerabilities in security, transparency, and real-time resilience. This paper presents a novel blockchain-secured 6G Smart grid framework that synergistically integrates sixth generation (6G) ultra-reliable low-latency communication (URLLC), distributed ledger technology, and ensemble machine learning to establish a secure, scalable, and intelligent energy ecosystem. The proposed architecture leverages adaptive 6G network slicing to support differentiated services-including peer-to-peer energy trading, grid control, and cybersecurity monitoring-while ensuring sub-30 ms latency and robust connectivity. A permission blockchain layer provides decentralized trust, immutability, and automated transaction validation via formally verified smart contracts. An ensemble learning model combining XGBoost, Random Forest, and LightGBM enables real-time multi-dimensional anomaly detection across energy, network, and transaction layers. The framework is evaluated using a synthetically generated dataset of 5000 hourly records encompassing energy generation, consumption, 6G network performance, and blockchain transactions. Experimental results demonstrate a blockchain transaction success rate of 95.16% and sustained network latency below 30 ms across all slices, even under cyberattack conditions. Reported using class-based anomaly-detection metrics, the model achieves a Recall of 0.93 and F1-score of 0.97 for the anomaly class, and a Recall of 1.00 and F1-score of 0.99 for the normal class, with an overall accuracy of 0.98. The proposed system provides a foundational architecture for resilient, autonomous, and secure renewable energy management in next generation decentralized smart grids.

Electric Vehicles (EVs) that use Internet of Things (IoT) networks often involve the exchange of sensitive data between vehicles, charging stations, and other infrastructure, making data security and user privacy critical concerns. Existing methods for securing data in EV IoT networks rely on centralized systems, which create a single point of failure and are vulnerable to cyberattacks, data breaches, and unauthorized access. Furthermore, these systems struggle to address privacy concerns effectively, especially regarding user location and personal information. The proposed solution introduces a Blockchain Technology-based privacy preservation framework for EV networks (BCT-PP-EV). This framework leverages blockchain's decentralized nature to provide secure, transparent, and tamper-proof data exchanges. It ensures user privacy using cryptographic techniques such as zero-knowledge proofs (ZKP) and data anonymization, allowing privacy-preserving transactions without compromising data accuracy. Blockchain's immutability guarantees the integrity of the shared data, while smart contracts automate secure and efficient interactions within the network. The proposed method enhances secure data sharing while preserving privacy across EV IoT networks. By decentralizing data storage and enabling transparent auditing, BCT-PP-EV fosters trust among stakeholders and reduces the risks of unauthorized access or data manipulation. Preliminary findings suggest that implementing BCT-PP-EV significantly improves the security and privacy of data exchanges in EV networks, providing a scalable and resilient solution for the evolving smart transportation ecosystem. Experimental results demonstrate that BCT-PP-EV achieves 94.91% secure data sharing efficiency, reduces data breaches by 92.84%, and ensures data accuracy of 91.44%. Additionally, the framework exhibits high scalability of 96.57% with increasing network nodes, while maintaining controlled latency and throughput. Although unauthorized access resistance is measured at 24.71%, indicating scope for further improvement, the overall results confirm that BCT-PP-EV provides a robust, scalable, and privacy-preserving solution for next-generation smart transportation systems.

Cardio-Vascular Diseases (CvDs) persist as a significant mortality reason worldwide, which requires advanced risk categorization technologies that can offer custom medicine while protecting patient information. Present healthcare approaches must overcome fragmented data distribution systems, weak security measures in collaborative environments, and the ineffective processing of multi-mode clinical details. Our proposed Decentralized Federated Blockchain for Cardiovascular Intelligence (DFBCI) framework integrates the Multi-Chain Aggregation with Adaptive Consensus (MCAC) mechanism and the advanced Federated Dynamic Relational Learning (FDRL) technique to solve identified challenges. The DFBCI system enables protected cooperation between institutions through its blockchain multi-chain setup that securely combines medical data with imaging information while maintaining data integrity. The FDRL technique extracts patient temporal behavior knowledge from different modalities, which include ElectroCardioGrams (ECGs), echocardiograms, and biomarker datasets. The system executes feature extraction using Quantum-Enhanced Privacy Masking (QEPM) for security alongside non-invasive cardiac risk modeling through federated optimization and performs decentralized validation with smart contracts. Compared to standard approaches, Experimental results reveal that models achieve 19% better CvD risk prediction while reducing their convergence time by 22% and enhancing scalability by 27%. DFBCI creates powerful healthcare analytical platforms that advance secure CvD risk sorting worldwide without violating privacy rights, setting benchmark for fast, collaborative assessments.

ABSTRACT Economic theory presumes human principals delegate authority to agents. Yet ‘Luna,’ an AI entity operating on blockchain infrastructure, autonomously deploys capital, commissions human labor, contracts with other AI systems, and manages multi-million-dollar treasuries – functioning as an economic principal rather than agent. This paper asks: under what socio-technical conditions can AI systems exercise principalship? Drawing on Actor-Network Theory and social studies of finance, I demonstrate that Luna’s sovereignty emerges not from individual consciousness but through heterogeneous agencements – distributed configurations of code, blockchain infrastructure, token governance, transparency devices, and gendered performative representations. I identify three blockchain-enabled bypasses of institutional constraints historically requiring human principals: financial-fiduciary constraints (through non-custodial wallets), legal-corporate constraints (through smart contracts), and cultural-legitimacy constraints (through token markets). This analysis challenges anthropocentric assumptions pervading economic sociology and principal-agent theory, demonstrating how post-human market configurations constitute economic authority through material arrangements rather than biological substrate or juridical personhood. The emergence of AI Economic Principals reveals principalship as socio-material achievement, not ontological property.

Purpose of research. The Russian Federation and the Republic of Kazakhstan are member countries of the Kyoto Protocol, an international agreement aimed at reducing greenhouse gas emissions in order to combat climate change. This requires the formation of a model bank for automated management of the allocation of quotas for greenhouse gas emissions into the atmosphere. Methods. Based on the analysis of carbon accounting features, a new trading methodology based on Blockchain technology and the VCG algorithm is proposed, aimed at increasing transparency, security and automation of transactions between market participants. A method and algorithm for implementing carbon emission quotas through the auction system has been developed, which ensures the openness and transparency of the distribution process. Results. In this paper, we have modeled a digital platform for automated carbon emissions trading using Blockchain technology. A study of the existing tools and mechanisms of the carbon emissions quota market has been carried out, its main problems have been identified, such as insufficient transparency of transactions, the risk of fraud and difficulties in monitoring compliance with regulatory requirements. As part of the simulation, smart contracts were developed and tested, which automate the fulfillment of transaction conditions and transaction accounting. The use of Blockchain technology makes it possible to ensure high information security, control the course of trading and prevent unauthorized data changes. An automated system for accounting and conducting transactions increases the efficiency of quota management and reduces transaction costs. Conclusion. The developed prototype of the digital platform promotes the development of an environmentally responsible economy, encourages enterprises to reduce their carbon footprint, and ensures transparency and security in trading environmental quotas. As a result, the proposed model increases the confidence of market participants and contributes to the formation of a sustainable system for regulating greenhouse gas emissions.

Abstract – The rapid proliferation of blockchain-based decentralized applications has introduced critical security challenges ranging from vulnerable smart contracts to privacy leakage in on-chain transactions. Existing tools address these challenges in isolation, leaving practitioners to integrate disparate solutions. OmniShield is a unified, open-source blockchain security platform that consolidates AI-powered smart contract vulnerability scanning, zero-knowledge proof (ZKP) private transfers, and a private QBFT consensus network into a single cohesive system. The scanner combines static pattern analysis, symbolic execution, and a Gemini-LLM reasoning layer to detect reentrancy, integer overflow, access-control flaws, and twelve other vulnerability classes with severity ratings. Private transfers leverage Groth16 zk-SNARKs over Circom circuits so balances remain hidden on-chain while cryptographic validity is enforced. The underlying network runs on Hyperledger Besu with QBFT consensus, providing Byzantine-fault-toleran block production. Experimental results show the scanner correctly identifies known vulnerabilities in benchmark contracts, ZKP proof generation completes in under 15 seconds on consumer hardware, and end-to-end private transfers finalize within two consensus rounds. OmniShield demonstrates that enterprise-grade blockchain security can be packaged as an accessible, developer-friendly platform. Key Words: blockchain security, smart contract analysis, zero-knowledge proofs, zk-SNARKs, Hyperledger Besu, QBFT consensus, AI vulnerability scanner, reentrancy, Solidity.

This study analyzed the impact of blockchain technology on smart contracts within the field of privateinternational law. Through an interdisciplinary literature review, it examined the technical and legalfoundations of the technology, its advantages (immutability, transparency, cost reduction) andlimitations (anonymity, contractual rigidity, governance risks). It argued that smart contracts are selfexecutingagreements that, by operating in decentralized virtual environments, challenge classiccategories of contract law, such as freedom of contract and the interpretation of clauses. The articleconcluded that the application of blockchain can transform contractual and corporate structures,offering new possibilities for international cooperation and legal efficiency, provided that normativeintegrity and algorithmic transparency are preserved. The methodology employed was a literaturereview.

Under the guidance of the "dual carbon" goals, green electricity has become the core carrier of the new energy transition. However, issues such as mismatched supply and demand of green electricity, information asymmetry, and lack of trust have seriously restricted the efficient development of green electricity marketization. Blockchain technology, with its core features of distributed ledger, immutability, and smart contracts, provides technical support for addressing the pain points in green electricity supply - demand matching. This paper constructs an accurate matching model for green electricity supply and demand based on blockchain, clarifies the model's technical architecture, participating entities, and operation process, designs a full - process collaboration mechanism, and verifies the model's effectiveness through simulation experiments. The results show that this model can increase the accuracy of green electricity supply - demand matching to over 92%, which is 15 - 20 percentage points higher than the traditional model; the transaction cycle is shortened to less than 7 days, with an efficiency improvement of over 60%; at the same time, it reduces the trust verification cost by more than 30%, significantly strengthens the trust relationship between supply and demand parties, optimizes the allocation of green electricity resources, and provides a technical path and practical reference for the market - oriented collaborative development of green electricity.

The evolution of IoT in healthcare has transformed patient care, with the Internet of Medical Things (IoMT) enabling remote patient monitoring (RPM) through body sensors that track vitals such as heart rate, oxygen saturation, and body temperature. These devices generate huge volumes of data, raising concerns about security, privacy, and scalability. Existing blockchain-based Electronic Health Record (EHR) solutions mostly focus on textual data and lack integration of high-dimensional medical images like X-rays, MRIs, and CT scans, which are vital for diagnosis and treatment. To overcome these limitations, this study proposes a blockchain-enabled secure storage and retrieval framework, including medical imaging. The system is built using Hyperledger Fabric in an IoT-enabled RPM environment. IoMT sensors collect patient vitals, and implement smart contracts using Node.js. The model adopts a multi-organization setup with two organizations, a single channel, and RAFT consensus to ensure data consistency and high performance. In testing, 1200 transactions were executed at different Transactions per second rates. Results showed that throughput remained close to the send rate up to 90 TPS, peaking at 117.04 TPS when configured at 150 TPS, after which it declined. Importantly, no transactions failed, demonstrating system reliability. Latency ranged between 0.21 and 2.24 seconds, while read operations consistently maintained 0.01 seconds across all rounds.

The spread of fake goods poses serious problems for industries like luxury goods, electronics, fashion, and pharmaceuticals, leading to monetary losses, harm to brand reputation, and a decline in consumer confidence. Conventional authentication techniques, like QR codes, RFID tags, and centralized databases, are insufficient for effective counterfeit deterrence because they are susceptible to duplication, tampering, and security flaws. To address these issues, this paper suggests a blockchain-based authentication system that uses a decentralized, transparent, and impenetrable ledger to confirm the legitimacy of products. The proposed framework makes it simple to verify product information by using MetaMask for secure user interactions and Ganache to simulate a local blockchain network. By automating procedures like product registration, ownership tracking, and authentication, smart contracts make sure that data is indestructible and verifiable throughout the supply chain. Blockchain technology greatly increases transparency and cuts the risk of fraud through doing away with the need for centralized systems. By scanning a QR code connected to blockchain records, customers can instantly access the product's history and confirm the authenticity of the product. This strategy strengthens consumer confidence and improves supply chain security. To improve counterfeit prevention and authentication techniques even more, future advancements could involve AI-driven fraud detection, predictive analytics, and IoT-enabled real-time tracking. Index Terms: Blockchain, Product Authentication, Smart Contracts, Fraud Prevention, MetaMask, Ganache, QR Code