The tamper-proof feature of blockchain makes the correctness of smart contracts crucial before they are run on the chain. Using model checking methods to verify smart contracts helps ensure their correctness. Methods for converting smart contracts into semantically rigorous formal models that can be used in model checking methods still face the challenges of poor usability and difficulty in breaking away from manual modeling. Colored Petri nets, as discrete state-based systems, have similarities to smart contracts and are suitable for analyzing interactions between transactions. The analysis of control flow and data flow interactions, and transactions on the blockchain is missing in the research on converting smart contracts into colored Petri nets. In this paper, we propose an automated modeling method based on colored Petri nets, using template modeling and combination with data flow interactions and transaction modeling. Firstly, the data and control flows are modeled and combined, and secondly, the external user invocation is modeled to be able to correctly portray the on-chain contract invocations. The correctness in the smart contract is then verified based on the colored Petri net. Finally a prototype tool is implemented to demonstrate the usability and correctness of the modeling algorithm.
Blockchain Technology Applications and Security
Economic and Technological Systems Analysis
Advanced Research in Systems and Signal Processing
Integration of Internet of Things (IoT) and blockchain combined with the power of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are transforming the sphere of smart industries, propagating a new era of boosted productivity, information assurance, and data-influenced deliberation. Our research looks into how these cutting-edge technologies flow together to enable smart industry breakthroughs. This offers conductive connectiveness and communication capabilities between devices and can create large pools of data, that are essential for making more informed decisions and finally, operating more sustainably. This data is then scaled and processed by the AI ML and DL algorithm to get the predictive insights; process optimization and to improve on automation. The security and immutability of data are critical in an IoT network, and this is something that blockchain technology excels at and ensures data exchanged within these networks is safe and unalterable. Thanks to recent developments in AI, ML, and DL, they can now better meet the challenges of industrial applications well beyond predictive maintenance and supply chain optimization and extend into real-time monitoring and autonomous operations. The perspective taken in this research is instead one of a practical, real-world implementations, illustrating some of the advantages as well as challenges when integrating these technologies. The results point to the enormous transformative capability of this integration and suggest a level of efficiency, security and innovation not seen before that will redefine intelligent industries today and possibly more importantly tomorrow, in effect defining the fourth industrial revolution and beyond.
This article presents a Blockchain-based solution for the management of multipolicies in insurance companies, introducing a standardized policy model to facilitate streamlined operations and enhance collaboration between entities. The model ensures uniform policy management, providing scalability and flexibility to adapt to new market demands. The solution leverages Merkle trees for secure data management, with each policy represented by an independent Merkle tree, enabling updates and additions without altering existing policies. The architecture, implemented on a private Ethereum network using Hyperledger Besu and Tessera, ensures secure and transparent transactions, robust dispute resolution, and fraud prevention mechanisms. The validation phase demonstrated the modelโs efficiency in reducing data redundancy and ensuring the consistency and integrity of policy information. Additionally, the systemโs technical management has been simplified, operational redundancies have been eliminated, and privacy is enhanced.
George Lฤzฤroiu, Tom Gedeon, Elลผbieta Rogalska, Katarรญna Valรกลกkovรก ยท 17 authors
Research background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data and enterprise asset management in multiphysics simulation environments by industrial big data processing, modeling, and monitoring, enabling business organizational and managerial practices. Machine learning-based decision support and edge generative AI sensing systems can reduce persistent labor shortages and job vacancies and power productivity growth and labor market dynamics, shaping career pathways and facilitating occupational transitions by skill gap identification and labor-intensive manufacturing job automation by path planning and spatial cognition algorithms, furthering theoretical implications for management sciences. Generative AI fintech, machine learning algorithms, and behavioral analytics can assist multi-layered payment and transaction processing screening with regard to authorized push payment, account takeover, and synthetic identity frauds, flagging suspicious activities and combating economic crimes by rigorous verification processes. Purpose of the article: We show that edge device management functionalities of cloud industrial IoT and virtual robotic simulation technologies configure plant production and route planning processes across cyber-physical production and industrial automation systems in multi-cloud immersive 3D environments, leading to tangible business outcomes by reinforcement learning and convolutional neural networks. Labor-augmenting automation and generative AI technologies can impact employment participation, increase wage and wealth inequality, and lead to potential job displacement and massive labor market disruptions. The deep learning capabilities of generative AI fintech in terms of adaptive behavioral analytics and credit scoring mechanisms can enhance financial transaction behaviors and algorithmic trading returns, identify fraudulent payment transactions swiftly, and improve financial forecasts, leading to customized investment recommendations and well-informed financial decisions. Methods: Machine learning-based study selection process and text mining systematic review management software and tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package for R, SluRp, and SWIFT-Active Screener. Such reference management systems are harnessed for methodologically rigorous evidence synthesis, study selection and characteristic extraction, predictive document classification, machine learning-based citation and record screening, bias assessment, article retrieval automation, and document classification and prioritization. Findings & value added: Industrial IoT and 3D augmented reality technologies can create business value by streamlining virtual product and remote asset management across extended reality-based navigation and robotic autonomous systems in smart factory environments by generative AI and machine learning algorithms, articulating business organizational level and theory of management implications. 3D simulation and operational modeling tools can execute and complete complex cognitive task-oriented and knowledge economy jobs, producing first-rate quality outputs swiftly while leading to unemployment spells, labor market disruptions, job displacement losses, and reduced earnings by machine learning clustering and spatial cognition algorithms. Generative AI decentralized finance, interoperable blockchain networks, cash flow management tools, and asset tokenization can mitigate fraud risks, enable digital fund and crypto investing servicing, and automate treasury operations by integrating real-time payment capabilities, routing and configurable workflows, and lending and payment technologies.
The article discusses the use of Ethereum blockchain technology in the Internet of Things (IoT) network for IT diagnostics of patients, which increases data security and user privacy. This integration is proving effective for storing and managing sensitive data of patients with neurological diseases. An integrated system architecture has been developed that combines the IoT network, the IPFS (InterPlanetary File System) file structure with the Ethereum blockchain to create a reliable data storage model. This system ensures efficient, secure and transparent data processing, optimizing the processes of data registration, authorization and verification. Using IPFS for decentralized file storage, along with the Ethereum blockchain to create tamper-proof medical records, provides increased efficiency, scalability and privacy. During the experiments, the process of creating and testing the system was implemented, including setting up the environment, connecting an IPFS node, programming Ethereum smart contracts, sampling voice data and storing their hashes.
The increasing complexity of modern aircraft systems necessitates advanced monitoring solutions to ensure operational safety and efficiency. Traditional aircraft health monitoring systems (AHMS) often rely on reactive maintenance strategies, detecting only visible faults while leaving underlying issues unaddressed. This gap can lead to critical failures and unplanned downtime, resulting in significant operational costs. To address this issue, this paper proposes the integration of artificial intelligence (AI) and blockchain technologies within an enhanced AHMS, utilizing the iceberg model as a conceptual framework to illustrate both visible and hidden defects. The model highlights the importance of detecting and addressing issues at the earliest possible stages, ensuring that hidden defects are identified and mitigated before they evolve into significant failures. The rationale behind this approach lies in the need for a predictive maintenance system capable of identifying and mitigating hidden risks before they escalate. Key tasks completed in this study include: a comparative analysis of the proposed system with existing monitoring solutions, the selection of AI algorithms for fault prediction, and the development of a blockchain-based infrastructure for secure, transparent data sharing. The evolution of AHMS is discussed, emphasizing the shift from traditional monitoring to advanced, predictive, and prescriptive maintenance approaches. This integrated approach demonstrates the potential to significantly improve fault detection, optimize maintenance schedules, and enhance data security across the aviation industry.
The financial markets are undergoing rapid transformations that raise fundamental questions about the effectiveness of traditional investment models and strategies. Nowadays, investment options are incomparably wider than ever before, and one of the areas of this global financial transformation is alternative investments, so the question is what might be the trends of one of these alternative investments, non-fungible tokens (NFT). The object of the study is alternative investments, such as NFTs. The article intends to reveal how NFTs might impact the valuation and trade of digital assets, as well as to identify the key advantages and risks associated with NFTs for investors and creators. The research will carry out cluster analysis of NFTs, which will help to better understand the NFT market, learn about possible prospects and developments, possible advantages and disadvantages, as well as the level of risk.
Purpose This study aims to investigate blockchain technology (BT) and its opportunities and weaknesses in Iran's tax system; it addresses the opportunities and challenges of BT when incorporated into Iran's tax system. Design/methodology/approach The statistical population consists of all the employees and managers working in tax administration, and 674 participants were selected as the sample size via Cochran sampling. The partial least square tests are used to investigate the impact of the independent variable on dependent ones. Findings The results show that BT positively affects three components of tax, including value-added tax, tax on shipping goods and income tax. BTโs advantages and opportunities positively affect these taxation types, while its threats negatively affect the opportunities and challenges in Iranโs tax system; this study provides helpful insights and develops the knowledge. Furthermore, this is among the initiatives addressing BTโs opportunities and challenges in three discriminative taxation sectors, including value-added tax, tax on shipping goods and payroll tax. Originality/value Since no study has addressed BTโs opportunities and weaknesses in Iranโs tax system, it addresses the opportunities and challenges of BT when incorporated into Iranโs tax system.
Non-fungible token (NFT) markets leveraging blockchain technology have surged in popularity, offering advantages like transparent ownership history, fractional ownership, and secure operations. However, high gas fees can make trading on the blockchain expensive. This paper investigates strategies to reduce these costs in NFT marketplace contract implementation and execution. We tested our solutions on the Polygon blockchain, using Solidity for smart contract development and Visual Studio Code for testing. Our approach focuses on creating efficient NFT marketplace contracts for listing properties, executing transactions, and ensuring secure ownership transfer. Our analysis demonstrates an average transaction cost reduction of $\mathbf{3 5. 1 4 \%}$ and a 5.10% decrease in the cost of a single transaction. These optimizations significantly lower gas fees, enhancing the efficiency and viability of NFT markets in the private sector.
Muhammad Asfund Khalid, Muhammad Usman Hassan, Fahim Ullah, Khursheed Ahmed
Purpose The debate around automation through digital technologies has gathered traction in line with the advancement of Industry 4.0. Blockchain-powered construction progress payment has emerged as an area that can benefit from such automation. However, the challenges inherent in real-time construction payment processes cannot be solely mitigated by blockchain. Including building information modeling (BIM)-based schedule information stored in decentralized storage linked with a smart contract (SC) can allow the efficient administration of payments. Accordingly, this study aims to present an integrated BIM-blockchain system (BBS) to administer decentralized progress payments in construction projects. Design/methodology/approach A mixed-method approach is adopted, including an extensive literature review, development of the integrated BBS, and a case study with 13 respondents to test and validate the BBS. This study proposes a BBS that extracts the invoices from BIM and pushes them to the decentralized app (dApp) for digital payment to the contractor through the Ethereum blockchain. The Solc npm package was used to compile the backend SC. Next.js was used to create the front end of the dApp. The Web3 npm package is paramount in developing a dApp. A total of 13 construction professionals working on the case study project were engaged through a questionnaire survey to comment on and validate the proposed BBS. A descriptive analysis was conducted on the case study data to apprehend the responses of expert professionals. Findings The proposed BBS creates an SC, enables sender verification, checks contract complaints, verifies bills, and processes the currency flow based on a coded payment logic. After passing the initial checks, the bill amount is processed and made available for the contractor to claim. Every activity on dApp leaves its trace on the blockchain ledger. A control mechanism for accepting or rejecting the invoice is also incorporated into the system. The case study-based validation confirmed that the proposed BBS could increase payment efficiency (92.3%), tackle financial misconduct (84.6%), ensure transparency and audibility (92.4%), and ensure payment security (61%) in construction projects. A total of 46.2% of respondents were skeptical of the BBS because of its dependency on cryptocurrencies. A further 23.1% of respondents indicated that the price fluctuation of cryptocurrencies is a major barrier to BBS adoption. Others highlighted the absence of legal frameworks for cryptocurrenciesโ usage. Originality/value This study opens the avenue for the application of dApp for autonomous contract management and progress payments, which is flexible with applications across various construction processes. Overall, it is a potential solution to the endemic problem of cash flow that has devastating consequences for all project stakeholders. This is also aligned with the goals of Industry 4.0, where process automation is a key focus. The study provides a practice application for automated progress payments that can be leveraged in construction projects across the globe.
The characteristics of blockchain technology, which is a decentralised database or "distributed ledger," include independence, lack of central authority, and a trustless setting. Because of these characteristics, blockchain technology is well-suited for use in many IoT applications. This article details a real-world use of the Proof of Authority (PoA) Ethereum blockchain on an web of Things (IoT) system. In order to study and highlight potential challenges that might impact the integration of blockchain with IoT, and to set the stage for future research and potential solutions to these concerns, this implementation was carried out in a practical sense.
The aim of this paper is to synthesize and analyze existing evidence on interconnected sensor networks and digital urban governance in data-driven smart sustainable cities. The research topic of this systematic review is whether and to what extent smart city governance can effectively integrate the Internet of Things (IoT), Artificial Intelligence of Things (AIoT), intelligent decision algorithms based on big data technologies, and cloud computing. This is relevant since smart cities place special emphasis on the involvement of citizens in decision-making processes and sustainable urban development. To investigate the work to date, search outcome management and systematic review screening procedures were handled by PRISMA and Shiny app flow design. A quantitative literature review was carried out in June 2024 for published original and review research between 2018 and 2024. For qualitative and quantitative data management and analysis in the research review process, data extraction tools, study screening, reference management software, evidence map visualization, machine learning classifiers, and reference management software were harnessed. Dimensions and VOSviewer were deployed to explore and visualize the bibliometric data.
As blockchain technology and smart contracts develop, computer technology is constantly integrating with smart chemical plants. Due to the continuous development of intelligent chemical plants, their systems have gradually become large and dispersed, posing a threat to safety management. In order to improve the performance of intelligent security management systems, the study first explores the principles of blockchain and smart contract technology, and then combined with the requirements of intelligent chemical plant security management systems, designs an intelligent security management system based on blockchain and smart contract technology. The experimental results showed that compared to systems without smart contract support, the communication success rate between nodes was lower. The error rates of blockchain-based encryption systems, deep learning-based encryption systems and improved data encryption systems proposed in the study were 0.22, 0.07 and 0.09, respectively. The packet loss rates were 0.13, 0.04 and 0.05, respectively. The lower the bit error rate and packet loss rate of the encryption system, the clearer the illegal eavesdropping information. The experimental results indicate that the intelligent security management system designed in this study has good encryption performance and a higher communication success rate. The results have certain reference value in security management application in intelligent chemical plants.
Chibuikem Michael Adilieme, Rotimi Boluwatife Abidoye, Chyi Lin Lee
Purpose Blockchain is an emerging digital technology proposed and trialled among different built environment professions. The technology has been proposed to introduce transparency, security and trust in property transactions. Despite this proposition, few studies have analysed the barriers and prospects in property valuation, especially in markets plagued by low transparency and a lack of stakeholder trust. Using Nigeria as a case study, this study assesses the barriers and prospects for adopting blockchain technology in property valuation. Design/methodology/approach Data was collected from 180 valuers practising in Nigeria through an online survey, and the data was analysed using mean score ranking and the chi-square (ฯ2) test of independence. Findings Firstly, there was a low awareness of the application of blockchain technology and an association between the number of valuation jobs executed annually and awareness of the application of blockchain technology. The most important barriers revolved around the knowledge, technical know-how of blockchain and the cost of implementing such technology. The prospects for blockchain are very high as all identified prospects were considered important, with transparency being the most crucial factor for its adoption, followed by the monitoring activities in real time and the permanence in storing records. Research limitations/implications This study's implications lie in the potential benefit of transparency identified for blockchain, which could act as a tool to introduce transparency into valuation industries that battle key issues surrounding transparency and trust. Furthermore, this study can be utilised by policymakers and property industry players in mapping strategies to adopt the beneficial use of blockchain as one among the suite of proptech tools disrupting the property valuation scene, in their practice. This also presents an opportunity to draw upon insights from this study to better prepare for using blockchain in property valuation. Originality/value This study appears to be the first to empirically assess barriers and prospects for blockchain in property valuation practice. It contributes to the literature by identifying key factors that will deter and/or promote the application of blockchain, an emerging and disruptive digital technology.
This study aims to assess the extent to which blockchain technology (BCT) may constitute an alternative to the conventional stock trading system and emphasize the changing roles of the key parties. It is expected that BCT would enhance the performance of the process across the three stages (i.e. trading, clearing and settlement within the stock exchange environment). A thorough literature review is conducted to understand the BCT performance modeling techniques and approaches (empirical and analytical) and to examine the theoretical potentials and capabilities of BCT in the financial markets. The case study and simulation methods are used to evaluate the impact of BCT implementation in optimizing the process of trading, clearing and settlement in Abu Dhabi Securities Exchange (ADX) stock-trading activities. This paper presents a simulation analysis comparing a blockchain system with a traditional trading system in the context of stock market. The simulation procedures involve modeling processes over different durations and transaction volumes, using metrics such as process time and cycle time to evaluate performance. The performance index combines these metrics with weights to ensure accurate and consistent measurements. Simulation results reveal that the blockchain system significantly outperforms the current trading system, especially at higher transaction volumes, highlighting its scalability and efficiency. A threshold of 30,000 transactions is identified as the point where blockchainโs benefits become apparent. The analysis shows that blockchain significantly elevate the process efficiency. It reduces both cycle time and process time across varying transaction volumes, maintaining consistency and reliability. Additionally, a simple simulation using the Hyperledger Fabric platform demonstrates the practical implementation of a permissioned blockchain for clearing transactions, emphasizing the system's capability to manage high transaction volumes efficiently and securely. The use of blockchain network for handling seamless transactions using pre-defined smart contracts significantly improves the performance of the stock trading processes, specifically in the clearing phase. Interestingly, the BCT system drops the need for a โthird partyโ (i.e. stock custodian) across the three stages. At the end of the paper, we propose a thereat mitigating model for stock trading with a new blockchain system.
The article examines a new object of forensic economic examination โ cryptocurrency. The author provides classic definitions of an object of forensic examination and establish the differences of cryptocurrency from the traditionally understood objects of both forensic examination in general and economic examination in particular. The main difference of cryptocurrency from other currencies and objects of investigation is its virtual nature, lack of affiliation with the material world. Two main points of view on the essence of cryptocurrency are analyzed: as a basis and tool for the development of new effective forms of payments, exchange of goods, and as an object and instrument of criminal activity. A definition of cryptocurrency is given as interpreted by the FATF โ Financial Action Task Force. It is identified which issues related to the circulation of cryptocurrency can be attributed to the competence of a forensic expert-economist, and which โ to the field of computer forensics. The author also describes the features of cryptocurrency that must be taken into account when considering it as an object of forensic economic examination.
Blockchain provides a decentralised, tamper-proof and trustworthy distributed database technology that is widely used in finance and economics, IoT and big data. Artificial intelligence (AI) provides a technology that can mimic human intelligence, learn autonomously and automate decision-making, which plays a major role in enhancing productivity, solving complex problems and improving decision-making. The two represent two of the major driving forces in technology today, and their integration is redefining our digital world. The aim of this paper is to explore the integration of these two technologies and the innovations, challenges, and future prospects they bring. First, we trace their history and evolution, introduce the basic characteristics of blockchain and AI, and explain in detail how they work. We then delve into the integration of blockchain and AI, highlighting their importance and significance in areas such as finance, supply chain and healthcare. We analyse the applications and implications of this integration for these areas, as well as the challenges and dilemmas faced, including issues of security, privacy, data leakage, and technical feasibility. Finally, we explore future trends and related work, highlighting the importance of global community collaboration and innovation to realize the potential of blockchain and AI.