Abstract—The integrity and transparency of voting systems are fundamental to the democratic process; however, traditional voting mechanisms often encounter issues such as fraud, manipulation, limited transparency, and centralized control. To address these challenges, this research proposes a decentralized voting system utilizing blockchain technology. The system leverages the Ethereum blockchain, smart contracts developed in Solidity, and a React.js-based frontend integrated with Web3.js and MetaMask to ensure secure voter authentication, transparent vote casting, and immutable vote recording. Voter and candidate registrations are managed through decentralized smart contracts, and all transactions are permanently stored on the blockchain, providing public verifiability while preserving voter anonymity. Development and testing were conducted in a simulated environment using Ganache and the Truffle Suite, allowing for extensive validation of system functionalities. Experimental results demonstrate enhanced security, real-time result computation, prevention of double voting, and elimination of any single point of failure. This decentralized architecture significantly improves trust, transparency, and security in electoral processes, offering a scalable and reliable model for the future of electronic voting systems. Index Terms— Blockchain, Decentralized Voting, Ethereum, Smart Contracts, Solidity, Web3.js, MetaMask, Ganache, Truffle Framework, E-voting Systems.
The integration of Distributed Ledger Technologies (DLT), such as blockchain, in power system management is rapidly gaining attention for its potential to enhance grid transparency, security, and operational efficiency. This paper explores the role of DLT in power systems, focusing on decentralized energy trading, grid data integrity, and smart contract-based automation for demand response. It highlights the benefits of DLT in managing distributed energy resources (DERs), securing transactions, and improving trust among stakeholders. Challenges related to scalability, energy consumption, and regulatory compliance are also discussed. Case studies from pilot projects demonstrate promising results, underscoring DLT’s transformative potential in modernizing power grids.
Blockchain technology has set off a wave of decentralization in the world since its birth. The trust system constructed by blockchain technology based on cryptography algorithm and computing power provides a practical and powerful solution to solve the trust problem in human society. In order to make more convenient use of the characteristics of blockchain and build applications on it, smart contracts appear. By defining some trigger automatic execution contracts, the application space of blockchain is expanded and the foundation for the rapid development of blockchain is laid. This is blockchain 2.0. However, the programmability of smart contracts also introduces vulnerabilities. In order to cope with the insufficient security guarantee of high-value application networks running on blockchain 2.0 and smart contracts, this article will be represented by Ethereum to introduce the technical details of understanding blockchain 2.0 and the operation principle of contract virtual machines, and explain how cryptocurrencies based on blockchain 2.0 are constructed and operated. The common security problems and solutions are also discussed. Based on relevant research and on-chain practice, this paper provides a complete and comprehensive perspective to understanding cryptocurrency technology based on blockchain 2.0 and provides a reference for building more secure cryptocurrency contracts.
Harsha Sammangi, Aditya Jagatha, Giridhar Reddy Bojja, Jun Liu
AI Innovations in the IoT for Real-Time Patient Monitoring On one hand, the current traditional centralized healthcare architecture poses numerous issues, including data privacy, delay, and security. Here, we present an AI-enabled decentralized IoT architecture that can address such challenges during a pandemic and critical care settings. This work presents our architecture to enhance the effectiveness of the current available federated learning, blockchain, and edge computing approach, maximizing data privacy, minimizing latency, and improving other general system metrics. Experimental results demonstrate transaction latency, energy consumption, and data throughput orders of magnitude lower than competitive cloud solutions.
The convergence of blockchain and artificial intelligence (AI) has led to the emergence of AI-based tokens, which are cryptographic assets designed to power decentralized AI platforms and services. This paper provides a comprehensive review of leading AI-token projects, examining their technical architectures, token utilities, consensus mechanisms, and underlying business models. We explore how these tokens operate across various blockchain ecosystems and assess the extent to which they offer value beyond traditional centralized AI services. Based on this assessment, our analysis identifies several core limitations. From a technical perspective, many platforms depend extensively on off-chain computation, exhibit limited capabilities for on-chain intelligence, and encounter significant scalability challenges. From a business perspective, many models appear to replicate centralized AI service structures, simply adding token-based payment and governance layers without delivering truly novel value. In light of these challenges, we also examine emerging developments that may shape the next phase of decentralized AI systems. These include approaches for on-chain verification of AI outputs, blockchain-enabled federated learning, and more robust incentive frameworks. Collectively, while emerging innovations offer pathways to strengthen decentralized AI ecosystems, significant gaps remain between the promises and the realities of current AI-token implementations. Our findings contribute to a growing body of research at the intersection of AI and blockchain, highlighting the need for critical evaluation and more grounded approaches as the field continues to evolve.
This study introduces a cutting-edge architecture developed for the NewbornTime project, which uses advanced AI to analyze video data at birth and during newborn resuscitation, with the aim of improving newborn care. The proposed architecture addresses the crucial issues of patient consent, data security, and investing trust in healthcare by integrating Ethereum blockchain with cloud computing. Our blockchain-based consent application simplifies patient consent's secure and transparent management. We explain the smart contract mechanisms and privacy measures employed, ensuring data protection while permitting controlled data sharing among authorized parties. This work demonstrates the potential of combining blockchain and cloud technologies in healthcare, emphasizing their role in maintaining data integrity, with implications for computer science and healthcare innovation.
Xiaohan Hu, Jyoti Sahni, Colin R. Simpson, Normalia Samian · 5 authors
Flexible sharing of electronic medical records (EMRs) is an urgent need in healthcare, as fragmented storage creates EMR management complexity for both practitioners and patients. Blockchain has emerged as a promising solution to address the limitations of centralized EMR systems regarding interoperability, data ownership, and trust concerns. Whilst its healthcare implementation continues to face scalability challenges, particularly in uploading lag time as EMR volumes increase. In this paper, we describe the design of a novel blockchain-based data structure, MedBlockTree, which aims to solve the scalability issue in blockchain-based EMR systems, particularly low block throughput and patient awareness. MedBlockTree leverages a chameleon hash function to generate collision blocks for existing patients and expand a single chain into a growing block tree with $n$ branches that are capable of processing $n$ new blocks in a single consensus round. We also introduce the EnhancedPro consensus algorithm to manage multiple branches and maintain network consistency. Our comprehensive simulation evaluates performance across four dimensions: branch number, worker number, collision rate, and network latency. Comparative analysis against a traditional blockchain-based EMR system demonstrates outstanding throughput improvements across all dimensions, achieving processing speeds $ν\cdot n$ times faster than conventional approaches.
Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network (PCN), as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channel, restricting the ability of PCNs to transact through a path and subsequently reducing the transaction success rate. To address this issue, the technology of rebalancing has been proposed. However, existing rebalancing strategies in PCNs are limited in their capacity and efficiency. Cycle-based approaches only address rebalancing within groups of nodes that form a cycle network, while non-cycle-based approaches face high complexity of on-chain operations and limitations on rebalancing capacity. In this study, we propose Starfish, a rebalancing approach that captures the star-shaped network structure to provide high rebalancing efficiency and large channel capacity. Starfish requires only $N$-time on-chain operations to connect independent channels and aggregate the total budget of all channels. To demonstrate the correctness and advantages of our method, we provide a formal security proof of the Starfish protocol and conduct comparative experiments with existing rebalancing techniques.
We show that assuming that the returns are independent when conditioned on the value of their variance (volatility), which itself varies in time randomly, then the distribution of returns is well described by the statistics of the sum of conditionally independent random variables. In particular, we show that the distribution of returns can be cast in a simple scaling form, and that its functional form is directly related to the distribution of the volatilities. This approach explains the presence of power-law tails in the returns as a direct consequence of the presence of a power law tail in the distribution of volatilities. It also provides the form of the distribution of Bitcoin returns, which behaves as a stretched exponential, as a consequence of the fact that the Bitcoin volatilities distribution is also closely described by a stretched exponential. We test our predictions with data from the S\&P 500 index, Apple and Paramount stocks; and Bitcoin.
Mingyue Xie, Jun Liu, Shuyu Chen, Ming‐Wei Lin · 7 authors
Blockchain-based IoT applications in agriculture have drawn extensive attention in recent years, allowing the implementation of smart agriculture solutions. By transmitting collected relevant data to a control center through the blockchain, corresponding regulation can be realized in agricultural production management systems. However, existing efforts for directly adopting the technique to data transmission are obstructed by several issues. The traceability of agricultural data stored in the blockchain leads to the exposure of the identity of the data collecting devices. And the tracing difficulty of completely invisible data for identity protection also exists in the smart agricultural system. To tackle these limitations, we propose a novel blockchain-based smart agricultural IoT system framework for regulating the agricultural production environment through trusted data. First, the elliptic curve integrated encryption scheme (ECIES) and the group signature scheme are integrated to guarantee the traceability and identity protection of the data and equipment, respectively. Then, to enhance the security of session key transmission in the ECIES scheme, we further design an on-chain-off-chain key agreement protocol (ECIES-OOKA). In addition, we propose a novel group manager selection method based on probabilistic linguistic term sets (PLTSs) for the group signature implementation. Finally, a practical example is provided to demonstrate the group manager selection process and verify the feasibility of the proposed method. The security and performance analysis for the system framework are also presented.
Purpose The integration of blockchain technology and artificial intelligence (AI) is reshaping the financial services industry, offering transformative solutions in areas such as risk management, fraud detection, regulatory compliance and operational efficiency. Design/methodology/approach This paper presents a systematic literature review of over 100 peer-reviewed studies published between 2020 and 2024, analyzing the benefits, challenges and future directions of blockchain-AI applications in financial services. Our findings reveal that while blockchain enhances data integrity, security and transparency, AI drives predictive analytics, automation and decision-making efficiency. Findings The synergy of these technologies holds significant potential yet faces critical challenges related to scalability, interoperability, regulatory compliance and ethical AI governance. We identify key research gaps, including the lack of standardized regulatory frameworks, limited real-world case studies and technical barriers to integration. To address these gaps, we propose a comprehensive theoretical framework linking technological advancements to regulatory and ethical considerations. This study contributes to both academic discourse and industry practice, offering actionable insights for financial institutions, technology developers and policymakers navigating the rapidly evolving FinTech landscape. Research limitations/implications The rapidly evolving nature of blockchain and AI technologies may limit the long-term applicability of some findings. The study primarily focuses on published academic literature, potentially overlooking some industry-specific developments. Future research should address the identified gaps, particularly in cross-chain interoperability, ethical AI frameworks, and long-term economic impacts. Empirical studies and case analyses could further validate the theoretical insights presented in this review. Originality/value This study provides a novel, comprehensive synthesis of blockchain and AI applications in financial services, offering valuable insights for both academics and practitioners. By critically examining the synergies and challenges of these technologies, it presents a unique perspective on their transformative potential in FinTech. The proposed research agenda addresses crucial gaps in current knowledge, guiding future investigations. The findings contribute to a deeper understanding of the complex interplay between technological innovation, regulatory frameworks and ethical considerations in the evolving landscape of financial services.
Since the fiscal decentralization process began in 1980, territorial inequality among the poorest and richest regions of the Mexican Republic has grown. In this work, states are divided into two groups: Those that contribute more to the federal budget than they receive and are considered fiscally surplus, and those that contribute less and are in deficit. It is shown that the fiscal deficit of the poorest states has been able to be financed through oil revenues. However, this situation is not sustainable and to maintain the current levels of transfers to the most deficient regions, it will be necessary to squeeze more from the taxpayers of the richest states. Without a new fiscal pact between the states, demands for greater autonomy in the richest regions will increase, as has recently happened in other countries.
The cryptocurrency market, characterized by its extreme volatility and speculative nature, poses significant risk and reward opportunities for investors. This conceptual paper explores the dynamics of risk and volatility within cryptocurrency markets, with a focus on Bitcoin, Ethereum, and other leading digital assets. Drawing exclusively on data from widely recognized online financial sources, this study examines the interplay between cryptocurrency volatility and traditional asset behaviour under market stress conditions. Key crisis events—including the 2016 Brexit referendum, the 2017 market correction, and the 2020 COVID-19 crash—serve as case studies to highlight how cryptocurrencies diverge in risk profiles from conventional assets like Gold and the S&P 500. Additionally, correlations between Bitcoin, Gold, and US Treasury Bonds illustrate the nuanced hedge potential of cryptocurrencies in both short- and long-term investment horizons. This analysis underscores the importance of understanding cryptocurrency behaviour as it matures, while providing insights into the broader financial implications of incorporating these assets into traditional portfolios amidst economic uncertainties.
Bitcoin's high volatility poses significant challenges for short-term price prediction, making it a critical area of study for financial forecasting. Traditional models such as Long Short-Term Memory (LSTM) networks often encounter difficulties in handling long-range dependencies and non-stationary data, limiting their predictive accuracy under volatile conditions. This study introduces the Time-Series Transformer (TST) as a novel approach to predict Bitcoin's short-term prices. By leveraging self-attention mechanisms, TST effectively captures complex temporal patterns in historical Bitcoin data, including prices and trading volume. The data was segmented into fixed-length windows to facilitate model training and testing. Evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Scaled Error (MASE), and R-squared (R²) demonstrated TST’s superior performance over LSTM, particularly during periods of high market fluctuation. Furthermore, TST exhibited notable computational efficiency when working with large datasets, underscoring its scalability. These findings not only highlight TST’s potential for enhancing cryptocurrency price prediction but also pave the way for future research integrating external data sources and exploring further model enhancements for more robust financial forecasting.
Don Roosan, Rubayat Khan, Saif Nirzhor, Fahmida Hai
Objective: The authors propose and evaluate a novel cybersecurity architecture for telehealth that is resilient against future quantum computing cyber threats. By integrating post-quantum cryptography (PQC) with quantum key distribution (QKD) and privacy-preserving mechanisms, data confidentiality and immutability for patient records in a post-quantum era are ensured. Methods: A multi-layered design approach was adopted. The PQC algorithms (e.g. CRYSTALS-Dilithium) were integrated at the blockchain consensus layer to resist quantum attacks. A directed acyclic graph (DAG)-based ledger managed high transaction throughput and latency constraints typical of telehealth. A QKD-enhanced key management protocol leveraged quantum channels for secure exchanges. Zero-knowledge proofs (ZKPs) and secure multiparty computation (MPC) verified transactions without exposing sensitive patient data. A granular access control model used attribute-based encryption and smart contracts to govern which participants could view or modify encrypted medical records. Results: The prototype was developed within a simulated telehealth network comprising hospitals, clinics, and patient devices. The PQC signatures at the consensus layer provided effective resistance to both classical and anticipated quantum attacks. The QKD facilitated secure key distribution, while ZKPs and MPC enabled validation of healthcare transactions without compromising patient privacy. Despite increased computational overhead, the DAG approach efficiently handled parallel transactions, indicating improved scalability compared to traditional linear blockchains. Conclusion: A QKD-enhanced, PQC-driven framework successfully addresses critical security and privacy requirements, safeguarding medical data from emerging quantum threats. Although overhead and infrastructural costs are significant, sustained cryptographic resilience and robust patient confidentiality underscore its suitability for next-generation healthcare systems. Future studies should explore additional optimizations, homomorphic encryption, and larger-scale pilots under regulatory standards.
Saad Alahmari, Amal Alshardan, Fahd N. Al‐Wesabi, Shaymaa E. Sorour · 8 authors
As healthcare services have become increasingly digitized, Electronic Health Records (EHRs) have become widely adopted, providing seamless data exchange among providers. Conventional EHRs, however, are extremely vulnerable to cyber threats because patients' sensitive data is centralized and transmitted electronically. The paper proposes a decentralized, privacy-preserving framework for managing EHRs on blockchains in order to address these security and privacy concerns. Using cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, the proposed system enhances security and ensures data integrity. Additionally, the model facilitates scalable, efficient, and secure access to patient records through the integration of cloud-based storage and blockchain. Using smart contracts, we also ensure compliance with healthcare regulations by regulating access control and authentication. As a result of performance evaluations, the proposed approach is demonstrated to be feasible, and the advantages it offers in terms of security, privacy, and efficiency are highlighted.
Blockchain technologies have overturned the digital finance industry by introducing a decentralized pseudonymous means of monetary transfer. The pseudonymous nature introduced privacy concerns, enabling various deanonymization techniques, which in turn spurred development of stronger anonymity-preserving measures. The purpose of this paper is to create a comprehensive survey of mixing techniques and implementations within the vast ecosystem surrounding anonymization tools and mechanisms available in blockchain cryptocurrencies. First, we begin by reviewing classifications used in the field. Then, we survey various obfuscation techniques, helping to delve into actual implementations and combinations of these techniques. Next, we identify the positive and negative attributes of the approaches and implementations included. Moreover, we examine the implications of anonymization tools for user privacy, including their effectiveness in preserving anonymity and susceptibility to attacks and vulnerabilities. Finally, we discuss the challenges and innovations for extending mixing services into the realm of smart contracts or cross-chain space.
Mohammadhossein Homaei, Victor Gonzalez Morales, Oscar Mogollon Gutierrez, Ruben Molano Gomez · 5 authors
Water distribution systems in rural areas face serious challenges such as a lack of real-time monitoring, vulnerability to cyberattacks, and unreliable data handling. This paper presents an integrated framework that combines LoRaWAN-based data acquisition, a machine learning-driven Intrusion Detection System (IDS), and a blockchain-enabled Digital Twin (BC-DT) platform for secure and transparent water management. The IDS filters anomalous or spoofed data using a Long Short-Term Memory (LSTM) Autoencoder and Isolation Forest before validated data is logged via smart contracts on a private Ethereum blockchain using Proof of Authority (PoA) consensus. The verified data feeds into a real-time DT model supporting leak detection, consumption forecasting, and predictive maintenance. Experimental results demonstrate that the system achieves over 80 transactions per second (TPS) with under 2 seconds of latency while remaining cost-effective and scalable for up to 1,000 smart meters. This work demonstrates a practical and secure architecture for decentralized water infrastructure in under-connected rural environments.
Large language models (LLMs) excel at generating code from natural language instructions, yet they often lack an understanding of security vulnerabilities. This limitation makes it difficult for LLMs to avoid security risks in generated code, particularly in high-security programming tasks such as smart contract development for blockchain. Researchers have attempted to enhance the vulnerability awareness of these models by training them to differentiate between vulnerable and fixed code snippets. However, this approach relies heavily on manually labeled vulnerability data, which is only available for popular languages like Python and C++. For low-resource languages like Solidity, used in smart contracts, large-scale annotated datasets are scarce and difficult to obtain. To address this challenge, we introduce CodeBC, a code generation model specifically designed for generating secure smart contracts in blockchain. CodeBC employs a three-stage fine-tuning approach based on CodeLlama, distinguishing itself from previous methods by not relying on pairwise vulnerability location annotations. Instead, it leverages vulnerability and security tags to teach the model the differences between vulnerable and secure code. During the inference phase, the model leverages security tags to generate secure and robust code. Experimental results demonstrate that CodeBC outperforms baseline models in terms of BLEU, CodeBLEU, and compilation pass rates, while significantly reducing vulnerability rates. These findings validate the effectiveness and cost-efficiency of our three-stage fine-tuning strategy, making CodeBC a promising solution for generating secure smart contract code.
Integrating AI and blockchain technologies holds significant potential for enhancing e-governance, particularly in improving predictive policy execution within smart cities. This study conducts a comprehensive review and bibliometric analysis of existing literature to identify trends, key publications, and research gaps. Using peer-reviewed articles indexed by Scopus and published between 2019 and 2024, we observe a significant rise in research output, focusing on the separate applications of AI and blockchain in e-governance. Key themes identified include enhanced transparency, efficiency in public services, and concerns related to data privacy. However, our analysis uncovers a clear gap in empirical studies addressing the combined use of AI and blockchain technologies. The bibliometric coupling map reveals central clusters around “smart city” and “blockchain,” while topics such as “sustainability” and “climate change” show significant impact, highlighting their relevance to governance. Additionally, the study identifies a lack of cross-disciplinary research, emphasizing the need for future interdisciplinary collaborations. Despite the insights gained, the study is constrained by its reliance on bibliometric methods, which may not capture the complexities of real-world technology integration. Future research should prioritize longitudinal case studies and pilot projects to address regulatory, ethical, and practical challenges, contributing to the responsible adoption of AI and blockchain in digital governance.
This study investigates the impact of blockchain technology adoption on the financial performance of major Australian banks, specifically Commonwealth Bank, Westpac, and ANZ, from 2016 to 2023. Using a descriptive research design and secondary data from annual reports, financial performance was assessed through Return on Assets (ROA) and Return on Equity (ROE). The findings indicate a positive relationship between blockchain adoption and improved financial performance, suggesting gains in efficiency, cost management, and profitability. The study focuses on the Australian banking sector within its unique regulatory and market context. The originality of this research lies in its localized empirical approach, providing context-specific evidence of blockchain’s strategic contribution to financial performance in banking.
Oleh Harmash, Наталія Трушкіна, Kyryl Yevtushenko, Yu. Shkrygun
Currently, smart contracts are recognized as an effective mechanism for managing logistics activities and an innovative technology for managing relationships between a company and consumers based on blockchain. This technology is widely used in various sectors of the economy, including logistics and transport. Many transport companies in different countries of the world are turning to the use of smart contracts in their logistics activities. This is due to the fact that smart contracts simplify the management and processing of documents, contribute to improving the quality and level of service to customers and various groups of stakeholders, transforming the customer relationship management system, and also provide reliable data protection and help transport companies save time on routine operations, which ultimately reduces operating costs. In view of the above, the purpose of the article is to substantiate the need to use smart contacts as a tool to improve the efficiency of logistics management of transport companies in the era of digitalization. As part of the study, a bibliometric analysis of the database of scientific publications was conducted to identify the most cited works and authors. The results of the study will provide a holistic view of the current state and prospects for the development of research on reengineering logistics processes in the activities of transport companies when implementing smart contract technology. The article studies and summarizes scientific approaches to defining the essence and content of the concept of “smart contract”. The features of the use of smart contract technology in the USA, the European Union and Singapore are studied. The main indicators of the development of the smart contract system in the world are analyzed. The sequence of development of the smart contract system in the transport services sector is determined. A matrix of interests and goals of participants in the transport services market, which are provided using smart contract technology, is proposed. C The factors and conditions for the successful implementation of smart contract technology in the logistics activities of a transport company are determined. Alternative scenarios for the transition to smart contract technology as a mechanism for managing the logistics activities of transport companies are proposed.