Dileep Kumar Murala, Shadab Ahmad, V. A. Sankar Ponnapalli, Veera Ankalu Vuyyuru · 5 authors
Wireless sensor networks (WSNs) will be necessary for the next generation of Internet of Things (IoT) apps. They make it possible to use smart and long-lasting sensors and smart automation in healthcare, Industry 4.0, and critical infrastructure. But security is particularly hard since they have built-in flaws, not enough computer power, not enough energy, and a significant danger of insider threats. Standard encryption methods aren't enough, and in situations where resources are restricted, heavier blockchain or machine learning solutions aren't always possible. This study presents ChainShieldML, a lightweight hybrid security architecture that combines Blockchain (BC) and machine learning (ML) to provide decentralised, adaptive, and resource-efficient protection for wireless sensor networks (WSNs). The idea is based on a two-pronged defence strategy. The Blockchain Prevention Module's permissionless blockchain architecture for base stations and cluster heads makes it possible to verify identities, maintain trust in a decentralised way, and keep node interactions unchangeable. Smart contracts made in solidity and connected to the Ethereum ecosystem make it possible to safely register nodes and keep an eye on what they do. The VBFT consensus algorithm makes it possible to quickly validate without using as much computing power as most proof of work methods. The machine learning detection module uses the lightweight gradient boosting method (LightGBM) to find and rank dangerous nodes in real time. LightGBM is the best machine learning classifier when looking at things like recall, F1-score, Matthews correlation coefficient, training cost, and inference latency. ChainShieldML dramatically improves the detection of insider attacks, builds trust, and protects data while using very little energy and having very little communication delay, as shown in tests. For Wireless sensor networks (WSNs) to keep working, all of these things are very important. ChainShieldML is a novel solution to keep IoT devices safe. It uses blockchain's decentralised trust and ML's adaptive intelligence to make a defence system for next-generation wireless sensor networks that can grow, is strong, and is ready for the future.
Blockchain networks face a critical but understudied threat: wave attacks that exploit difficulty adjustment algorithms through strategic mining participation. Adversaries cyclically withdraw and re-enter mining to create oscillations that degrade network liveness and destabilize honest miners’ revenue. We present the first production-ready framework that maintains network responsiveness while enabling robust, post hoc threat detection. The framework employs a statistically rigorous pipeline featuring controller-aligned anomaly detection, transitive collusion grouping via union-find, and Benjamini–Hochberg False Discovery Rate control. We formally prove the economic viability of this architecture: when penalties on unvested rewards are enabled by governance, wave attacks become asymptotically unprofitable for rational adversaries. Evaluated on a 128-node distributed testbed simulating Bitcoin, Ethereum Classic, and Monacoin networks over 30 independent runs, our framework achieves 92.7% F1-score in detecting attacks, significantly outperforming baseline methods (74.7%). This work provides a complete, theoretically-grounded solution for securing proof-of-work blockchains against difficulty manipulation, forming the foundation for the adaptive AI-driven enhancements presented in our companion paper (Part II).
This paper presents a ubiquitous, blockchain-based system designed to improve transparency, traceability and trust in supply chains involving autonomous vehicles (AVs). The framework integrates Internet of Things (IoT) sensors, radio-frequency identification (RFID) and QR identifiers, global positioning system (GPS) tracking, and mobile communications with smart contracts implemented on the Ethereum 2.0 blockchain. The main contributions are as follows: (1) an architecture enabling real-time monitoring and automated verification of logistics transactions; (2) a proof of concept integrating blockchain, the IoT and Android-based OBUs; and (3) a quantitative analysis of gas and smart contract execution costs. Experimental tests show gas consumption ranging from 21,000 to 5,000,000 units and transaction costs ranging from 0.0001 to 0.0033 ETH, confirming the system’s technical feasibility and cost-efficiency. As well as cost and efficiency, the process improved transparency, real-time traceability and decentralized verification, confirming the system’s efficacy for supply chains involving autonomous vehicles.
In the contemporary global job market, the secure and efficient verification of a candidate’s academic qualifications presents a significant challenge, particularly across international boundaries. Conventional techniques frequently necessitate physical documents or PDF scans, rendering them inefficient, susceptible to falsification, and hazardous about privacy. This study presents a contemporary, scalable framework that integrates Zero-Knowledge Proofs (ZKPs), blockchain technology, and decentralized storage (IPFS) to establish a secure, privacy-oriented method for candidate verification. In this proposed system, candidates submit their academic documents, which are digitally signed by the issuing universities using cryptographic methods. The signed files are preserved on IPFS, guaranteeing their integrity and accessibility. The hash of each document is then stored on a blockchain, either Ethereum or Polygon, offering a public and immutable reference. Zero-Knowledge Proofs enable candidates to validate the legitimacy of their credentials while safeguarding sensitive information. Human Resources teams can authenticate these documents in real time, validating their integrity against the blockchain hash while preserving the candidate’s confidentiality. The evaluation results demonstrate that Ethereum offers robust decentralization and trust; nevertheless, Polygon proved to be more pragmatic because to its reduced gas price and expedited transaction times, making it suitable for high-volume recruitment. This proposed initiative addresses weaknesses in digital recruitment by guaranteeing trust, privacy, and automated credential verification procedure. It provides a customized approach for present recruitment requirements, particularly for organizations engaged in cross-border hiring, where security, scalability and protection of candidate information are paramount.
Herman Herman, Rinday Zildjiani Salji, Herman Yuliansyah
This research studies implementation of decentralized applications (DApps) that are combined with blockchain technology and IPFS for storing patient medical data. The goal of this research is to increase the security, transparency, and access control of stored medical data to make sure only legitimate users can access the data. The proposed system uses smart contracts on the Ethereum network to handle user rights of access (doctors, patients, and admins) and ensure data integrity through the blockchain immutability feature. Patient medical records are retained in IPFS and traced using the Content Identifier (CID). Implementation outcome reveals that the system can safely process medical information, keeping patients in full control of their information, and restricting data access only to scheduled time. This system also shows the potential of blockchain and IPFS technology-based applications in achieving a more efficient health ecosystem focused on safeguarding people's data.
Blockchain technology has fueled exponential growth across various industries, including finance, supply chain management, and healthcare, enabling greater transparency in transaction management and supporting decentralized implementations. This paper presents a comprehensive performance analysis of permissioned and permissionless blockchain platforms, specifically Hyperledger Fabric and Ethereum. The study evaluates these platforms with varying transaction workloads (100 to 1000 transactions) with a consistent network. Our objective is to measure key performance metrics such as send rate, throughput, latency, resource utilization, and transaction success rate using established benchmarking tools and methodologies. The findings offer valuable insights into the comparative strengths, limitations, and optimal use cases of these blockchain platforms across different performance parameters. The results indicate that Hyperledger Fabric achieves, on average, 3.5–4.5 times higher throughput and 10–12 times lower latency than Ethereum, while consuming 2.5–3 times less memory across tested workloads. In contrast, Ethereum demonstrates a higher send rate and lower CPU demand in some operations. Overall, the study suggests that Hyperledger Fabric is better suited for enterprise applications that demand high scalability and performance.
Andrei Brînzea, Răzvan-Andrei Leancă, Iulian Aciobăniţei, Florin Pop
Traditional Time-Stamping Authorities provide reliable temporal evidence. However, they operate as single points of trust and do not supply a tamper-evident record of event ordering. This paper presents a standards-compliant extension that anchors each issued timestamp token to a blockchain ledger while preserving full compatibility with existing TSA clients. Our proposal is compliant with RFC 3161. The implementation uses an identifier in the token that is also included in the distributed ledger. Experiments were conducted on the Ethereum and Hyperledger Fabric networks. Our design allows for external verification of the existence and relative ordering of tokens without modifying the RFC-defined validation process. Experimental evaluation compares issuance latency, anchoring time, and transaction cost across both networks. Our work presents a practical and viable approach to enhancing trust in digital signature infrastructures by combining the regulatory reliability of qualified TSAs with the auditability and persistence of distributed ledgers.
Access control in P2P network is extremely challenging due to several reasons such as decentrailized network, anonymous connectivity, high churn, resource constraints and large attack surface. The number of attributes to be tracked grows exponentially as P2P networks scale, exacerbating issues in traditional approaches such as RBAC, ABAC, RuBAC. This paper presents a novel blockchain-based access control framework for P2P networks using Ethereum smart contracts to offer an adaptable, decentralized, and scalable solution. Our token economy framework incentivizes nodes that demonstrate consistent compliance and penalize malicious actions using fungible ERC-20 tokens. Hierarchical static role-based structure consisting of regular members, primary and secondary group is the foundational layer of role management. Dynamic state management through a finite state machine classifies peers as benign, suspicious, or malicious mapping their behavior to the STRIDE threat model. We have used standard blockchain tool stack consisting of OpenZeppelin (contract library), Mocha (testing), and Web3.js (deployment). Our framework is tested with eight test cases, such as privilege escalation and denial-of-service attacks, and built using three types of Ethereum smart contracts. Our focus is on collecting four metrics gas cost, latency, execution time, simultaneous requests that provides a comprehensive view of realistic network activity. This study goes beyond conceptual nature of state-of-art architecture to showcase the realistic mapping between resources, peer roles and standard threats in a P2P network. The experimental results shows low gas costs (upto 1.4 million gas) and fast execution times (80–550 ms), confirming the design efficiency. The framework handles up to five concurrent requests with minimal performance impact, demonstrating strong scalability across roles and threat conditions. This framework enables scalable, behavior-driven governance in P2P networks, enhancing IoT and Web3 security.
I Made Candra Girinata, Budi Styawan, Arwin Wahyu Saputra, M Aidil Arif · 5 authors
ABSTRAK Perkembangan aset kripto yang pesat, khususnya Ethereum, menuntut adanya model prediksi harga yang akurat untuk mendukung strategi investasi dan manajemen risiko. Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja dua algoritma machine learning ensemble, yaitu Random Forest (RF) dan XGBoost, dalam memprediksi harga harian Ethereum. Dataset historis ETH/USD sebanyak 3.423 observasi dari periode September 2016 hingga Juli 2025 diperoleh dari platform Bitfinex. Setelah melalui tahap pra-pemrosesan data dan rekayasa fitur temporal, dataset dibagi dengan rasio 80:20 untuk pelatihan dan pengujian. Model dievaluasi menggunakan metrik Root Mean Square Error (RMSE) dan Koefisien Determinasi (R²). Hasil eksperimen menunjukkan bahwa XGBoost secara signifikan mengungguli Random Forest, dengan nilai RMSE 134.63 dan R² 0.958. Sebagai perbandingan, Random Forest menghasilkan RMSE 208.45 dan R² 0.899. Temuan ini mengindikasikan bahwa mekanisme boosting pada XGBoost lebih efektif dalam menangkap kompleksitas dan volatilitas data pasar kripto. Kata kunci: Prediksi Harga, Ethereum, Machine Learning, XGBoost, Random Forest.
The decentralized and anonymous nature of Ethereum makes it a prime target for phishing scams. These scams account for nearly 50% of all blockchain-related fraud, thereby causing a substantial financial loss and eroding user trust. Unlike conventional phishing, Ethereum phishing users exploit user anonymity, lack of awareness, and market-driven dynamics to deceive normal users. Despite of a plethora of research in this direction, there is a lack of a rigorous and comprehensive survey which can fortify an insightful comparison of the existing works and provide a concrete future research guidance. To this end, this paper presents a systematic review of 90 studies published between 2020 and 2024, offering the following novel contributions, (1) Structured Taxonomy: We introduce a structured three-fold taxonomy that classifies existing methods into feature engineering-based, representation learning-based, and fusion-based frameworks. (2) Theoretical Analysis: Through theoretical analysis, we evaluate these approaches against the critical research challenges, such as rapid network dynamism, data leakage, and network sparsity and provide a comparative mapping of novel techniques adopted across the studies. (3) Empirical Evaluation: We conduct an extensive empirical evaluation of 14 representative models over multiple public datasets to assess their robustness under varying data conditions. The findings indicate that while feature-based models are more interpretable, they struggle with temporal adaptability; representation learning approaches, particularly GNN-based models, capture complex behavioral patterns but are computationally demanding and less explainable. Fusion methods demonstrate the most balanced trade-off between accuracy, scalability, and interpretability. (4) Future Research Guidance: Finally, we identify still persisting issues such as network sparsity, behavioral volatility, and scalability, and outline future research directions emphasizing temporal graph reasoning, self-supervised fusion, and explainable AI for developing transparent and deployable phishing detection frameworks on Ethereum.
Transaction fees play a crucial role in determining the efficiency and scalability of blockchain networks, particularly in Ethereum, where gas fees fluctuate significantly due to network congestion and competitive bidding. This study analyzes transaction fee patterns in the Ethereum blockchain and their impact on network efficiency by examining key blockchain metrics such as block density, transaction size, and transaction fee variability. The findings indicate that the mean transaction fee is 0.0342 ETH, with a median of 0.0008 ETH, demonstrating significant fee variability. The study also finds a strong positive correlation (r ≈ 0.75, p < 0.01) between transaction fees and block density, as well as a moderate correlation with transaction size (r ≈ 0.58, p < 0.01), highlighting the direct impact of network congestion on fee structures. Time series forecasting with Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models reveals cyclical trends in transaction fees, often influenced by major network activities such as NFT releases, DeFi protocol surges, and high-frequency trading. The LSTM model achieves a lower RMSE (0.09) compared to ARIMA (0.15), demonstrating its superior predictive capability for fee trends. Additionally, anomaly detection techniques identify outlier transactions with fees exceeding 2.5 ETH, often associated with front-running strategies, priority gas auctions (PGA), and inefficient smart contract executions. Despite improvements introduced by EIP-1559, the findings indicate that Ethereum’s transaction fee market remains highly volatile, with block density fluctuating between 512.0% and 3896.0%, causing extreme fee spikes during congestion periods. The presence of large transactions (maximum size: 250 bytes) further amplifies fee inefficiencies, reinforcing the need for improved scalability solutions. This study underscores the necessity of Layer-2 rollups, dynamic block size adjustments, and more adaptive fee mechanisms to enhance blockchain efficiency. Future research should explore comparative studies across blockchain networks, advanced predictive modeling techniques, and the role of miner extractable value (MEV) in transaction ordering fairness. The study’s insights provide valuable guidance for developers, users, and policymakers aiming to optimize Ethereum’s transaction fee structure and enhance overall blockchain performance.
In today’s technologically advancing world, many fields from finance to healthcare and education are shifting toward a digital and decentralized format. A significant transformation is underway with the currency of the masses. Blockchain-based cryptocurrencies like Bitcoin and Ethereum allow users to generate fungible tokens anonymously through smart contracts. However, these features also facilitate illicit transactions and cybercrimes like fraud, phishing, and money laundering. The proposed work explores the identification of suspicious transactions on the Ethereum blockchain by leveraging advanced machine-learning techniques. An Extreme Gradient Boosting (XGBoost) classifier is optimized for spotting unauthorized or malicious transactions, exploring features like transaction patterns and value anomalies. Feature scaling and log transformations normalize skewed distributions, while rigorous model training and hyperparameter tuning enhance the system's precision, recall, and overall accuracy. Other aids, such as feature importance rankings, precision-recall curves, and diagnostic statistics, provide useful information on fraud patterns. Evaluation of the model shows that integrating cost-sensitive learning significantly reduces false positives, from 51 to 44, representing a 13.7% decrease, which enhances practical usability by minimizing false alerts and manual verification efforts. Although there was a slight increase in false negatives (from 14 to 15), the overall classification accuracy improved. The model demonstrated strong performance in managing class imbalance which is common in fraud detection contexts.
Ethereum, as a leading blockchain platform, experiences high variability in transaction fees due to network congestion, gas bidding, and computational complexity. This study analyzes 10,000 Ethereum transactions to identify key factors influencing transaction fees, block density, and staking mechanisms. The results show that transaction fees vary significantly, with an average of 0.1826 ETH and a standard deviation of 0.2381 ETH, indicating substantial fluctuations. A strong positive correlation (r = 0.72) between transaction size and transaction fee confirms that larger transactions incur higher costs due to increased computational demand. Time-series analysis reveals periodic spikes in gas fees, aligning with network congestion patterns. Block density averages 1718.8% (std = 501.01%), showing that some blocks are highly congested while others are underutilized. An Isolation Forest anomaly detection model identifies 3.4% of transactions as outliers, exhibiting unusually high gas fees, which may be caused by priority-based bidding, inefficient smart contract execution, or potential fee manipulation. Further analysis demonstrates that Coin Age and Stake Reward significantly influence transaction success rates. Transactions with older coins show a 7.8% higher success rate, indicating that validators may prioritize transactions with greater historical weight. Additionally, Stake Reward positively affects the Block Generation Rate (p < 0.05), confirming its role in securing the network and optimizing transaction processing. These findings provide valuable insights for Ethereum users, developers, and validators to optimize gas fees, transaction timing, and staking incentives. While this study offers critical observations, future research should focus on real-time gas fee monitoring, deep learning-based congestion forecasting, and the impact of Layer-2 scaling solutions. Understanding Ethereum’s Proof-of-Stake (PoS) dynamics will be essential for ensuring fair transaction processing, reducing gas fees, and improving blockchain efficiency.
This study examines the relationship between gas prices and transaction values on the Ethereum blockchain, providing a detailed analysis of transaction dynamics and the factors influencing gas price determination. The correlation coefficient between gas prices and transaction values is -0.0273, indicating a very weak and negative relationship. Instead, gas prices are driven by factors such as computational intensity, network congestion, and user prioritization. Functions with higher computational demands, such as mint, recorded the highest mean gas price of 120.45 Gwei, with a standard deviation of 15.30 Gwei, while functions like approve and transfer exhibited mean gas prices of 98.30 Gwei and 110.80 Gwei, respectively. Recipient address analysis reveals a strong concentration of transaction values, with the top recipient address receiving 49.95 ETH consistently, indicating high-value operations directed toward specific accounts. High-gas transactions, defined as those above the 90th percentile, displayed a mean gas price of 191.96 Gwei with minimal variability, while their corresponding transaction values varied widely, with a mean of 23.91 ETH and a standard deviation of 13.66 ETH. These findings provide critical insights into Ethereum transaction behavior, emphasizing the role of function type and user prioritization in shaping gas price decisions. Future research should investigate the impact of network upgrades such as EIP-1559, the adoption of Layer-2 scaling solutions, and temporal trends in transaction behavior to enhance network scalability and cost efficiency as Ethereum continues to evolve.
Michele Pasqua, Sofia Mari, Ferdinando Santoro, Mariano Ceccato
With Ethereum’s rise as the leading platform for decentralized applications, securing Ethereum smart contracts, very often having a financial impact, becomes paramount. Existing research lacks a comprehensive overview of Ethereum defects (and the terminology is often inconsistent), making it difficult for researchers, developers, and industry professionals to navigate this nowadays critical topic. This necessitates a unified source of information detailing defects affecting Ethereum and its smart contracts, along with their root causes, impact, and mitigation strategies. In this paper, we propose a knowledge base of defects , encompassing security vulnerabilities and code flaws found in the Ethereum blockchain and its smart contracts. We started by performing a systematic literature review to identify the currently known defects and then created a hierarchical tag system to classify them. This system was then used to build an ontology allowing users to easily search and learn about Ethereum defects. We also implemented EDOV, a tool to graphically navigate and explore the ontology, perform search queries, and visualize defect details, such as examples of defective/fixed code. As new defects may appear in the future, the ontology and the tool are built with extensibility in mind. We believe this research is a valuable contribution to helping developers and practitioners avoid common mistakes, improving the overall security and reliability of the Ethereum ecosystem.
Jianlong Xu, C. F. Xu, Rongtao Zhang, Feixiang Diao · 5 authors
With the wide application of blockchain technology in finance, IoT, healthcare, and other fields, phishing scams have emerged as a growing security threat. Existing detection methods often lack in-depth modeling of the directional properties of transaction flows and struggle to effectively capture diverse transaction behaviors, directional relationships, and key neighbor dependencies. To address these limitations, we propose TGAT-MPGCN, a direction-aware phishing detection model that constructs three complementary first-order subgraphs, a sending graph, a receiving graph, and a bidirectional graph to explicitly capture transaction directionality. By integrating a graph-attention mechanism with weighted neighbor aggregation, the model enhances feature learning. Experimental evaluations on an Ethereum transaction dataset demonstrate the superior performance of our approach, achieving an accuracy of 97.21%, an AUC of 0.9721, an F1-score of 0.9719, a recall of 0.9629, and a precision rate of 98.11%, significantly outperforming traditional detection methods. This study offers a practical and scalable solution for accurate phishing detection in blockchain transaction networks.
Kundan Mukhia, Buddha Nath Sharma, Salam Rabindrajit Luwang, Md. Nurujjaman · 7 authors
We study how the 2024 U.S. presidential election, viewed as a major political risk event, affected cryptocurrency markets by distinguishing human-driven peer-to-peer stablecoin transactions from automated algorithmic activity. Using structural break analysis, we find that human-driven Ethereum Request for Comment 20 (ERC-20) transactions shifted on November 3, two days before the election, while exchange trading volumes reacted only on Election Day. Automated smart-contract activity adjusted much later, with structural breaks appearing in January 2025. We validate these shifts using surrogate-based robustness tests. Complementary energy-spectrum analysis of Bitcoin and Ethereum identifies pronounced post-election turbulence, and a structural vector autoregression confirms a regime shift in stablecoin dynamics. Overall, human-driven stablecoin flows act as early-warning indicators of political stress, preceding both exchange behavior and algorithmic responses.
This paper explores how Bitcoin and Ethereum differ from traditional financial assets such as gold, Brent crude oil, the S&P 500 and Apple Inc. in terms of risk, return and integration with the traditional financial market over the period of 2018-2025. The thesis evaluates whether these digital assets can serve as viable components of a diversified investment portfolio. The motivation stems from the recent institutionalization of cryptocurrencies, including the recent approval of spot Bitcoin and Ethereum ETFs and wide public interest. 2858 observations of log returns were used to analyse correlation, multivariate regression, volatility, CAPM regression and Sharpe ratio. The results show that Bitcoin and Ethereum exhibit very low correlations with traditional assets, which supports their ability to act as diversifiers. The regression models revealed that gold and the S&P 500 have small but statistically significant explanatory power for cryptocurrency returns, while Apple Inc. and Brent crude oil do not. Volatility analysis confirms that Bitcoin and especially Ethereum are much more volatile than all traditional assets in the sample. CAPM results show that both digital assets respond positively to market movements, implying slow financial integration. Returns of cryptocurrencies were extremely high, but when the Sharpe ratios were computed, cryptocurrencies showed weak risk-adjusted performance, compared to Apple Inc. and gold. Overall, the findings show that cryptocurrencies are assets with high risk and are driven more by crypto-specific factors, but are increasingly integrating into the broader traditional financial market. They provide diversification benefits but only in small allocations.
Blockchain is the new age internet equivalent evolution along with the rise of artificial intelligence they both are revolutionizing a new way of safer, faster, and government free interaction for real time financial verification. This study primarily investigates the role of these new age technologies in providing better security and transparent transactions in accounting systems related to crypto notably in decentralized finance (DeFi) ecosystems currently prevailing on solana, Ethereum and Base networks. These blockchain networks currently power billions of transactions in value using great techniques to ensure immutable, verifiable audit trails while removing any 3rd party interference In 2025 major accounting firms and compliance institutions will use AI-driven analytics, anomaly detection and predictive modelling to provide much better scope and structure of audit; this will help to decrease human errors and compliance delays by an estimate of 30%. AI along with blockchain technologies will provide real time decentralized monitoring and forensic analysis via smart contracts with include the major one such as ERC-20The combination of blockchain’s immutable ledger and AIs supremely adaptive intelligence creates a new wave of real time auditing right from retrospective verification to predictive ongoing assurance. However due to delays and legislative fragmentation issues and government ethical remain major obstacles for successful development. Finally our study demonstrates that the combination of Al with blockchain marks a fundamental change toward transparent, automated, and resilient accounting ecosystems capable of maintaining confidence in an increasingly digital and decentralized global economy.
Open access
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Blockchain Technology Applications and Security
Internet of Things and AI
Innovations and Analysis in Business and Education
International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Open access
Advanced Data Storage Technologies
Security and Verification in Computing
Physical Unclonable Functions (PUFs) and Hardware Security
Healthcare collaborative processes still encounter major challenges, particularly regarding the interoperability of heterogeneous information systems, the traceability of medical interventions, and the secure sharing of patient data under strict privacy regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). This paper presents a patient-centric, blockchain-based framework designed to overcome these limitations. The proposed solution integrates smart contracts and non-fungible tokens (NFTs) within the Ethereum blockchain to ensure data integrity, traceability, and privacy preservation. Furthermore, a compliance-by-design mechanism is embedded into the smart contracts to enable self-supervision of collaborative workflows without third-party intervention. A Proof-of-Authority (PoA) consensus protocol is also adopted to optimize validation efficiency and significantly reduce computational and energy costs.
Digital forensics faces unprecedented challenges with the emergence of digital twins and metaverse technologies. This paper presents the first comparative analysis between blockchain-based and traditional database systems for managing digital twin evidence in forensic investigations. We conducted controlled experiments comparing the Ethereum blockchain with IPFS storage against traditional SQL databases for digital twin evidence management. Our findings reveal that while blockchain provides superior data integrity and immutability, crucial for forensic applications, traditional databases offer better performance consistency. The blockchain implementation showed faster average storage times but higher variability in retrieval operations. Both systems maintained forensic integrity through hash verification, though blockchain's immutable nature provides additional security guarantees essential for legal proceedings. This research contributes to the development of robust digital forensic methodologies for emerging technologies in the metaverse era.
The major focus of this research study is to understand the impact of the Russia-Ukraine crises or war on three major Crypto currencies like Bitcoin, Binance coin and Ethereum. This study also provides insight about the reaction of the Crypto market during the ongoing war situation and how the Cryptocurrencies react during the war crises, either bitcoin, ethereum, and the binance coin have the positive impact or the negative impact during the war, or the war has no impact on Cryptocurrencies. The relationship between these cryptocurrencies are also examined during this research. The major findings show that the ARCH effect exist in the Binance coin, Bitcoin, and the Ethereum market series. The research study used the GARCH methodology for analysis of results. For Bitcoin and Binance coin there is no direct impact in it, and factor of volatility exist in it. For Ethereum there is no direct impact of war, and factor of volatility does not exist in it. The research gives valuable insights to investors and policy makers.
Dynamic spectrum sharing (DSS) is essential for 6G networks, yet existing blockchain-based DSS solutions often lack an integrated approach that simultaneously addresses trust, allocation fairness, and system scalability. This paper proposes HierSpectrumChain, a hierarchical blockchain framework that incorporates a global main chain, localized sub-chains, and a smart-contract based Stackelberg auction for credible and automated spectrum allocation. The system model formalizes interactions among spectrum holders, secondary users, and sub-chain validators, enabling transparent bidding and decentralized coordination. A proof-of-concept implementation on an Ethereum Ganache environment evaluates the functional correctness of the auction workflow and measures throughput under varying client loads. While the evaluation is limited to a single-node testbed, the results demonstrate the feasibility of the proposed architecture and establish a basis for future multi-peer experiments on permissioned blockchains. This work provides a coherent design and initial validation for blockchain-enabled DSS in 6G networks.