N. A. Natraj, J. Midhunchakkaravarthy, Brojo Kishore Mishra
INTRODUCTION: The application of blockchain technology to Internet of Things (IoT) systems offers substantial potential for enhancing security, but traditional consensus mechanisms are ill-suited for resource-constrained environments. While hybrid consensus solutions have emerged as a promising alternative, a systematic framework for their classification and evaluation is notably absent. OBJECTIVES: This study addresses this critical gap by introducing a novel, application-driven framework for analyzing hybrid consensus mechanisms, underpinned by a quantitative synthesis of performance benchmarks. METHODS: We analyze diverse architectures—including combinations of Proof of Work (PoW) and Proof of Stake (PoS), PBFT-enhanced systems, and hierarchical models—through the lens of specific IoT application priorities, such as latency, energy efficiency, and scalability. Case studies of IOTA's Tangle, IoTeX's Roll-DPoS, and Hyperledger Fabric illustrate these practical trade-offs. RESULTS: Our framework reveals not only primary performance trade-offs but also critical "second-order" complexities, such as emergent vulnerabilities at the intersection of different consensus layers. CONCLUSION: Our findings demonstrate that this structured, quantitatively-grounded approach provides an effective methodology for designing and selecting regulatory-compliant hybrid consensus solutions for specific IoT applications.
Ala Alrawajfi, Mohd Tahir Ismail, Sadam Al Wadi, Saleh Atiewi
Ethereum and other cryptocurrencies are volatile, making Ethereum-USD rate evaluation difficult.Due to unsuccessful data collection and exchange downtimes, financial time series data are incomplete and lacking critical values.Thus, assessments may be incomplete, and trends may be miscalculated.This research builds and tests an ARIMA-random forest data imputation method to overcome these concerns.This innovative strategy uses AutoRegressive Integrated Moving Average (ARIMA) to describe the linear chronologic sequence relationship and random forest to solve nonlinearity.The suggested method uses ARIMA to handle the linear time-dependent data feature and random forest to reduce estimation errors to improve Ethereum-USD closing price estimates.The mean absolute error (MAE) and mean absolute percentage error (MAPE) results demonstrate that the proposed hybrid model significantly outperforms conventional imputation approaches across all missing data levels (10%-50%).For example, at 30% missing data, the hybrid model achieved an MAE of 0.91 and a MAPE of 0.00074, compared to ARIMA's MAE of 2.21 (MAPE 0.00185) and Random Forest's MAE of 2.34 (MAPE 0.00186).Across all scenarios, the hybrid model reduced MAE by up to 60% and MAPE by over 55% relative to the best single-method baseline, indicating superior robustness and accuracy in handling incomplete Ethereum-USD datasets.By providing precise market and result knowledge, these insights help financial analysts, traders, and researchers make accurate, efficient decisions.
András Nagy, János Tapolcai, István András Seres, Bence Ladóczki
Proof-of-stake consensus protocols often rely on distributed randomness beacons (DRBs) to generate randomness for leader selection. This work analyses the manipulability of Ethereum's DRB implementation, RANDAO, in its current consensus mechanism. Even with its efficiency, RANDAO remains vulnerable to manipulation through the deliberate omission of blocks from the canonical chain. Previous research has shown that economically rational players can withhold blocks known as a block withholding attack or selfish mixing when the manipulated RANDAO outcome yields greater financial rewards.
Layer 2 rollups offer promising solutions to address Ethereum's scalability issues. However, the centralized nature of the sequencer in these rollups makes them vulnerable to denial of service attacks, in which adversaries overwhelm the sequencer with invalid transactions that cannot be included in blocks, thereby exhausting its computational resources for transaction processing. To mitigate such threat, layer 2 rollups implement the legality check mechanism to filter out invalid transactions before they reach the sequencer.
Zero-knowledge proofs of training (zkPoT) allow a party to prove that a model is trained correctly on a committed dataset without revealing any additional information about the model or the dataset. Existing zkPoT protocols prove the entire training process in zero knowledge; i.e., they prove that the final model was obtained in an iterative fashion starting from the training data and a random seed (and potentially other parameters) and applying the correct algorithm at each iteration. This approach inherently requires the prover to perform work linear to the number of iterations.
Mohit Malik, Rahul S Mor, Vijay Kumar Gahlawat, Vikas Kumar
• Presents a novel hybrid blockchain and AI-enabled end-to-end SC traceability model. • Validates a multilayer Web3-based architecture integrating smart contracts, ML algorithms & IoT-enabled data capture. • Offers a proof-of-concept and feasibility analysis, highlighting scalability, transaction speed & system responsiveness. Conventional traceability systems without real-time information transmission are susceptible to tampering. In contrast, blockchain and artificial intelligence (AI)-enabled traceability models offer transparency and accountability, given their decentralized nature and immutability. This research conceptualizes and develops a hybrid blockchain and AI-enabled traceability (prototype) model and implements it in the dairy industry. The study includes a collaborative research methodology, including a literature review to analyze the existing traceability solutions, identify data entry points, select model requirements, and deploy smart contracts, decentralized applications (Dapps) and Web3 technologies to develop and validate the proposed model via Testnet . The findings present the user interface developed as a prototype traceability model and its characteristics, such as transparency, decentralized nature, and immutability, followed by practical validation. The post-implementation data analysis highlighted the security, privacy, smart contract validation rules, and comparative insights, as well as the alignment of the theoretical model with practical applications using Web3 technologies. This research contributes to the literature on hybrid blockchain and AI-enabled traceability, highlighting the potential for exploring opportunities in the food industry.
M. Zeeshan Haider, Tayyaba Noreen, M. D. Assuncao, Kaiwen Zhang
Sharding has emerged as a key technique to address blockchain scalability by partitioning the ledger into multiple shards that process transactions in parallel. Although this approach improves throughput, static or heuristic shard allocation often leads to workload skew, congestion, and excessive cross-shard communication diminishing the scalability benefits of sharding. To overcome these challenges, we propose the Predictive Shard Allocation Protocol (PSAP), a dynamic and intelligent allocation framework that proactively assigns accounts and transactions to shards based on workload forecasts. PSAP integrates a Temporal Workload Forecasting (TWF) model with a safety-constrained reinforcement learning (Safe-PPO) controller, jointly enabling multi-block-ahead prediction and adaptive shard reconfiguration. The protocol enforces deterministic inference across validators through a synchronized quantized runtime and a safety gate that limits stake concentration, migration gas, and utilization thresholds. By anticipating hotspot formation and executing bounded, atomic migrations, PSAP achieves stable load balance while preserving Byzantine safety. Experimental evaluation on heterogeneous datasets, including Ethereum, NEAR, and Hyperledger Fabric mapped via address-clustering heuristics, demonstrates up to 2x throughput improvement, 35\% lower latency, and 20\% reduced cross-shard overhead compared to existing dynamic sharding baselines. These results confirm that predictive, deterministic, and security-aware shard allocation is a promising direction for next-generation scalable blockchain systems.
The rapid evolution of financial technologies (FinTech) and digital assets—including cryptocurrencies, decentralized finance (DeFi), and tokenized capital markets—has created an unprecedented need for secure, scalable, and computationally efficient systems. This study examines the transformative potential of quantum computing in reshaping financial technology infrastructures and digital asset ecosystems. Traditional computational models, constrained by classical encryption limits and the exponential growth of financial data, face increasing inefficiencies in handling real-time risk assessment, portfolio optimization, and transaction verification. Quantum computing, with its capacity for superposition, entanglement, and parallel state evaluation, provides novel opportunities to redefine data security, financial modeling, and cryptographic mechanisms in the digital economy. The research explores how quantum algorithms—notably Quantum Approximate Optimization Algorithm (QAOA), Quantum Fourier Transform (QFT), and Grover’s search algorithm—can enhance financial operations such as market forecasting, fraud detection, and asset pricing. Additionally, it investigates quantum-resistant cryptography to safeguard digital asset networks against the vulnerabilities introduced by future quantum decryption capabilities. By integrating hybrid quantum–classical frameworks, this approach enables the development of sustainable, adaptive, and transparent financial systems. The findings highlight quantum computing’s potential to advance financial inclusion, increase transaction speed, and improve systemic resilience. As global financial markets transition toward quantum readiness, the convergence of FinTech and quantum innovation is expected to redefine how digital assets are managed, traded, and secured—marking a paradigm shift toward quantum financial intelligence.
Purpose Cement manufacturing is a vital yet emission-intensive industry that faces challenges in certifying sustainable production practices, driven by the need for transparency, accountability and regulatory compliance. This paper provides a blockchain-based certification framework to enhance traceability and sustainability in cement production. Design/methodology/approach Leveraging Ethereum smart contracts (SCs) and blockchain technology, our solution ensures decentralized, immutable tracking of emissions data, production processes and compliance through secure interactions among regulators, manufacturers and auditors. The framework facilitates deployment, registration, reporting, auditing and certification. Detailed insights into system architecture, algorithms, SC implementation and validation are provided. Security analysis evaluates access controls, data privacy and vulnerability mitigation, while cost analysis highlights the framework's economic feasibility by examining gas costs for key functions. Findings The blockchain-based framework successfully produced a scalable solution with a modular design to allow for flexible deployment by different regulatory bodies. Transparency was achieved through blockchain events announcement, and accuracy was ensured through periodic auditing rounds. Security analysis results show no serious vulnerabilities. The developed framework proves a cost-effective solution for certifying sustainable production practices in the cement industry, with potential applications across other heavy manufacturing sectors. Research limitations/implications Cement manufacturing is a vital yet emission-intensive industry that faces significant challenges in certifying sustainable production practices, driven by the need for transparency, accountability and regulatory compliance. Practical implications We believe that this paper provides the following practical implications: Innovative Framework: A blockchain-based certification system promoting adherence to sustainable processes in cement manufacturing. SC Implementation: Detailed insights into the system architecture, implementation and validation of algorithms and SCs. Comprehensive Analysis: A thorough security analysis of SC coding and a cost analysis highlighting the economic feasibility of our proposed framework. Social implications The proposed methodology will help all stakeholder involved in the cement production and supply chain have a better understanding and a robust tool to observe and learn about operations, tasks and other activities made through sustainable cement production Originality/value The study contributes a novel blockchain-based method to certify sustainable production in energy-intensive industries, especially cement, offering a valuable tool that ensures transparency and immutability.
The exponential growth of digital finance—encompassing online banking, digital assets, decentralized finance (DeFi), and algorithmic trading—has intensified the need for robust cybersecurity frameworks. However, the rise of quantum computing presents a dual challenge: while it enables revolutionary advances in data analytics and optimization, it simultaneously threatens the cryptographic foundations of contemporary financial systems. This research explores the emerging field of quantum cybersecurity and its implications for safeguarding financial infrastructures in the post-quantum era. Traditional encryption methods such as RSA, ECC, and Diffie–Hellman key exchange are vulnerable to quantum attacks, particularly through Shor’s algorithm and Grover’s search algorithm, which can efficiently break asymmetric and symmetric cryptographic schemes. The study evaluates quantum-resistant cryptographic protocols—including lattice-based, hash-based, and multivariate polynomial encryption—as viable solutions for ensuring financial data integrity, transaction confidentiality, and regulatory compliance in quantum-vulnerable environments. Furthermore, it investigates Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as hardware-assisted techniques for achieving unconditional security in financial communications and transaction authentication. By integrating quantum cryptography, hybrid encryption, and AI-driven threat modeling, this work outlines a roadmap for financial institutions transitioning toward quantum-secure infrastructures. The findings demonstrate that quantum cybersecurity is not merely a defensive measure but a transformative enabler for resilient digital finance, aligning with global efforts to achieve technological sovereignty, financial stability, and sustainable innovation in the era of quantum computing.
Smart contracts automate blockchain transactions but are vulnerable to reentrancy attacks, where an attacker repeatedly calls a function before the contract updates its state, stealing funds. A well-known case is the 2016 DAO exploit, which caused a loss of $60 million.This study investigates methods to protect contracts from such attacks through a literature review, a case study comparing a vulnerable and a fixed contract, and evaluation of analysis tools (Mythril, Slither, Securify, Sereum, BlockWatchdog).The expected results include identifying effective coding patterns (like checks-effects-interactions and reentrancy guards), assessing tool accuracy, and providing secure development guidelines. All experiments will be conducted safely on test networks.
Hanouf Al Ghanmi, Sabreen Ahmadjee, Rami Bahsoon, Hayatullahi Bolaji Adeyemo
Blockchain smart contract technology has revolutionised various industries by automating agreements through immutable and self-executing logic, reducing reliance on third-party intermediaries. However, despite its transformative potential, existing research has predominantly focused on technical aspects—particularly security—while largely neglecting human-in-the-loop concerns. Systematic efforts to explore these concerns from a human perspective have been limited which creates a gap in the literature. This study aims to address this gap by offering a comprehensive understanding of smart contracts from a human-centred perspective. To achieve this, we conducted a systematic literature review to examine human-related issues in smart contracts and their existing solutions. We found that concerns are primarily concentrated in two stages: development and interaction. During the development stage, issues arise in relation to programming languages, including complexity, readability and expressiveness, as well as the legality of smart contracts and their ethical and social implications. In the interaction stage, concerns focus on usability, human readability, trust, governance and cost. Additionally, we identified several quality attributes frequently associated with these concerns such as transparency, accountability, understandability, simplicity, learnability, compliance and fairness. We also uncovered new human-centred quality attributes that are overlooked in existing literature, such as explainability and interpretability. This research offers valuable insights for researchers, requirements engineers and designers by examining existing efforts to address human-centric concerns and proposing future directions and opportunities to improve smart contract design.
This research investigates the dynamics of connectedness among cryptocurrency and various risk factors, including oil price demand and supply shocks, EPU, GPR, and the ADS business conditions index using the quantile time-frequency connectedness approach. The findings reveal that cryptocurrency behaves as a net receiver of shocks in the short term but transitions to a net transmitter over the long term. Critical sources of both short- and long-term shocks are attributed to oil price demand, supply fluctuations, and GPR. However, during extreme events like the COVID−19 pandemic and the Russia-Ukraine war, cryptocurrency, oil shocks, and other indices alternately become net transmitters and receivers of shocks depending on time frames and quantile ranges. During periods of heightened market uncertainty, monitoring the interconnected behavior of these variables is critical for investors and policymakers aiming to predict market shifts and manage risks effectively.
To overcome the structural limitations of traditional hydroponic systems—inefficient input management, lack of verifiable traceability, high energy consumption, and absence of adaptive optimization—this paper presents an innovative architecture that synergistically integrates Distributed Ledger Technology (DLT), Internet of Things (IoT), and Artificial Intelligence (AI) to optimize resource management in controlled hydroponic environments. The proposed architecture constitutes a hybrid DTL, IoT and IA system founded on six principles: radical distribution of trust, defense in depth, verifiable trust through cryptographic proofs, modularity, native interoperability, and scalability. It comprises a distributed intelligent sensor network, a low-cost edge computing cluster, optimized artificial intelligence modules, and a DLT infrastructure based on Hyperledger Fabric with Raft consensus. Experimental results, obtained through system simulation on a 100 m² greenhouse and validated by partial prototyping, demonstrate robust operational performance: average latency of 847 ms from sensor to blockchain, throughput of 150 transactions per second, availability of 99.7%, support for 500 simultaneous sensors, and energy autonomy of 14 months. AI models achieve 96.3% accuracy in nutritional prediction, with pH prediction error of 0.08 units and EC error of 15 µS/cm. DDPG orchestration converges after 45 days with stabilization of the reward function. Comparative analysis reveals significant advantages: 18% yield increase, 15% reduction in input costs, 22% decrease in energy consumption during peak pricing periods, and 40% improvement in total cost of ownership over 5 years.
Daria Schumm, Gabriel Stegmaier, Cedric von Rauscher, Katharina Müller · 5 authors
Blockchains raise new privacy challenges, especially in Decentralized Identity (DI) and Self-Sovereign Identity (SSI) systems. Zero Knowledge Proofs (ZKPs) offer privacy, but only allow binary verification. Homomorphic Encryption (HE) enables flexible operations on encrypted data (e.g., addition, multiplication) but lacks comparison support. This paper addresses this gap by introducing a privacy-preserving comparison operation within HE, presenting the first comprehensive comparison of ZKP and HE as privacy-preserving mechanisms.
Background. The rapid advancement of digital technologies has introduced blockchain as a potential tool in public procurement contracts within the public sector. Smart contracts, particularly within civil law frameworks, have gained legislative recognition in jurisdictions such as France and several U.S. states. This development raises important questions about integrating blockchain-based smart contracts into governmental procurement systems, with a view to enhancing procedural transparency and operational efficiency, while acknowledging the limitations and dependencies on institutional frameworks. The central issue lies in clarifying the legal and technical implications of blockchain-based smart procurement contracts. The research examines their potential to streamline public procurement management and improve procedural efficiency, while recognising the need for legal safeguards that maintain administrative law principles and accommodate institutional constraints. Methods. This study adopts a comparative analytical approach, examining relevant legal provisions, technical requirements, and administrative practices across multiple jurisdictions. Various blockchain models—public, private, hybrid, and consortium—are evaluated for their suitability in procurement processes. Legislative experiences regulating smart contracts are analysed to extract best practices and inform a cautious framework for public sector adoption. Results and Conclusions. The analysis indicates that blockchain-based smart procurement contracts may reduce bureaucratic delays and minimise human errors, while providing immutable records that can support accountability. However, successful implementation requires legal and institutional adjustments to address enforceability, liability allocation, interoperability, and data protection. A practical model illustrating each operational step—from drafting to automated execution—is proposed, emphasising feasibility and legal compliance rather than assuming transformative effects. The study highlights the necessity of tailored legislation, standardised protocols, and targeted training for public officials to support the cautious integration of blockchain in public procurement contracting. These measures aim to guide the legally informed and context-sensitive adoption of smart contracts, contributing to sustainable digital transformation in public sector governance.
Miaoer Li, Yi Zhu, Yali Liu, Zexin Li · 5 authors
Smart contracts are self-executing programs on blockchains, critical for enabling efficient, secure, and reliable data exchange and value transfer. However, as their application scenarios expand, reliability issues have become a major bottleneck for blockchain development. Existing vulnerability detection methods often model smart contract source code as graph structures and use Graph Neural Networks (GNNs) for feature learning. Yet these methods over-rely on static execution flow features and ignore dynamic behavioral information of contract accounts in real runtime environments, limiting their ability to capture dynamic patterns and semantic details of contracts. To address these challenges, this paper proposes a graph attention network vulnerability detection model with global feature augmentation for smart contracts (GaGAT). Specifically, we first model key functions and variables in the contract source code as nodes, and execution flows as edges to construct a base contract graph. Then, we innovatively introduce global virtual nodes that integrate two types of information: contract categories and contract account behavioral features, including balance changes, Ether inflow/outflow, daily transaction frequency and single transaction duration. After contract graph contraction and feature transformation, we generate a feature matrix as input to the GaGAT model. Subsequently, we conduct vulnerability detection. Through a series of experiments, we provide empirical evidence of the superior performance of our proposed method compared to existing approaches in detecting six different categories of vulnerabilities.This study provides a new paradigm for cross-modal feature fusion for smart contract security analysis.
Cryptocurrency investment in India has quickly become a mainstream financial activity, but it is still highly prone to psychological factors that impact the decision-making of retail investors. This study examines the effect of personality traits on cryptocurrency investment behavior using the mediating variable of behavioral biases. Based on the Big Five Personality Model and the theory of Behavioral Finance, data were gathered from 716 Indian retail investors using a structured questionnaire. Partial Least Squares Structural Equation Modeling (PLS-SEM) was conducted to analyze the relationships among the variables. Results show that Openness to experience and Agreeableness significantly predict Availability Bias, whereas Extraversion and Agreeableness affect the Disposition Effect. The theoretical framework shows how bias-driven investment behavior in volatile markets such as cryptocurrency is triggered by personality-based predispositions. The study adds to the behavioral finance literature by taking psychological profiling outside the realms of traditional investment contexts into digital asset investing and provides practical insights for regulators, fintech platforms, and investment advisors to design interventions to mitigate bias and enhance investor education.
This presentation will demonstrate using WebAssembly for audio plugins outside the web browser. Specifically, the showcase is a WebAssembly binary used as a synthesizer plugin in a commercial, desktop based Digital Audio Workstation software such as Garage Band or Logic. In addition there will be a proposal and demonstration of a commercial model for distributing and controlling access and ownership to these WebAssembly audio plugins, by storing them on the NEAR blockchain and binding them to Non-Fungible-Tokens ( NFTs ).
R. Li, Srisht Fateh Singh, Andreas Park, Andreas Veneris
This paper presents a securities tokenization solution that brings the accessibility, transparency, efficiency, and innovation of blockchain and decentralized finance to real-world securities. Tokenization in principle seems straightforward—an intermediary holds assets and issues 1:1 tokens—but decentralized finance applications (DeFi) introduce significant complications. Even basic DeFi mechanisms, such as liquidity pools, pose challenges for tokenizing stocks and bonds because when assets are pooled in smart contracts, ownership becomes unclear, hindering asset owners to access their entitlements, such as dividends, coupons, or voting rights. Existing solutions often fail to address these challenges and are typically limited to specific security types. Our solution, by contrast, generalizes to any security and any holding rights through fungible tokens and using separate smart contracts for shareholders to redeem their entitlements. To address the decentralized ownership issue, our solution employs off-chain accounting with additional logic for liquidity pools. We implement this on Ethereum, demonstrating that it is 27% cheaper in gas costs than current alternatives. We also analyze the liquidity logic of over 90% of Ethereum's liquidity pools, confirming compatibility with our solution. Finally, we demonstrate its use for dividend-paying stocks, common stock, mergers, and coupon-paying bonds.
Markus Jungnickel, Ferda Özdemir Sönmez, Catherine Mulligan, William J. Knottenbelt
Decentralized autonomous organizations (DAOs) have emerged as a novel organizational structure, attracting growing interest due to their decentralized, transparent governance, which replaces traditional hierarchies with stakeholder-managed rules codified as smart contracts. Although various governance models exist, comparative research across dimensions remains limited, leaving the literature fragmented and offering little practical guidance for selecting suitable models. This article critically analyses existing governance mechanisms and their implementation to support the development of more effective DAO models. To address current gaps, we review prior quantitative studies and conduct exploratory data analysis on centralization, participation, and decision controversy. The findings show that reputation and share-based models can mitigate the centralization seen in token-based systems, though all models suffer from low member engagement, suggesting an over reliance on direct democracy. Our analysis can be replicated across platforms and time frames to refine and validate these insights.
Christian Zeiß, Lisa Straub, Maximilian Greiner, Marcel Neis · 7 authors
Purpose To promote acceptance of blockchain-based investment options and enhance confidence for new investors, the market must become more comprehensible and accessible to the broad masses. This requires transparency to build trust in web-based intermediaries, particularly given the multitude of websites that often advertise unrealistic returns in the crypto sector. Consequently, intermediaries within the decentralized finance ecosystem need to be clearly identified and categorized to facilitate mass-market adoption. Design/methodology/approach We employ a six-iteration taxonomy approach, establishing a data foundation through literature reviews, expert interviews and document analysis of 50 intermediaries. Archetypes are derived using a hierarchical clustering algorithm. Finally, a survey is conducted to evaluate the taxonomy and the archetypes. Findings The taxonomy encompasses three meta-characteristics (functionality, architecture, security) and 63 characteristics. Furthermore, the research findings reveal six archetypes of blockchain-based investment intermediaries, demonstrating significant discrepancies between them, particularly in terms of financial features and governance structures. Given the complexity of crypto intermediary platforms for novice users, the findings underscore the need to implement technology-based and institutional-based trust mechanisms, improve risk assessment and enable informed decision-making. Originality/value By increasing market transparency and fostering trust, this study contributes to the acceptance and adoption of blockchain-based financial intermediaries, drawing on the diffusion of innovation theory. The proposed taxonomy, particularly its dimensions, specifically addresses the requirements of both technology-based and institution-based trust, which are critical for crypto investments. Moreover, the findings emphasize the importance of educational resources and communicated trust features in strengthening user confidence and facilitating broader market participation.