Carbon credit systems have emerged as a policy tool to incentivize emission reductions and support the transition to clean energy. Reliable carbon-credit certification depends on mechanisms that connect actual, measured renewable-energy production to verifiable emission-reduction records. Although blockchain and IoT technologies have been applied to emission monitoring and trading, existing work offers limited support for certification processes, particularly for small and medium-scale renewable installations. This paper introduces a blockchain-based carbon-credit certification architecture, demonstrated through a 100 kWp photovoltaic case study, that integrates real-time IoT data collection, edge-level aggregation, and secure on-chain storage on a permissioned blockchain with smart contracts. Unlike approaches focused on trading mechanisms, the proposed system aligns with European legislation and voluntary carbon-market standards, clarifying the practical requirements and constraints that apply to photovoltaic operators. The resulting architecture provides a structured pathway for generating verifiable carbon-credit records and supporting third-party verification.
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.
Blockchain technology offers decentralization and security but struggles with scalability, particularly in enterprise settings where efficiency and controlled access are paramount. Sharding is a promising solution for private blockchains, yet existing approaches face challenges in coordinating shards, ensuring fault tolerance with limited nodes, and minimizing the high overhead of consensus mechanisms like PBFT. This paper proposes the Range-Based Sharding (RBS) Protocol, a novel sharding mechanism tailored for enterprise blockchains, implemented on Quorum. Unlike traditional sharding models such as OmniLedger and non-sharding Corda framework, RBS employs a commit-reveal scheme for secure and unbiased shard allocation, ensuring fair validator distribution while reducing cross-shard transaction delays. Our approach enhances scalability by balancing computational loads across shards, reducing consensus overhead, and improving parallel transaction execution. Experimental evaluations demonstrate that RBS achieves significantly higher throughput and lower latency compared to existing enterprise sharding frameworks, making it a viable and efficient solution for largescale blockchain deployments.
Dorottya Zelenyanszki, Zhe Hou, Kamanashis Biswas
et al.
Decentralised applications (dApps) that run on public blockchains have the benefit of trustworthiness and transparency as every activity that happens on the blockchain can be publicly traced through the transaction data. However, this introduces a potential privacy problem as this data can be tracked and analysed, which can reveal user-behaviour information. A user behaviour analysis pipeline was proposed to present how this type of information can be extracted and analysed to identify separate behavioural clusters that can describe how users behave in the game. The pipeline starts with the collection of transaction data, involving smart contracts, that is collected from a blockchain-based game called Planet IX. Both the raw transaction information and the transaction events are considered in the data collection. From this data, separate game actions can be formed and those are leveraged to present how and when the users conducted their in-game activities in the form of user flows. An extended version of these user flows also presents how the Non-Fungible Tokens (NFTs) are being leveraged in the user actions. The latter is given as input for a Graph Neural Network (GNN) model to provide graph embeddings for these flows which then can be leveraged by clustering algorithms to cluster user behaviours into separate behavioural clusters. We benchmark and compare well-known clustering algorithms as a part of the proposed method. The user behaviour clusters were analysed and visualised in a graph format. It was found that behavioural information can be extracted regarding the users that belong to these clusters. Such information can be exploited by malicious users to their advantage. To demonstrate this, a privacy threat model was also presented based on the results that correspond to multiple potentially affected areas.
Anton Ivashkevich, Matija Piškorec, Claudio J. Tessone
We describe a prototype of a fully capable Ethereum Proof-of-Work (PoW) blockchain network running on multiple Raspberry Pi (RPi) computers. The prototype is easy to set up and is intended to function as a completely standalone system, using a local WiFi router for connectivity. It features LCD screens for visualization of the local state of blockchain ledgers on each RPi, making it ideal for educational purposes and to demonstrate fundamental blockchain concepts to a wide audience. For example, a functioning PoW consensus is easily visible from the LCD screens, as well as consensus degradation which might arise from various factors, including peer-to-peer topology and communication latency - all parameters which can be configured from the central web-based interface.
Joel Poncha Lemayian, Ghyslain Gagnon, Kaiwen Zhang
et al.
Cryptocurrency blockchain networks safeguard digital assets using cryptographic keys, with wallets playing a critical role in generating, storing, and managing these keys. Wallets, typically categorized as hot and cold, offer varying degrees of security and convenience. However, they are generally software-based applications running on microcontrollers. Consequently, they are vulnerable to malware and side-channel attacks, allowing perpetrators to extract private keys by targeting critical algorithms, such as ECC, which processes private keys to generate public keys and authorize transactions. To address these issues, this work presents EthVault, the first hardware architecture for an Ethereum hierarchically deterministic cold wallet, featuring hardware implementations of key algorithms for secure key generation. Also, an ECC architecture resilient to side-channel and timing attacks is proposed. Moreover, an architecture of the child key derivation function, a fundamental component of cryptocurrency wallets, is proposed. The design minimizes resource usage, meeting market demand for small, portable cryptocurrency wallets. FPGA implementation results validate the feasibility of the proposed approach. The ECC architecture exhibits uniform execution behavior across varying inputs, while the complete design utilizes only 27%, 7%, and 6% of LUTs, registers, and RAM blocks, respectively, on a Xilinx Zynq UltraScale+ FPGA
Rowdy Chotkan, Bulat Nasrulin, Jérémie Decouchant
et al.
Spam poses a growing threat to blockchain networks. Adversaries can easily create multiple accounts to flood transaction pools, inflating fees and degrading service quality. Existing defenses against spam, such as fee markets and staking requirements, primarily rely on economic deterrence, which fails to distinguish between malicious and legitimate users and often exclude low-value but honest activity. To address these shortcomings, we present StarveSpam, a decentralized reputation-based protocol that mitigates spam by operating at the transaction relay layer. StarveSpam combines local behavior tracking, peer scoring, and adaptive rate-limiting to suppress abusive actors, without requiring global consensus, protocol changes, or trusted infrastructure. We evaluate StarveSpam using real Ethereum data from a major NFT spam event and show that it outperforms existing fee-based and rule-based defenses, allowing each node to block over 95% of spam while dropping just 3% of honest traffic, and reducing the fraction of the network exposed to spam by 85% compared to existing rule-based methods. StarveSpam offers a scalable and deployable alternative to traditional spam defenses, paving the way toward more resilient and equitable blockchain infrastructure.
Blockchain Business applications and cryptocurrencies such as enable secure, decentralized value transfer, yet their pseudonymous nature creates opportunities for illicit activity, challenging regulators and exchanges in anti money laundering (AML) enforcement. Detecting fraudulent transactions in blockchain networks requires models that can capture both structural and temporal dependencies while remaining resilient to noise, imbalance, and adversarial behavior. In this work, we propose an ensemble framework that integrates Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph Isomorphism Networks (GIN) to enhance blockchain fraud detection. Using the real-world Elliptic dataset, our tuned soft voting ensemble achieves high recall of illicit transactions while maintaining a false positive rate below 1%, beating individual GNN models and baseline methods. The modular architecture incorporates quantum-ready design hooks, allowing seamless future integration of quantum feature mappings and hybrid quantum classical graph neural networks. This ensures scalability, robustness, and long-term adaptability as quantum computing technologies mature. Our findings highlight ensemble GNNs as a practical and forward-looking solution for real-time cryptocurrency monitoring, providing both immediate AML utility and a pathway toward quantum-enhanced financial security analytics.
Jhon Wilder Zartha Sossa, Adriana Maria Zuluaga Monsalve, Nolberto Gutiérrez Posada
et al.
The objective of this article is to identify and prioritize technologies, innovations and new businesses related to the dairy agro-industrial chain that are expected to emerge by 2035. To do so, the two-round Delphi method was used and questionnaires were applied to 27 national and international experts. A technology tree was built with Python codes and libraries, consisting of 174 topics. Additionally, 39 variables were generated for scenarios in the Good Livestock Practices BPG; Research, Development and Innovation R&D&I; Sustainable Livestock and Agroindustry groups, as well as four hypotheses and a bet scenario, with the future objectives of sustainable specialization of forage production and mass production and standardization in collection centers. This can be achieved through projects on technologies and innovations prioritized in the Delphi method, including ultrasound, pulsed combustion drying, dairy-derived medicinal products, bioethanol produced from whey, artificial intelligence and selection assisted by molecular markers, electromembrane filtration technologies, whey protein concentrates, life cycle assessment, blockchain, neural networks and smart assays, among others. The opportunity that actors in the Science, Technology and Innovation system have in the chain for the development of programs, plans, public policies and open innovation challenges in the prioritized technologies is highlighted.
Nutrition. Foods and food supply, Food processing and manufacture
Blockchain technology empowers decentralized transactions in smart grids, but existing consensus algorithms face efficiency and security bottlenecks under Byzantine attacks. This article proposes the RVR consensus algorithm, which innovatively integrates dynamic reputation evaluation, verifiable random function (VRF), and a weight-driven probability election mechanism to achieve (1) behavior-aware dynamic adjustment of reputation weights and (2) manipulation-resistant random leader election via VRF. Experimental verification shows that under a silence attack, the maximum latency is reduced by 37.88% compared to HotStuff, and under a forking attack, the maximum throughput is increased by 50.66%, providing an efficient and secure new paradigm for distributed energy trading.
Alessio Ferone, Antonio Maratea, Francesco Camastra
et al.
The primary purpose of video surveillance is to monitor public indoor areas or the boundaries of secure facilities to safeguard them against theft, unauthorized access, fire, and various other potential threats. Security cameras, equipped with integrated video surveillance systems, are strategically placed throughout critical locations on the premises, allowing security personnel to observe all areas for specific behaviors that may signal an emergency or a situation requiring intervention. A significant challenge arises from the fact that individuals cannot maintain focus on multiple screens simultaneously, which can result in the oversight of crucial incidents. In this regard, artificial intelligence (AI) video analytics has become increasingly prominent, driven by numerous practical applications that include object identification, detection of unusual behavior patterns, facial recognition, and traffic management. Recent advancements in this technology have led to enhanced functionality, remarkable accuracy, and reduced costs for consumers. There is a noticeable trend towards upgrading security frameworks by incorporating AI into pre-existing video surveillance systems, thus leading to modern video surveillance that leverages video analytics, enabling the detection and reporting of anomalies within mere seconds, thereby transforming it into a proactive security solution. In this context, the AiWatch system introduces digital twin (DT) technology in a modern video surveillance architecture to facilitate advanced analytics through the aggregation of data from various sources. By exploiting AI and DT to analyze the different sources, it is possible to derive deeper insights applicable at higher decision levels. This approach allows for the evaluation of the effects and outcomes of actions by examining different scenarios, hence yielding more robust decisions.
The Industrial Revolution 4.0 (IR4.0) introduces transformative technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and Blockchain, offering small and medium enterprises (SMEs) opportunities to revolutionize operations and competitiveness. However, existing IR4.0 readiness assessment tools are manufacturing-centric, time-consuming, and lack adaptability across sectors. This study aims to develop a novel, web-based self-assessment tool that enables Malaysian SMEs across both manufacturing and non-manufacturing sectors to evaluate their IR4.0 readiness independently and efficiently. To date, no such self-evaluation system exists in Malaysia that empowers SMEs to independently assess their IR4.0 readiness without the need for third-party audits. Unlike prior tools, this system integrates recommendations for training and funding, enhancing both usability and actionability. A three-phase methodology was applied. The initial phase involved a systematic literature review of 10,428 articles, filtered to 13 high-quality studies, and qualitative content analysis, which identified 23 readiness items within five dimensions: Organizational, Data, Infrastructure, Analytics, and IT, Development, and Operations. Field observations from IR4.0 programs further contextualized these factors. The second phase focused on model design and tool development, incorporating expert-weighted scoring through a Delphi process and automated recommendations for national support programs. In the final phase, the tool was tested with 52 SMEs and validated through interviews with Malaysian industry experts. Findings show an average readiness of 69 %, with gaps in infrastructure, analytics, and organizational commitment. The study concludes by offering an empirically grounded, sector-neutral tool that benchmarks SME digital readiness and informs both enterprise strategies and policymaking.
This study aims to investigate the interoperability of the Bitcoin blockchain by comparing the US dollar prices of five cryptocurrencies derived from the Bitcoin price with their corresponding market prices. The deviation rate between the derived price and the market price, referred to as the arbitrage return rate, is examined with respect to its adherence to the efficient market hypothesis and martingale theory principles, specifically regarding mean-reversion and serial independence. Hurst exponents are estimated using R/S and DFA methods, and their dynamics are analyzed using a sliding window technique. Our findings demonstrate that the Bitcoin blockchain effectively facilitates transactions among the five cryptocurrencies, though evidence suggests a potential structural change in Bitcoin blockchain interoperability following April 2023.
Nowadays, federated recommendation technology is rapidly evolving to help multiple organisations share data and train models while meeting user privacy, data security and government regulatory requirements. However, federated recommendation increases customer system costs such as power, computational and communication resources. Besides, federated recommendation systems are also susceptible to model attacks and data poisoning by participating malicious clients. Therefore, most customers are unwilling to participate in federated recommendation without any incentive. To address these problems, we propose a blockchain-based federated recommendation system with incentive mechanism to promote more trustworthy, secure, and efficient federated recommendation service. First, we construct a federated recommendation system based on NeuMF and FedAvg. Then we introduce a reverse auction mechanism to select optimal clients that can maximize the social surplus. Finally, we employ blockchain for on-chain evidence storage of models to ensure the safety of the federated recommendation system. The experimental results show that our proposed incentive mechanism can attract clients with superior training data to engage in the federal recommendation at a lower cost, which can increase the economic benefit of federal recommendation by 54.9\% while improve the recommendation performance. Thus our work provides theoretical and technological support for the construction of a harmonious and healthy ecological environment for the application of federal recommendation.
The paradigm of Internet of Things (IoT) is paving the way for a world, where many of our daily objects will be interconnected and will interact with their environment in order to collect information and automate certain tasks. Such a vision requires, among other things, seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self-maintenance. Such features can be brought by blockchain, a technology born with a cryptocurrency called Bitcoin. In this paper, a thorough review on how to adapt blockchain to the specific needs of IoT in order to develop Blockchain-based IoT (BIoT) applications is presented. After describing the basics of blockchain, the most relevant BIoT applications are described with the objective of emphasizing how blockchain can impact traditional cloud-centered IoT applications. Then, the current challenges and possible optimizations are detailed regarding many aspects that affect the design, development, and deployment of a BIoT application. Finally, some recommendations are enumerated with the aim of guiding future BIoT researchers and developers on some of the issues that will have to be tackled before deploying the next generation of BIoT applications.
The ongoing regulation of blockchain-based services and applications requires the identification of users who are issuing transactions on the blockchain. This systematic review explores the current status, identifies research gaps, and outlines future research directions for establishing trusted and privacy-compliant identities on the blockchain (on-chain identity). A systematic search term was applied across various scientific databases, collecting 2232 potentially relevant research papers. These papers were narrowed down in two methodologically executed steps to 98 and finally to 13 relevant sources. The relevant articles were then systematically analyzed based on a set of screening questions. The results of the selected studies have provided insightful findings on the mechanisms of on-chain identities. On-chain identities are established using zero-knowledge proofs, public key infrastructure/certificates, and web of trust approaches. The technologies and architectures used by the authors are also highlighted. Trust has emerged as a key research gap, manifesting in two ways: firstly, a gap in how to trust the digital identity representation of a physical human; secondly, a gap in how to trust identity providers that issue identity confirmations on-chain. Potential future research avenues are suggested to help fill the current gaps in establishing trust and on-chain identities.
One of the biggest challenges of building artificial intelligence (AI) model in the healthcare area is the data sharing. Since healthcare data is private, sensitive, and heterogeneous, collecting sufficient data for modelling is exhausting, costly, and sometimes impossible. In this paper, we propose a framework for global healthcare modelling using datasets from multi-continents (Europe, North America, and Asia) without sharing the local datasets, and choose glucose management as a study model to verify its effectiveness. Technically, blockchain-enabled federated learning is implemented with adaptation to meet the privacy and safety requirements of healthcare data, meanwhile, it rewards honest participation and penalizes malicious activities using its on-chain incentive mechanism. Experimental results show that the proposed framework is effective, efficient, and privacy-preserving. Its prediction accuracy consistently outperforms models trained on limited personal data and achieves comparable or even slightly better results than centralized training in certain scenarios, all while preserving data privacy. This work paves the way for international collaborations on healthcare projects, where additional data is crucial for reducing bias and providing benefits to humanity.
El uso de los criptoactivos ha experimentado un notorio aumento en los últimos años, introduciendo consigo una serie de conceptos novedosos en la economía española. Este fenómeno ha permitido a los usuarios operar de nuevas formas, lo que entraña una serie de ventajas y riesgos inherentes que deberían conocer. Las ventajas asociadas a estos activos financieros han supuesto un reclamo que ha hecho que cada vez más individuos hagan uso de los mismos. Esta atracción se ha traducido en una mayor presencia de los criptoactivos en las investigaciones policiales, utilizados como medio de pago, promocionados como inversiones con rendimientos rápidos e incluso utilizados en operativas de blanqueo de capitales procedentes de todo tipo de delitos. La versatilidad en su utilización y su cada vez más marcada presencia en la sociedad plantea desafíos significativos para las autoridades, que deben, sin limitar las oportunidades legítimas que los criptoactivos pueden ofrecer, adaptar su legislación para salvaguardar a la población frente a los posibles riesgos asociados a los criptoactivos y fomentar su uso responsable y seguro. En este sentido, las Fuerzas y Cuerpos de Seguridad están en la obligación de proteger a los ciudadanos en este nuevo ámbito virtual que se presenta.
Social pathology. Social and public welfare. Criminology, Criminal law and procedure