In this paper, we design, implement, and empirically evaluate a tamper-evident, blockchain-secured solar energy logging system for resource-constrained edge Internet of Things (IoT) devices. Using a Merkle tree batching approach in conjunction with threshold-triggered blockchain anchoring, the system combines high-frequency local logging with energy-efficient, cryptographically verifiable submissions to the Ethereum Sepolia testnet, a public Proof-of-Stake (PoS) blockchain. The logger captured and hashed cryptographic chains on a minute-by-minute basis during a continuous 135 h deployment on a Raspberry Pi equipped with an INA219 sensor. Thanks to effective retrial and daily rollover mechanisms, it committed 130 verified Merkle batches to the blockchain without any data loss or unverifiable records, even during internet outages. The system offers robust end-to-end auditability and tamper resistance with low operational and carbon overhead, which was tested with comparative benchmarking against other blockchain logging models and conventional local and cloud-based loggers. The findings illustrate the technical and sustainability feasibility of digital audit trails based on blockchain technology for distributed solar energy systems. These audit trails facilitate scalable environmental, social, and governance (ESG) reporting, automated renewable energy certification, and transparent carbon accounting.
Arash Peik, Mohammad Ali Zare Chahooki, Amin Milani Fard, Mehdi Agha Sarram
Precise short-term price prediction in the highly volatile cryptocurrency market is critical for informed trading strategies. Although Temporal Fusion Transformers (TFTs) have shown potential, their direct use often struggles in the face of the market's non-stationary nature and extreme volatility. This paper introduces an adaptive TFT modeling approach leveraging dynamic subseries lengths and pattern-based categorization to enhance short-term forecasting. We propose a novel segmentation method where subseries end at relative maxima, identified when the price increase from the preceding minimum surpasses a threshold, thus capturing significant upward movements, which act as key markers for the end of a growth phase, while potentially filtering the noise. Crucially, the fixed-length pattern ending each subseries determines the category assigned to the subsequent variable-length subseries, grouping typical market responses that follow similar preceding conditions. A distinct TFT model trained for each category is specialized in predicting the evolution of these subsequent subseries based on their initial steps after the preceding peak. Experimental results on ETH-USDT 10-minute data over a two-month test period demonstrate that our adaptive approach significantly outperforms baseline fixed-length TFT and LSTM models in prediction accuracy and simulated trading profitability. Our combination of adaptive segmentation and pattern-conditioned forecasting enables more robust and responsive cryptocurrency price prediction.
We introduce a novel rough Bergomi (rBergomi) model featuring a variance-driven exponentially weighted moving average (EWMA) time-dependent Hurst parameter $H_t$, fundamentally distinct from recent machine learning and wavelet-based approaches in the literature. Our framework pioneers a unified rough differential equation (RDE) formulation grounded in rough path theory, where the Hurst parameter dynamically adapts to evolving volatility regimes through a continuous EWMA mechanism tied to instantaneous variance. Unlike discrete model-switching or computationally intensive forecasting methods, our approach provides mathematical tractability while capturing volatility clustering and roughness bursts. We rigorously establish existence and uniqueness of solutions via rough path theory and derive martingale properties. Empirical validation on diverse asset classes including equities, cryptocurrencies, and commodities demonstrates superior performance in capturing dynamics and out-of-sample pricing accuracy. Our results show significant improvements over traditional constant-Hurst models.
Traditional security models for Nakamoto-style blockchains assume instantaneous synchronization among malicious nodes, which overestimate adversarial coordination capability. We revisit these existing models and propose two more realistic security models. First, we propose the static delay model. This model first incorporates adversarial communication delay. It quantifies how the delay constrains the effective growth rate of private chains and yields a closed-form expression for the security threshold. Second, we propose the dynamic delay model that further captures the decay of adversarial corruption capability and the total adversarial delay window. Theoretical analysis shows that private attacks remain optimal under both models. Finally, we prove that large-scale Nakamoto-style blockchains offer better security. This result provided a theoretical foundation for optimizing consensus protocols and assessing the robustness of large-scale blockchains.
Mehmed Oğuz Şen, Fatıh Okumuş, Adnan Fatıh Kocamaz
Decentralized task allocation is a critical challenge in multi-robot systems, particularly in scenarios where autonomy, scalability, and robustness are essential. While centralized approaches simplify coordination, they suffer from limitations such as single points of failure and poor scalability in dynamic environments. This paper presents a comparative evaluation of three decentralized and distributed task allocation algorithms integrated into a blockchain-powered multi-robot system where Hyperledger Fabric is used as blockchain platform. Each algorithm employs a cost-based selection mechanism to assign tasks autonomously while leveraging a distributed ledger for data consistency and conflict resolution. The algorithms -Euclidean distance, TEB motion planner and every robot computing all robots' costs- are evaluated for a system of three TIAGo++ robots in two different simulation environments. Performance metrics include computational overhead and task request conflict rates. Results show that while Euclidean distance offers the lowest overhead, it suffers from high conflict rates; TEB motion planner improves fairness with moderate overhead; and every robot computing all robots' costs ensures the highest consistency at the cost of increased computation. The findings highlight key trade-offs in decentralized coordination and offer guidance for designing scalable and reliable blockchain-powered multi robot systems.
Ali M. Emran, Md Kamrul Islam -, Md Ashraful Islam Nayem -, Md Rubel · 5 authors
Abstract: Exploring GNNs as a cutting-edge approach to real-time detection of online money transfer fraud is the focus of this work. P2P payment systems, mobile money platforms, and decentralized financial infrastructures (DeFi) have all experienced explosive growth over the past decade due to their simplicity, speed, and affordability. Identity fraud, synthetic account misuse, coordinated fraud rings that exploit systemic vulnerabilities, and transaction laundering are some of the new types of fraud that can occur in these platforms, despite their desirability. In situations where fraud is predictable, isolated, and statistically distinct, logistic regression, rule-based algorithms, and standard ML models like Random Forests and SVMs have all proved effective in detecting it. Modern, hyper-connected, real-time financial ecosystems are seeing an uptick in non-linear, relational, and temporal fraud patterns, which these tactics struggle to combat. Because of their inherent bias, they fail to recognize the interconnected structural and relational processes that may point to coordinated fraud. The graph-like qualities of monetary exchanges, where elements (like IP addresses, users, and devices) are organically linked through edges that stand for transactions or relationships, are utilized by Graph Neural Networks to give a paradigm shift, on the other hand. Generalized neural networks (GNNs) are crucial for uncovering intricate fraud schemes because they represent these interactions as a graph structure that permits data to travel and accumulate across nodes. Because of this, the model may take global and regional effects into consideration. Relational learning excels when other methods fail, such as when trying to detect suspicious clusters of transactions, multi-hop collusions, or fraudulent subnetworks using separate features. In order to implement this method, we constructed an entirely new fraud detection system utilizing GNNs. Node feature engineering, graph generation, classification heads, message-passing layers, and a real-time processing optimized pipeline are all parts of it. We were able to empirically evaluate our technique using a real-world transactional dataset that was acquired from a leading financial services provider. As is typical in fraud detection tasks, the dataset had a highly skewed class distribution, which impacted both memory and accuracy. With an F1-score of 0.78, accuracy of 98.7 percent, precision of 0.81%, and recall of 0.76%, the model nevertheless performed admirably. The model's ability to detect fraudulent behaviors while maintaining dependable operations in the real world is demonstrated by these measures. Beyond its implications for technological performance, this study will help achieve broader aims in regulation, ethics, and national security. A number of federal agencies have issued advisories highlighting the need for strong, intelligent, and real-time fraud monitoring systems to safeguard national financial systems from fraudulent exploitation. These agencies include the DOJ, FinCEN, and DHS. Compliance with the USA PATRIOT Act and the Bank Secrecy Act (BSA) is of the utmost importance to financial institutions and fintech enterprises. As stated in the National Strategy to Combat Terrorist and Other Illicit Financing, they also want AI-driven surveillance systems to be resilient and explainable. This national goal is helped by our study, which provides a scalable, interpretable, and performance-driven GNN-based system. Along with helping with auditability, model explainability, and compliance reporting, all of which are crucial for regulated businesses, this strategy also helps with effective fraud detection. Integrating our suggested architecture for decentralized, privacy-preserving fraud detection into online learning extensions can further improve their functionality. Over time, these extensions can be integrated with federated learning systems and streaming data platforms. This work puts GNNs in a position to become a new weapon in the fight against digital payment fraud by combining cutting-edge graph representation learning with cybersecurity regulations and goals for financial integrity. Thanks to our research's careful analysis, innovative architecture, and adherence to statutory criteria, future financial systems will be reliable, safe, and robust. Additionally, it resolves a significant technical matter.
Vincent Duronio, Mohamed Firas Aguir, Hari Sai Kaja, Andrew J. Kalafut · 6 authors
The Internet of Health Things (IoHT) introduces new opportunities for remote patient monitoring but also exposes sensitive medical data to serious security risks. Traditional authentication mechanisms are often too resource-intensive for IoHT devices, which are constrained in terms of storage, computation, and energy. This paper presents a lightweight, decentralized authentication protocol tailored for IoHT environments, integrating blockchain technology, Physical Unclonable Functions (PUFs), and zero-knowledge proofs (zk-SNARKs). Our protocol employs the Proof of Elapsed Work and Luck (PoEWAL) consensus mechanism, enabling secure, energy-efficient participation in a private Hyperledger Fabric blockchain. The scheme ensures strong mutual authentication between healthcare providers and IoHT devices without exposing sensitive data. An informal analysis is done to demonstrate the protocol’s resilience against common attacks. This protocol offers a secure solution for enabling trustworthy healthcare communication in resource-limited IoHT networks.
Physical Unclonable Functions (PUFs) and Hardware Security
The convergence of artificial intelligence (AI), multi-access edge computing (MEC), and cloud environments has transformed identity governance by enabling real-time decision-making and seamless access control across decentralized infrastructures. However, this evolution has also introduced complex challenges concerning data privacy, identity trust, and security. This review explores privacy-first security models that integrate AI for identity governance in hybrid cloud-edge architectures. It evaluates privacy-preserving techniques such as homomorphic encryption, federated learning, and zero-knowledge proofs, emphasizing their role in ensuring secure identity authentication, authorization, and auditability. The paper critically analyzes the limitations of conventional identity and access management (IAM) frameworks in dynamic, resource-constrained edge environments and proposes adaptive models that embed privacy by design. Furthermore, the review investigates the interplay between explainable AI (XAI) and policy enforcement for transparent and compliant identity governance. By synthesizing advancements in cryptographic methods, AI reasoning engines, and decentralized identity (DID) systems, the paper outlines a roadmap for building secure, scalable, and privacy-compliant identity infrastructures in the era of pervasive computing. Keywords: Privacy-Preserving Identity Governance, AI-Driven Access Control, Multi-Access Edge Computing (MEC). Federated Identity Management, Explainable AI (XAI), Zero-Knowledge Proofs.
Teoman Berkay Ayaz, Muhammet Furkan Özara, Ahmet Erkan Çelik, Akhan Akbulut
Blockchain systems promote transparency, decentralization, and reliability. Nevertheless, they remain vulnerable to more sophisticated fraudulent actors, particularly on newly launched platforms with little or nonexistent transaction histories. This paper introduces an innovative hybrid learning model that integrates few-shot learning with active learning to address two fundamental issues in fraud detection within financial systems: the limited availability of annotated fraud data and the ongoing evolution of illegal behavior. The study provides a comprehensive evaluation of two complementary datasets: a public dataset comprising real-life transactions obtained from Kaggle to establish a generalizable benchmark (9,841 transactions, 22.1% fraudulent) and a custom synthetic dataset designed for the PointXchange platform (197,458 transactions, 0.16% fraudulent). The approach we use generates balanced training sets and minimizes annotation costs by sampling as few as 8 to 128 samples per class and iteratively querying an oracle for useful labels. Benchmarks conducted across four families of algorithms: gradient boosting machines (XGBoost, LightGBM, CatBoost), boosting (AdaBoost), ensemble learners (Random Forest, Extra Trees), and neural networks (MLP, XNet), demonstrate the effectiveness of the proposed approach with recall scores reaching up to 0.9906.
Stephen Kirkman, Richard E. Newman, Christopher Garcia
The Ethereum Trilemma states that security, scalability, and decentralization cannot all be achieved at the same time in a blockchain. We call this the ‘Ethereum’ Trilemma because there are solutions to generic blockchain scalability. Due to the added computation, Ethereum has certain challenges that so far cannot be solved without going to Layer 2 or completely changing the base chain. Layer 2 scaling techniques are add-ons to Layer 1 (the base chain). These scaling techniques, may, in some cases reduce the security of the blockchain - but it depends on the definition of security one uses. We examine the implications of Layer 2 scaling (particularly optimistic rollups since they are most popular) and note that decentralized computation and smart contract security have been relegated to a back seat at best or tossed out completely at worst - validation appears to be left behind. This is a gap in rollups that neither Optimistic Rollups nor ZK-Rollups fill. We propose to fill that gap with what we call an $\mathrm{n} \%$-Validations Rollup that bring some validation back - not fully decentralized, but not completely centralized either; the best of both worlds. In our vision, the user needs more options to specify their desired level of decentralized validation. Currently, if you use a rollup, you have potentially no Layer 1 validation. On the other extreme, too much validation is the scaling roadblock. This position paper suggests avenues for research of these issues. This is a transitional period for Ethereum and Layer 2 appears to have become the wild west.
Decentralized Finance (DeFi) attacks have resulted in significant losses, often orchestrated through Adversarial Exploiter Contracts (AECs) that exploit vulnerabilities in victim smart contracts. To proactively identify such threats, this paper targets the explainable detection of AECs. Existing detection methods struggle to capture semantic dependencies and lack interpretability, limiting their effectiveness and leaving critical knowledge gaps in AEC analysis. To address these challenges, we introduce SEASONED, an effective, self-explanatory, and robust framework for AEC detection. SEASONED extracts semantic information from contract bytecode to construct a semantic relation graph (SRG), and employs a self-counterfactual explainable detector (SCFED) to classify SRGs and generate explanations that highlight the core attack logic. SCFED further enhances robustness, generalizability, and data efficiency by extracting representative information from these explanations. Both theoretical analysis and experimental results demonstrate the effectiveness of SEASONED, which showcases outstanding detection performance, robustness, generalizability, and data efficiency learning ability. To support further research, we also release a new dataset of 359 AECs.
The incorporation of the Internet of Things (IoT) into daily life has brought about challenges related to security and privacy. Smart home security systems using physical locks connected to the internet continue to be susceptible to unrecognized key duplication and unauthorized entry. To overcome this challenge, this work proposes a decentralized smart lock system using the blockchain technology as a means of providing improved access control and security in smart homes. The proposed system integrates the coupling of an IoT device with a keypad entry and a Solidity-based smart contract logic on the IOTA Ethereum Virtual Machine (EVM) test network. A webbased frontend using Node.js enables homeowners to grant access permissions remotely, where Web3.py ensures safe blockchain interactions. The initiative eliminates the vulnerability that comes with physical keys, facilitates secure remote access control, and improves the safety using hashed access codes, time-limited procedures, and non-repudiation of transactions. Experimental results further show that the proposed IOTA-based smart lock system keeps the usage of resources lower compared to existing Ethereum-based solutions.
The energy sector faces inefficiencies, fraud, and lack of transparency, while the traditional peer-to-peer (P2P) energy trading market faces challenges of trust, interoperability, and flexibility. This paper proposes an innovative solution leveraging blockchain technology to address these issues. By representing energy units as unique, verifiable Non-Fungible Tokens (NFTs), our aim is to create a transparent, secure, and efficient energy marketplace. Our architecture integrates a Web3 marketplace with smart contracts to manage trading transactions, NFTs to visualize energy, and a sophisticated loyalty program to incentivize user participation. Furthermore, the platform improves user accessibility through cross-chain token bridging via the Across Protocol, enabling seamless fund transfers from Layer 1 or other Layer 2 networks to the Base network. These features collectively reduce entry barriers and expand market participation. Using blockchain technology, our solution addresses the limitations of traditional systems, offering enhanced security, user engagement, improved efficiency, and simplified access to the energy marketplace.
Rhana Elsayed, Mohamed I. Ismail, Ahmed F. Ashour, Hesham A. Sakr · 6 authors
Cyber-physical Systems (CPS) are increasingly utilized by Smart Building Management Systems (SBMS) to achieve intelligent, energy-efficient operations. The significant energy footprint of buildings (approximately 40% of worldwide energy use) and the desire to improve sustainability without compromising occupant comfort are the primary drivers of this movement. However, complicated cybersecurity issues are also brought about by the close integration of Internet of Things (IoT) sensors, automated controls, and networked management. In this study, we provide a comprehensive examination of CPS-based smart building monitoring and control technologies, exploring how state-of-the-art sensors, data analytics, and Artificial Intelligence (AI)-powered decision engines are utilized to enhance responsiveness and energy efficiency. We present a comparison between contemporary CPS-enabled SBMS and conventional building management systems, emphasizing the enhancements in security, flexibility, and energy efficiency. A thorough analysis of energy optimization processes demonstrates how machine learning algorithms and feedback loops may dynamically strike a balance between efficiency and comfort. We also examine new security models being developed to defend these cyber-physical infrastructures against attacks. This survey is unique because it comprehensively covers recent CPS developments and identifies emerging trends that will influence next-generation smart building management, including blockchain, Decentralized Autonomous Organization (DAO)based decentralized management architectures, and federated learning for collaborative optimization. In addition to outlining substantial obstacles and future research opportunities, our findings highlight the crucial role that CPS plays in enabling safe, self-sufficient, and sustainable buildings.
Time series forecasting plays a critical role in decision-making processes across diverse fields including meteorology, traffic, electricity, economics, finance, and so on. Especially, predicting returns on financial instruments is a challenging problem. Some researchers have proposed time series foundation models applicable to various forecasting tasks. Simultaneously, based on the recognition that real-world time series exhibit chaotic properties, methods have been developed to artificially generate synthetic chaotic time series, construct diverse datasets and train models. In this study, we propose a methodology for modeling financial time series by generating artificial chaotic time series and applying resampling techniques to simulate financial time series data, which we then use as training samples. Increasing the resampling interval to extend predictive horizons, we conducted large-scale pre-training using 10 billion training samples for each case. We subsequently created test datasets for multiple timeframes using actual Bitcoin trade data and performed zero-shot prediction without re-training the pre-trained model. The results of evaluating the profitability of a simple trading strategy based on these predictions demonstrated significant performance improvements over autocorrelation models. During the large-scale pre-training process, we observed a scaling law-like phenomenon that we can achieve predictive performance at a certain level with extended predictive horizons for chaotic time series by increasing the number of training samples exponentially. If this scaling law proves robust and holds true across various chaotic models, it suggests the potential to predict near-future events by investing substantial computational resources. Future research should focus on further large-scale training and verifying the applicability of this scaling law to diverse chaotic models.
Andrei Damian, Petrica Butusina, Alessandro De Franceschi, Vitalii Toderian · 6 authors
We propose the Ratio1 AI meta-operating system (meta-OS), a decentralized MLOps protocol that unifies AI model development, deployment, and inference across heterogeneous edge devices. Its key innovation is an integrated blockchain-based framework that transforms idle computing resources (laptops, smartphones, cloud VMs) into a trustless global supercomputer. The architecture includes novel components: a decentralized authentication layer (dAuth), an in-memory state database (CSTORE), a distributed storage system (R1FS), homomorphic encrypted federated learning (EDIL), decentralized container orchestration (Deeploy) and an oracle network (OracleSync), which collectively ensure secure, resilient execution of AI pipelines and other container based apps at scale. The protocol enforces a formal circular token-economic model combining Proof-of-Availability (PoA) and Proof-of-AI (PoAI) consensus. Compared to centralized heterogeneous cloud MLOps and existing decentralized compute platforms, which often lack integrated AI toolchains or trusted Ratio1 node operators (R1OP) mechanics, Ratio1's holistic design lowers barriers for AI deployment and improves cost-efficiency. We provide mathematical formulations of its secure licensing and reward protocols, and include descriptive information for the system architecture and protocol flow. We argue that our proposed fully functional ecosystem proposes and demonstrates significant improvements in accessibility, scalability, and security over existing alternatives.
Although the P2P power transactions using the multiagent deep deterministic policy gradient (MADDPG) algorithm has been extensively studied, there are still challenges in privacy protection and training incentives. Furthermore, the stability and efficiency of the strategy decreases when dealing with nonindependent identically distribution (Non-IID) data from heterogeneous prosumers. Therefore, this article proposes a blockchain-enabled asynchronous federated learning-MADDPG (BEAFL-MADDPG) framework designed to enhance the training efficiency of heterogeneous prosumers while safeguarding data privacy. The framework includes a novel P2P energy trading model that facilitates energy trading amidst incomplete information while ensuring privacy assurances. In addition, a BEAFL-MADDPG algorithm is proposed, which accelerates training processes and enables parallel computation among agents. This algorithm enhances the efficiency of algorithm and empowers the training of diverse prosumers. Furthermore, a blockchain-enabled training mechanism and prosumer incentive scheme are proposed that not only encourage prosumer engagement in training but also ensure traceable transactions without the need for trust among participants. These mechanisms promote transparency and integrity, fostering a collaborative and secure environment for energy trading. Simulation results demonstrate that the framework achieves peak load reduction through optimized P2P trading, maintains computation efficiency across discount rates, and ensures secure transactions via blockchain-based incentives. These practical benefits support scalable and sustainable community microgrid operations.
This deliverable examines political economy barriers to climate policy through sectoral entry points that can make transitions more just, feasible, and developmentcompatible. It focuses on three areas where governance and politics strongly shape outcomes: coal transition strategies, carbon pricing, and international finance. Across these domains, the report draws on seven peer-reviewed studies and working papers to highlight five strategic entry points: i) decentralized just transition planning, ii) clean industrial development, iii) revenue recycling with social protection, iv) strategic framing and coalition building for carbon taxes, and v) equity-focused international finance reforms. Coal transitions are shown to depend on domestic contexts. Comparative analysis of 12 coal-relevant countries reveals six distinct clusters of political economy dynamics, ranging from civil society-driven transition in South Africa to contested transition pathways in India and Indonesia. Case studies stress the need for regionally tailored approaches. Carbon pricing is politically viable when embedded in broader fiscal or political agendas. Evidence from 46 global policy attempts underlines the role of coalitions, leadership, and framing co-benefits. Microsimulations for 16 Latin American and Caribbean countries show regressive impacts, with many highly affected households lacking social protection. The international finance analysis assesses the G7 pledge and Clean Energy Transition Partnership, tracking shifts in public finance for energy across income groups. While fossil fuel support has declined, clean energy funding has not risen proportionally, remains loan-heavy, and is concentrated in wealthier nations. Low-income countries receive negligible concessional flows, while G7 members continue expanding domestic fossil infrastructure. The study recommends embedding distributive justice into finance governance, scaling grant-based clean energy support for the Global South and aligning domestic actions with international commitments.
Rakan B Aldosari, Farah Kalmey, Abdullah T Alanazi, Ashraf A’aqoulah
Background: Blockchain is a cutting-edge innovation being applied to offer innovative solutions in various fields, including healthcare. The healthcare industry uses blockchain networks to store and distribute patient data across hospitals, physicians, diagnostic labs, and pharmaceutical firms. Blockchain applications are essential in the healthcare industry for identifying crucial fallacies that might be fatal. The effectiveness, security, and transparency in exchanging medical data may thus be improved in the healthcare industry. This technology may also aid medical institutions in procuring information and analysing patient data. Aim: To examine the published papers that discussed the ability of utilization and probable challenges of blockchain technology in KSA's healthcare supply chain management. Methods: Until February 10, 2023, the prime databases: Pub Med, Science Direct, Scopus, Google Scholar, Web of science, Embase and The Cochrane Library were searched. Published studies (except case reports), commentaries, editorials, reviews, and meta-analysis reporting on the use of blockchain technology in healthcare supply chain management were included and a Preferred Reporting Items of systematic Reviews and Meta-analyses (PRISMA) flow diagram was used to present the process. To assess risk of bias and the quality of the included studies, the Joanna Briggs Institute's (JBI) critical evaluation tools were implemented. Results: A total of 22 studies were included and most of them used blockchain in technology for ensuring transparency, security, and storage of electronic health or medical records. Patients benefited from seamless electronic health records provided by a multi-level blockchain eHealth system. It was observed that blockchain technology effectively addresses clinical trial misconduct, and its potential in this area is to boost data productivity for the healthcare sector. The distinctive data storage pattern of blockchain offers a high-security standard that potentially reduces concerns about data tampering. It provides flexibility, accountability, connection, and data access authentication. Blockchain helps the healthcare sector avoid certain risks and offers decentralized data protection. In our study, we observed that the most preferred network for integrating the healthcare authority, manufacturer, wholesaler, retailer, and service was Ethereum (ETH). Conclusion: Healthcare policymakers should implement blockchain in Healthcare Supply Chain Management. Moreover, they need to be aware that the primary issues with blockchain technology in the healthcare industry are the lack of practical applications, the high rate of failed initiatives, and the requirement for collaboration between diverse stakeholders. However, there is a lack of studies on how to evaluate the acceptability and assist healthcare organisations in using blockchain.
This Research Paper examines the implementation of blockchain-based decentralized applications (DApps) for the secure sharing of student credentials. The study begins by defining DApps and exploring how blockchain technology can enhance security and transparency in credential verification. Key aspects of blockchain technology, such as distributed ledger systems and consensus mechanisms, are discussed, along with their advantages, including immutability and decentralization. The implementation of the DApp is then reviewed, covering client-server communication interfaces and integration with existing credential systems. Additionally, the design and functionality of smart contracts are analyzed, emphasizing their role in automating processes and reducing security risks in credential sharing. Various real-world applications and use cases are presented, highlighting successful implementations. The analysis further explores the benefits and applications resulting from these implementations, as well as the challenges and limitations of adopting blockchain for credential sharing, including technical, legal, and privacy concerns. Finally, the study considers future applications of blockchain technology in education and its potential integration with emerging technologies such as artificial intelligence (AI) and the Internet of Things (IoT).
Kriptovalute predstavljaju jednu od najvažnijih inovacija u suvremenom financijskom sustavu. Temeljene na blockchain tehnologiji, omogućuju sigurne, transparentne i decentralizirane financijske transakcije bez potrebe za posrednicima poput banaka. Cilj ovog rada je analizirati ulogu kriptovaluta u aktualnom financijskom okruženju te istražiti njihov potencijalni utjecaj na budućnost globalnog financijskog sustava. U radu se obrađuju osnovne karakteristike kriptovaluta, s posebnim naglaskom na Bitcoin i Ethereum, ali i na nove oblike digitalne imovine koji se brzo razvijaju. Posebna pažnja posvećena je prednostima i rizicima korištenja kriptovaluta, regulatornim izazovima te mogućnostima integracije ovih tehnologija u tradicionalne financijske tokove. Kroz analizu tržišnih trendova i stručnih stavova, rad nudi uravnotežen prikaz potencijala i ograničenja kriptovaluta te razmatra mogući razvoj hibridnog financijskog sustava koji bi kombinirao elemente klasičnog i decentraliziranog pristupa. Zaključno, kriptovalute imaju značajan transformativni potencijal, ali njihova budućnost ovisi o tehnološkom razvoju, regulatornim okvirima i prihvaćenosti u široj javnosti.
In an uncertain world filled with cyberthreats, blockchain has proven to be a revolutionary technology of significant value to most industries. While blockchain is used extensively in the fields of energy, finance and governance, healthcare is among the key sectors whose applications have been most evident as far as its adoption into these sectors is concerned. Since data is currently regarded as both an asset and currency, security has emerged as a key issue especially in healthcare as more and more data breaches have emphasized the need for enhanced planning, requirements analysis and implementation of strong cybersecurity models. This paper introduces a cloud-based blockchain architecture The approach organizes network participants into clusters, with each cluster maintaining a single copy of the blockchain. This design introduces a new blockchain architecture tailored for secure healthcare data management, significantly lowering both computational demands and communication overhead particularly when compared with conventional Bitcoin networks and existing lightweight blockchain models, while at the same time, investigating how the proposed design adequately mitigates known security threats. Experimental results show that, with increasing number of nodes, the proposed model accelerates the updates of ledger, It achieves a 63% reduction in computational load while also decreasing network traffic by 10%.
Zero-knowledge Proof (ZKP), is an effective cryptographic primitive that allows one party to verify the correctness of a given statement without disclosing any additional information. It plays a central role in applications such as blockchain transactions and cryptocurrencies. However, implementations of ZKP suffer from the most time-consuming task called Multi-Scalar Multiplication (MSM). Existing works and evaluation criteria primarily emphasize speed enhancement, but overlook optimizations of area overhead. In this paper, a FPGA-based accelerator FusionMSM is designed to reduce the overall latency but also improve area overhead. We attribute the bottleneck of MSM to a three-layer pyramid, including the finite field arithmetic, point operations on elliptic curves and scheduling. For modular arithmetic, we propose an efficient and non-Montgomery modular multiplier by utilizing hybrid multiplication strategy and optimizing multi-bit LUT-based modular reduction. It obtains 1.11 x less area cost and 2.00 x speed-up versus the modular multipliers used in ZKP acceleration works. For point operations, we design a unified and fully pipelined point addition unit, which can run at 500 MHz, the highest frequency in the reported works. On top of that, we present a greedy mechanism to resolve potential collisions, which can reduce the idle cycles of the point addition unit and improve its utilization. As far as we know, FusionMSM achieves the best performance compared to other FPGA-based and ASIC-based works for the input sizes from 218 to 226. For the degree of 220, FusionMSM only needs 12.4% of time in Hardcaml, 24.54% of time in PipeMSM on FPGA, and 36.41% of time in ASIC-based work PipeZK. It also utilizes less resources, resulting in a 90.93% reduction in URAMs, 35.24% reduction in FFs and 47.59% reduction in CARRY8s. Compared to GPU-based implementations, FusionMSM delivers comparable performance but with a lower power of 24.5 W.