Abstract- This survey paper presents a comprehensive examination of blockchain technology and its diverse applications across multiple domains. It begins by outlining the historical evolution and fundamental structure of blockchain systems, followed by an in-depth discussion of the core technologies that enable decentralized and secure operations. The study further explores the potential applications of blockchain, highlighting its transformative impact on various industries. Additionally, the paper analyzes the key challenges associated with blockchain adoption, offering insights into current limitations and areas requiring further research. Keyword- Blockchain technology, Consensus mechanisms, Smart contracts, Cryptography, Distributed Ledger, Supply Chain Management, Healthcare Data Security, Identity Management, Asset Tokenization, Blockchain Applications
Hassan Raza, Tsendayush Erdenetsogt, Muhammad Mohsin Kabeer, Muhammad Arsalan Aslam · 5 authors
The block chain technology has become a potential solution to improving security, privacy, and trust on contemporary data management systems. Conventional centralized systems are easily breached, tampered with and unauthorized access makes it necessary to have decentralized systems that cannot easily be tampered with. Block chain offers immutability, transparency, and cryptographic security and smart contracts offer automated access control and auditing. Sensitive information is safeguarded using privacy-saving methods, such as encryption, a zero-knowledge proof, and decentralized identity schemes. Scalability and collaboration are further increased with integration with cloud and big data systems. This review identifies the uses of Block chain, challenges and future research direction, which shows that Block chain is capable of changing the way secure and privacy-conscious data management is achieved.
Stablecoin ENTISQ (ENUR TAGA ISTIQARAR) Technical Whitepaper v1.1 1. Executive Summary ENTISQ (Energy + Nur + Taqa + Istiqarar) is an innovative digital asset backed by the economic fundamentals of the GCC energy sector and synthetically pegged to AED and SAR. ENTISQ creates a new class of stable assets by combining currency stability with the region’s energy foundation. Objective: Provide a reliable stablecoin for cross-border payments, B2B transactions, energy contract settlements, and Web3 integrations within the GCC. 2. Mission & Vision Mission: Deliver a stable, transparent, and predictable digital asset for the GCC linking currency and energy markets. Vision: ENTISQ aims to become the benchmark digital currency of the region, serving as a foundation for a sustainable economy and energy sector.
Blockchain-based financial ecosystems generate unprecedented volumes of multi-temporal data streams requiring sophisticated analytical frameworks that leverage both on-chain transaction patterns and off-chain market microstructure dynamics. This study presents an empirical evaluation of a two-class confidence-threshold framework for cryptocurrency direction prediction, systematically integrating macro momentum indicators with microstructure dynamics through unified feature engineering. Building on established selective classification principles, the framework separates directional prediction from execution decisions through confidence-based thresholds, enabling explicit optimization of precision–recall trade-offs for decentralized financial applications. Unlike traditional three-class approaches that simultaneously learn direction and execution timing, our framework uses post-hoc confidence thresholds to separate these decisions. This enables systematic optimization of the accuracy-coverage trade-off for blockchain-integrated trading systems. We conduct comprehensive experiments across 11 major cryptocurrency pairs representing diverse blockchain protocols, evaluating prediction horizons from 10 to 600 min, deadband thresholds from 2 to 20 basis points, and confidence levels of 0.6 and 0.8. The experimental design employs rigorous temporal validation with symbol-wise splitting to prevent data leakage while maintaining realistic conditions for blockchain-integrated trading systems. High confidence regimes achieve peak profits of 167.64 basis points per trade with directional accuracies of 82–95% on executed trades, suggesting potential applicability for automated decentralized finance (DeFi) protocols and smart contract-based trading strategies on similar liquid cryptocurrency pairs. The systematic parameter optimization reveals fundamental trade-offs between trading frequency and signal quality in blockchain financial ecosystems, with high confidence strategies reducing median coverage while substantially improving per-trade profitability suitable for gas-optimized on-chain execution.
This paper introduces CryptoBench, the first expert-curated, dynamic benchmark designed to rigorously evaluate the real-world capabilities of Large Language Model (LLM) agents in the uniquely demanding and fast-paced cryptocurrency domain. Unlike general-purpose agent benchmarks for search and prediction, professional crypto analysis presents specific challenges: \emph{extreme time-sensitivity}, \emph{a highly adversarial information environment}, and the critical need to synthesize data from \emph{diverse, specialized sources}, such as on-chain intelligence platforms and real-time Decentralized Finance (DeFi) dashboards. CryptoBench thus serves as a much more challenging and valuable scenario for LLM agent assessment. To address these challenges, we constructed a live, dynamic benchmark featuring 50 questions per month, expertly designed by crypto-native professionals to mirror actual analyst workflows. These tasks are rigorously categorized within a four-quadrant system: Simple Retrieval, Complex Retrieval, Simple Prediction, and Complex Prediction. This granular categorization enables a precise assessment of an LLM agent's foundational data-gathering capabilities alongside its advanced analytical and forecasting skills. Our evaluation of ten LLMs, both directly and within an agentic framework, reveals a performance hierarchy and uncovers a failure mode. We observe a \textit{retrieval-prediction imbalance}, where many leading models, despite being proficient at data retrieval, demonstrate a pronounced weakness in tasks requiring predictive analysis. This highlights a problematic tendency for agents to appear factually grounded while lacking the deeper analytical capabilities to synthesize information.
Blockchain technology has emerged as a disruptive paradigm for secure and transparent data exchange; however, it continues to face significant challenges in scalability, interoperability, and security. Fragmentation across blockchain networks restricts seamless data integration, while traditional consensus mechanisms such as Proof of Work and Proof of Stake impose high computational costs and latency. To address these limitations, this article proposes an Intractive Blockchain structure to AI that takes advantage of deep learning and light consensus mechanisms to improve performance and safety. The proposed structure introduces three main contributions: (i) a model of detection of deep learning vulnerabilities that identifies real-time intelligent contract weaknesses to reduce application failures; (ii) a lightweight consensus protocol inspired by Byzantine failure tolerance (BFT) to minimize latency and improve the transfer rate, ensuring safe authentication; and (iii) a cross -chain interoperability layer that facilitates the perfect data exchange between heterogeneous blockchain networks. Experimental assessment of TensorFlow Hyperledger tissue shows that the proposed model improves the accuracy of vulnerabilities detection by up to 96 %, reaches a 23 % reduction in latency and increases the transfer rate by 18 % compared to conventional approaches. This research highlights the potential of AI-Empowered blockchain systems for scalable, secure and interpreter applications in financial, health and public services.
Blockchain technology has revolutionized the management of Electronic Health Records by enhancing healthcare efficiency, security, and immutability. To achieve an enhanced version of the healthcare framework, we utilize Algorand’s Pure Proof of Stake (P-POS) consensus algorithm, which further ensures decentralized, scalable, and tamper-proof data storage, thereby addressing key issues in EHR systems. This study examines the development of P-POS in healthcare, highlighting its benefits in enhancing patient privacy, interoperability, and data integrity. The conventional healthcare framework suffers from issues such as data breaches, unauthorized access, and inefficiencies in data storage. However, using P-POS may solve these problems, and it acts as a game-changer for next-generation EHR systems. Further, this paper discusses P-POS uses, advantages, and difficulties, establishing P-POS as a crucial facilitator of safe and effective healthcare ecosystems in the future. Finally, the discussion focuses on how this integration can be achieved within a healthcare framework.
Certificate authentication in online systems is required to ensure integrity and authenticity and prevent forgery. Traditional blockchain-based approaches work with double-chain architecture without any privacy-preservation capability or pack whole certificates into a single chain and incur substantial storage overhead. In this study, we introduce a light-weight dual-blockchain architecture with an external verification and audit side-chain and an inner chain for offline storing certificates. Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (Zero Knowledge Proofs) are employed to sign hashes without revealing sensitive information, and threshold signatures are employed to sign certificates. Compared to the traditional single-chain and double-chain architecture, the proposed system realizes up to 30% lower latency and 25% higher throughput based on experimental results on 1,000–100,000 certificate dataset. These results indicate the efficiency, scalability, and privacy-preserving feature of the proposed solution, which can be applied to large-scale applications for certificate management.
This paper presents a decentralized application (dApp) for warranty management built on the Ethereum blockchain using smart contracts. The solution addresses key challenges in conventional warranty systems, such as lack of transparency, difficulty in transferring ownership, and reliance on centralized control. By leveraging Solidity-based smart contracts, the proposed framework establishes a transparent and immutable warranty registry that supports registration, verification, and ownership transfer of warranties. A React-based frontend, integrated with MetaMask, provides an intuitive interface for warranty-related operations. Comprehensive testing and deployment in real-world scenarios demonstrate the system’s ability to manage the complete warranty lifecycle while enhancing security and transparency. This work contributes to the growing body of research on blockchain-enabled supply chain and asset management by offering a practical framework for decentralized warranty systems in production environments.
Brahma Sakthieswari G, Abisha Kamal D, Jesintha Sharon S, Priyanka S
The widespread adoption of e-learning platforms has increased the issuance of digital certificates; however, conventional certificate management systems are centralized and vulnerable to forgery, unauthorized alteration, and data loss. These limitations reduce trust and make verification dependent on intermediaries. This paper proposes a Blockchain and IPFS-based framework that ensures secure issuance, tamper-proof storage, and automated verification of digital certificates. The system deploys an Ethereum smart contract to register certificate metadata on-chain while the actual certificate file and associated JSON metadata are stored on IPFS, enabling decentralized storage and verifiable ownership. A dual-hash mechanism binds the certificate and its metadata to prevent hash spoofing. Additionally, Optical Character Recognition (OCR) and Natural Language Processing (NLP) are integrated for semantic integrity verification, allowing detection of textual manipulation even when the certificate's appearance remains unchanged. A dataset of 400 certificates was used to evaluate the system. The OCR/NLP module achieved 95% precision in name-matching, and smart contract operations incurred less than $0.05 per certificate, with verification completed in under 3.5 seconds. Results demonstrate that combining blockchain immutability, decentralized storage, and NLP-assisted verification provides a scalable and tamper-evident solution suitable for academic and professional certificate management.
NFT (Non-Fungible Token) has emerged as a trending topic in the digital world. This article focuses on the working principle of NFTs and their practical applications in real-world scenarios. Ethereum blockchain serves as the foundational technology that powers NFTs. This document provides a comprehensive technical overview of Ethereum blockchain technology applied in the textile industry for maintaining product ownership verification and authenticity
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
기존 이더리움 아키텍처는 트랜잭션 처리속도 문제와 구조상 적용될 수 없는 확장성 문제를 가지고 있었고 이는 대량 트래픽이 발생할 수 있는 기업에서 이더리움을 사용하기 어려운 이유였다. 본 논문에서는 옵티미스틱 롤업 기술을 기반으로 한 솔루션을 제안하였다. 기존 이더리움 아키텍처 구성인 이더리움 단일 노드에서는 평균 트랜잭션 처리속도가 28.15 TPS였고 제안된 아키텍처인 옵티미즘 솔루션을 단일 노드로 구성했을 경우, 기존 아키텍처보다 약 165% 트랜잭션 처리량이 증가한 74.6 TPS인 것을 확인하였으며, 확장할 수 없는 기존 이더리움 아키텍처와 달리 제안된 아키텍처에서 옵티미즘 노드를 10개로 확장하였을 경우 옵티미즘 단일 노드를 구성하였을 때보다 약 2,376% 증가한 699.1 TPS로 트랜잭션 처리가 가능한 것으로 확인되었다. 이를 통해 제안된 아키텍처에서 처리 노드를 확장할수록 선형적으로 트랜잭션 처리량이 증가하는 것을 확인하였다.
This paper presents a quantum-enhanced blockchain architecture addressing the dual challenges of quantum vulnerability and scalability limitations in conventional distributed ledgers. We propose a novel framework integrating three key innovations the NIST-standardized Dilithium lattice-based cryptosystem for post-quantum encryption, quantum-resistant binary data structures (1011011 sequences) for integrity verification, and sharded post-quantum key management. Our experimental results demonstrate significant improvements over classical systems, achieving 2,542 TPS throughput (2.7× increase) and reducing cryptographic latency by 67% using BB84 quantum protocols. The architecture provides comprehensive protection against Shor’s and Grover’s algorithms while maintaining blockchain’s decentralized principles through a hybrid quantum-classical consensus mechanism. Comparative analysis reveals our solution outperforms both RSA (classical) and Kyber (hybrid) in all evaluated metrics, including encryption speed (325 MB/s) and key generation time (112 ms). This work establishes a practical pathway for transitioning blockchain infrastructures to quantum-resistant paradigms without compromising performance.
This Utilize an efficiency-oriented procedure this research compares the achievement of two important Ethereum token standards: ERC-20 and ERC-721. Examining these processes under various operating settings while applying improving performance plan of action is the main goal. The research uses a administer Ethereum test net environment to apply maximizing techniques like batch processing, gas-efficient code, and meta data squeezing in addition to implementing token standards. Measured and examined are important performance indicators such as gas consumption, execution time, transaction output, and storage efficiency. According to the results, ERC-20 tokens are better ordered in routine and high-volume transactions because of their straight forward logic, but ERC-721 tokens take more resources at first because of their single identifiers and metadata needful although they manifest noticeable gains after optimization. The research highlight the theme of choosing a settlement according to the demands of the application, supporting ERC-20 for exchangeable asset management and ERC-721 for use cases involving non-exchangeable assets. Besides, it provide developers and blockchain architects practical advice for creating segregated apps (dApps) that are scalable, economical, and result-oriented. Due to its straighter forward and harmonious token logic, ERC-20 performs better than ERC-721 in terms of gas ability, transaction throughput, and storage utilization, according to the contrast analysis. Due to specialized token IDs and metadata, ERC-721, which is deliberated for unique assets, has increased gas expenses and storage on high. However, ERC-721 speed is greatly boosted by improving methods including batch processing and metadata compression. If presentation improving are used, ERC-721 is more looked right on for handling non- fungible assets like NFTs, but ERC-20 is more appropriate for high-frequency, exchangeable transactions like payments and DeFi.
Blockchain technology has emerged as a transformative paradigm for decentralized systems, enabling secure, transparent, and tamper-resistant data management through distributed consensus mechanisms that eliminate the need for centralized control. At the core of these systems, consensus protocols ensure agreement among network participants; however, traditional approaches such as Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT) face persistent challenges related to scalability, energy consumption, and latency. In response, graph-theoretic approaches have gained prominence as an effective framework for modeling and optimizing blockchain consensus by representing nodes as vertices and communication links as edges, thereby capturing complex network relationships, trust structures, and interaction patterns. This paper systematically reviews graph-based methods applied to blockchain consensus, highlighting their role in improving efficiency, enhancing security against attacks such as Sybil and double-spending, and optimizing node selection. Advanced techniques including graph partitioning, spectral clustering, and network flow optimization further contribute to improved scalability and throughput. The study identifies a clear transition toward intelligent, hybrid consensus mechanisms integrating graph theory, machine learning, and distributed computing, while also addressing ongoing challenges such as computational complexity and dynamic adaptability, and outlining future directions for AI-driven, scalable, and secure consensus models.
ABSTRACT The combination of blockchain technology with Industrial Internet of Things (IIoT) frameworks is promising in terms of building trust, data authenticity, and resilience. However, the efficiency and feasibility of integration largely rely upon the consensus mechanisms used. The present study is an overview of four renowned blockchain consensus schemes, namely Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Delegated Proof of Stake (DPoS), and the corresponding performance, security, efficiency, as well as compatibility under IIoT. The results reveal that low‐latency and lightweight consensus, such as PBFT and DPoS, will be helpful in IIoT applications, especially in applications with scarce resources. The paper offers practical guidance on the development of IIoT systems with integrated blockchain customized based on the requirements of the industry.
Ahmed Anwer Jaafa, Madhu Sahu, M. Jasmin, Mamadjanova Zukhra Bakhromjanovna · 8 authors
Sharing patient data safely and efficiently is still hard in the constantly changing world of digital healthcare because of worries about privacy, giving consent, and how different systems work together. This paper suggests using ChainMedX, which relies on blockchain technology to let patients, doctors, and other healthcare professionals exchange data in real time with dynamic consent consent management. ChainMedX uses permissions, smart contracts, and zero-knowledge proofs to let patients pick who can have access to their medical records, manage exactly what is shared, and specify the time period the sharing is needed, making sure those permissions cannot be altered. Being distributed across both cloud and edge servers, the patient-managed encrypted data vaults make active updates of medical records possible, complying with FHIR standards. Thanks to an AI-based consent suggestion module, patients receive useful advice that suits their needs and the current emergency situation. In addition, ChainMedX deals with urgent issues, such as fast access with easy ‘break-glass’ rules and complete tracking of every transaction, and makes it easier for healthcare services to interact with the wider health organization and verify insurance policies. According to the results, latency, security, and managing consent are all better in the new system than in systems that operate centrally or rely on blockchain. The research mentions that connecting blockchain, edge computing, and privacy-based cryptography can form a healthcare system that respects patient data privacy and makes healthcare cooperation speedy and secure. The goal of this framework is to help provide for data exchange between countries, while including new forms of digital health technology, all this aims to strengthen patient trust and the integrity of their medical data, along with providing better healthcare outcomes.
A big and always-changing network of nodes that are connected to each other is what makes Bitcoin and other decentralized block chain networks safe, scalable, and reliable. It takes a lot of time and work to accurately map and analyses these large-scale topologies because of their communication overhead and inherent complexity. We introduce a novel topology discovery methodology that addresses this issue by integrating lightweight “probe” nodes for quick data collection, a clustering mechanism to aggregate stable nodes for enhanced accuracy, and a visualization system capable of displaying the network's layered structure in realtime. Our tests on the Bitcoin network demonstrate that the suggested solution gets 95 % of the mapping right and cuts down on communication cost by roughly 72 %. The framework's architecture is not particular to Bitcoin; instead, the methodology can be used to various blockchain networks with similar peer-to-peer topologies. It may be used by researchers, developers, and system administrators to evaluate, monitor, and improve distributed ledger systems because it is scalable and works well. Its generalizability ensures that it can affect several blockchain ecosystems.
Self-collecting Internet of Things (IoT) gadgets have transformed healthcare systems. Centralising IoT healthcare data processing and storage introduces scalability, speed, security, and privacy issues. On the other hand, Blockchain technology attracts interest in the IoT healthcare industries because of its decentralisation, data protection, transparency, and security aspects. Single public blockchain ledgers are inefficient for healthcare IoT security and efficiency due to high transaction fees, limited scalability, and high patient traffic. Specifically, this article focuses on the concerns around privacy, security, performance, scalability, and energy consumption in healthcare blockchain-IoT systems. In this paper, we propose CareChain, an IPFS storage system with two blockchains, one for patients and the other for healthcare providers, to manage healthcare IoT data. The proposed model uses IPFS distributed storage to improve system throughput, reducing transaction latency and blockchain storage overhead. It improves storage requirements, energy efficiency, transaction speed, privacy, and security. It envisions a system-wide data and information security architecture that uses the Elliptic Curve Digital Signature Algorithm (ECDSA) and a device proxy to keep tabs on low-cost devices. The prototype model was tested to investigate its security, efficiency, and energy use. The results show that this system is more robust than the existing healthcare models.
ABSTRACT This paper employs deep learning and machine learning‐based NLP models to investigate the impact of the news sentiment on the Bitcoin price. The lagged Bitcoin variables, news indicators, macroeconomic, and financial factors were taken into account to explain the importance of news sentiment on the Bitcoin price. Moreover, FinBERT‐based sentiment scores and semantic features extracted from over 650,000 financial news headlines were integrated with financial and macroeconomic variables. The importance scores of the investigation showed that Bitcoin was largely explained by its lagged price movements, which suggests the speculative nature of the cryptocurrency. However, the investigation also revealed that Bitcoin was significantly influenced by the news sentiment score. In other words, the paper indicates that the movements in the Bitcoin price can be predominantly explained by the news sentiment. Advanced hybrid models (all ML and DL models with the addition of variables obtained with the FinBERT model) were optimized using Optuna and RandomizedSearchCV. The FinBERT‐LSTM model achieved the best prediction accuracy. Nevertheless, the main findings indicated that the response of the Bitcoin price to negative news was much stronger than to positive and neutral news. This finding suggests that the asymmetric relationship between the Bitcoin price and news sentiment was evident. GARCH‐based volatility and what‐if scenario analyses further demonstrated that negative sentiment leads to sharper fluctuations in the Bitcoin price. The paper provides important implications for policymakers, portfolio managers, investors, and academics.
The growing impact of climate change has made carbon credit trading an essential strategy for controlling industrial emissions. Traditional systems face issues such as fraud, inefficiency, and limited transparency. This paper presents a blockchain-based platform for carbon credit trading that supports the minting, exchange, and burning of digital credits. Organizations can retire (burn) credits to offset emissions and obtain a non-fungible token (NFT) certificate as verified proof of compliance. Through decentralization, immutability, and transparency, the framework removes intermediaries and builds trust. The proposed system offers a secure, scalable, and tamper-resistant solution for verifiable carbon offsetting, advancing sustainable blockchain innovation.
Francesco Salzano, Simone Scalabrino, Rocco Oliveto, Remo Pareschi
Smart Contracts are critical components of blockchain ecosystems, with Solidity as the dominant programming language. While LLMs excel at general-purpose code generation, the unique constraints of Smart Contracts, such as gas consumption, security, and determinism, raise open questions about the reliability of LLM-generated Solidity code. Existing studies lack a comprehensive evaluation of these critical functional and non-functional properties. We benchmark four state-of-the-art models under zero-shot and retrieval-augmented generation settings across 500 real-world functions. Our multi-faceted assessment employs code similarity metrics, semantic embeddings, automated test execution, gas profiling, and cognitive and cyclomatic complexity analysis. Results show that while LLMs produce code with high semantic similarity to real contracts, their functional correctness is low: only 20% to 26% of zero-shot generations behave identically to ground-truth implementations under testing. The generated code is consistently simpler, with significantly lower complexity and gas consumption, often due to omitted validation logic. Retrieval-Augmented Generation markedly improves performance, boosting functional correctness by up to 45% and yielding more concise and efficient code. Our findings reveal a significant gap between semantic similarity and functional plausibility in LLM-generated Smart Contracts. We conclude that while RAG is a powerful enhancer, achieving robust, production-ready code generation remains a substantial challenge, necessitating careful expert validation.