This research uses an Ethereum blockchain dataset that contains transactional data, metadata, registry logs, payments and invoices to investigate how Extreme Gradient Boosting (XGBOOST) and Merkle Tree Blockchain can be integrated to optimize Supply Chain Finance (SCF) operations. This will improve SCF processes by guaranteeing data integrity, transparency and security. In order to forecast monetary flows and effectively detect the anomalies, the researchers use a robust method that begins with preprocessing using One-Hot Encoding. After the preprocessing step, the Feature Extraction takes place and is done by Independent Component Analysis (ICA) to identify independent components from the dataset. Then the optimization is done by XGBOOST. Moreover, by comparing the Merkle Tree Blockchain method with the existing Practical Byzantine Fault Tolerance (PBFT), the proposed Merkle Tree Blockchain guarantees safe encoding, decoding and hashing processes while drastically lowering latency and raising throughput that improves the system’s overall efficiency. Furthermore, a robust SCF architecture is supported by network performance monitoring that guarantees scalability, low latency and high throughput. The proposed XGBOOST technique outperforms the current techniques in financial forecasting and fraud detection, reaching 99.96% accuracy, 99.05% precision, 98.61% recall and 99.54% of the F1-score. By enhancing the cash flow, this integration ensures sustainability and operational efficiency by fostering collaboration and trust among the supply chain partners. Thus, this research demonstrates the revolutionary potential of blockchain technology and powerful Machine Learning (ML) in transforming SCF operation by providing a more secure, transparent and effective way to manage financial transactions.
This research suggests a groundbreaking methodology for the optimization of Supply Chain Finance (SCF) by integrating Deep Belief Networks (DBN) and Proof of Stake (PoS) blockchain techniques. The research collects business details, financial tactics, and supplier information from the Kaggle dataset. This paper uses Tokenization for the process of preprocessing, and it uses Locally Linear Embedding (LLE) method to reduce the dimensionality and in the protection of limited structures. Then, the optimization is done by DBNs, which will improve the method’s accuracy, whereas the PoS secures the data with encryption, decryption, and hashing. Moreover, the method’s effectiveness is evaluated using performance analyses such as computational time, efficiency ratio, error ratio, data authentication, and data management ratio. The proposed DBN-PoS is then compared with some existing methods like Self-adaptive Tasmanian Devil Optimization (SA-TDO), Federated Learning (FL), and Deep Convolutional Neural Network (Deep CNN) to provide a higher accuracy of about 100% and F1-Score of about 99.90%. Furthermore, blockchain integration improves transparency, protects the details of transactions, and safeguards from fraud actions using the SCF system. This research addresses progressing SCF optimization by integrating AI and Blockchain, providing a climbable, well-organized, and protected resolution for real-world entities.
Prajakta Sudhir Khade, Aarushi Santosh Gode, Rajeshkumar U. Sambhe
The exponential rise of cyber threats has revealed the vulnerabilities of centralized security systems, including susceptibility to insider attacks, single points of failure, and regulatory inefficiencies. This paper investigates blockchain as a transformative backbone for cybersecurity, focusing on its potential to ensure data integrity, decentralize trust, and mitigate advanced cyber risks. Beginning with a comprehensive literature review, the study examines the fundamentals of blockchain technology—distributed ledgers, consensus mechanisms, and cryptographic primitives—that enable tamper-proof, transparent, and secure digital ecosystems. The challenges of centralized systems are contrasted with blockchain’s resilience, highlighting its role in eliminating bottlenecks and enhancing trust. Applications across identity management, IoT security, supply chains, and e-governance are analyzed alongside a proposed methodology that integrates blockchain with artificial intelligence, IoT, and quantum-resilient models. Real-world case studies demonstrate blockchain’s adoption in healthcare, government, and industrial systems, while challenges such as scalability, interoperability, and compliance are critically assessed. Collectively, this study underscores blockchain’s pivotal role in shaping next-generation cybersecurity architectures.
The rapid proliferation of Internet of Things (IoT) devices across critical sectors has introduced unprecedented security challenges. While Artificial Intelligence (AI) provides powerful tools for threat detection, AI models themselves are vulnerable to data poisoning and adversarial attacks. This paper introduces a novel hybrid framework that synergistically integrates AI with blockchain technology to create a secure and resilient data transmission ecosystem for the IoT. Our architecture utilizes AI-driven techniques, including simulated federated learning, for privacy-preserving anomaly detection at the network edge. A blockchain-based ledger then provides tamper-resistant data validation and immutable record-keeping. The system employs smart contracts on a private Ethereum testnet, combined with the InterPlanetary File System (IPFS) for efficient off-chain storage, ensuring both data integrity and provenance. We evaluated the framework in a simulated environment using benchmark datasets, including CICIDS2017 and UNSW-NB15. The results show a high efficacy in detecting sophisticated attacks, with a Random Forest classifier achieving 98.46
The emergence of blockchain technology represents a revolutionary breakthrough that specifically transforms computer networking operations. Security is strengthened by integrating blockchain technology with networking infrastructure, and decentralization functions become more effective and efficient. The paper examines recent developments that unite blockchain with FL (Federated Learning) through an analysis of ways blockchain technologies support FL functions while resolving its operational obstacles. The research examines how innovation develops through methods of decentralization coordination, along with incentive systems, solutions for privacy protection and scalability, and models of governance. The review paper investigates the responsibility of security adaptation and decentralized knowledge for enhancing blockchain-based networking systems. The research examines major obstacles along with the advantages and outlines prospective research pathways. Academic researchers with expertise in computer science evaluated blockchain, and it received evaluations from specialists in finance and healthcare as well as supply chain management fields.
Blockchain technology offers incredible value and opportunity in delivering secured, decentralized transactions but faces significant challenges as it relates to scalability, which hinders adoption and use. As in the maturation of blockchain technology, challenges related to its scalability are important for latest technologies as they can affect scalability and efficiency. These types of challenges to scalability generally emerge through increased user and transaction volumes and cause losses in performance and efficiency as they pertain to a congested network, storage of data, and speed in processing time. Additionally, traditional consensus mechanisms, like proof of work, seamlessly create challenges as they pertain to amounts of computational power, and other resources allow allocation associated with work completion. As we can imagine, potential solutions have already emerged that help to mitigate overcoming these challenges. Solutions related to Layer 2 scaling, such as side chains or payment channels, add an alternative layer to the transaction applications and, theoretically, increase throughput and financially through decreased network congestion. Sharding, which splits the blockchain into smaller, more manageable segments, also improves operational efficiency. Moreover, advancements in consensus algorithms, including Proof of Stake and hybrid models, aim to boost scalability while optimizing resource use. This research explores the primary scalability challenges faced by blockchain systems. It reviews the cutting-edge solutions being developed to improve their performance, ensuring that blockchain technology can effectively support the increasing demands of a decentralized digital world.
Shwetha K R, Divya G S, Bhavan Pande, Darshan K · 6 authors
Due to the ever-increasing demand to use safe and reliable electronic votes, a blockchain-based secure voting system has been developed to enhance transparency, trustfulness, and voter recognition. This system eliminates such issues as voting fraud, impersonation, and manipulating the results by means of biometric verification and decentralized blockchain ledger. The voters are matched to a facial-recognition database containing previously registered voters before voting. It is authenticated by a K-Nearest Neighbors (KNN) approach as it works well on classifying facial features and is not very laborious. After the vote is successfully authenticated, it is stored and signed on a blockchain network where it cannot be altered by another party. The features of smart contracts ensure the safety of voting, the correct counting of votes, and the awareness of each network node of what is happening. The cryptography of hashing and decentralized make certain that the votes are immutable, due to the decentralized structure of blockchain and consensus mechanisms. The face-matching module ensures that only the qualified individuals are allowed to vote. The system also supports mass elections and guarantees the ease of interaction among the voters. It was designed in such a way that it is scalable and user friendly. Trust, security, and efficiency are enhanced in the system through biometrical authentication, distributed ledger technology, encryption, and classification through machine-learning. It is highly dependable in how to conduct the current digital elections.
Since the introduction of Bitcoin in 2008, the blockchain technology as its underlying architecture, has attracted attention from various sides due to its decentralized and distributed computing characteristics. AS the core advantage of blockchain technology, the consensus mechanism determines various characteristics of blockchain, such as security, scalability, and decentralization. Currently, there are many consensus mechanisms suitable for different scenarios. This paper studies the existing consensus mechanisms from the perspectives of algorithm principles, performance, etc. Firstly, this article divides the existing consensus mechanisms into Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerance (BFT). Secondly, for each type of consensus mechanisms, the study analyzes their algorithmic principles, understands typical solutions and latest ones, clarifies the advantages, disadvantages, and the possible attack methods of various consensus mechanisms. Finally, the paper defines the basic requirements for new consensus mechanisms. It aims to help break through the application bottlenecks of blockchain technology and promote the development of blockchain technology in various scenarios.
Blockchain is rapidly developing in many sectors, and it is promising its application specifically for healthcare. This chapter examines the potential of blockchain technology to offer solutions to some of the key problems in healthcare systems, including data breaches, fragmented systems, and the apparent lack of patient control over their health records, all of which could be addressed through the use of secure and transparent blockchain systems. With its sophisticated encryption technology, consensus algorithms, and decentralised access control, Blockchain is well-suited to securing sensitive healthcare data. The chapter explores the technical architecture of Blockchain in healthcare, contrasting public and private blockchains and looking at the role of consensus mechanisms such as proof-of-stake and proof-ofauthority. Some critical healthcare-based applications are the management of secure patient data, the management of the drug supply chain, clinical trials, and medical billing, in which Blockchain helps keep the data integrity, fraud prevention, and compliance assurance. Despite its promising benefits, challenges such as scalability, high implementation costs, and resistance to change within the healthcare ecosystem are discussed as well. By addressing these limitations, Blockchain can significantly improve the security and transparency of healthcare systems.
Blockchain technology is rapidly becoming one of the most groundbreaking technologies for revolutionizing supply chain management with unprecedented security, transparency, and efficiency. This paper presents a comprehensive literature review of blockchain and its applications in leading industries such as transportation, manufacturing, food and beverage, and healthcare. Blockchain applies distributed ledger technology to secure tamper-evident record-keeping, which significantly enhances traceability and provenance verification across complex supply chains. By integrating smart contracts, IoT connectivity, and decentralized financial services, blockchain can solve significant challenges, such as counterfeiting, supplier management, and enforcing sustainable and responsible sourcing practices. Despite these benefits, the mass-scale adoption of blockchain faces serious challenges, such as scalability, interoperability, regulatory ambiguity, and a lack of standardized frameworks. The report also addresses the environmental concerns of blockchain’s power-intensive proof-of-work algorithm and discusses ways to counteract them. Future developments in artificial intelligence and 5G networks will continue to evolve supply chain management in ways that unleash unmatched efficiency and potential.
Supply chain operations that include blockchain technology have the potential to improve cybersecurity and data integrity. The complexity and interconnectedness of global supply chains make it imperative to guarantee the security and validity of data for all parties involved. Because blockchain technology is decentralized, transparent, and unchangeable, it offers a perfect solution to common supply chain weaknesses, including fraud, data tampering, and illegal access. The use of blockchain technology to protect important data at each link in the supply chain—from manufacturing and procurement to distribution and delivery—is examined in this article. By leveraging smart contracts, cryptographic security, and distributed ledger systems, blockchain can ensure the traceability of goods, validate transactions, and protect sensitive information from cyber threats. The paper also highlights how blockchain has the potential to completely transform supply chain management security while addressing issues like scalability, integration with current systems, and regulatory considerations. In the end, this study highlights how blockchain technology may improve data integrity, lower cyber threats, and foster trust among supply chain actors.
This paper presents a privacy-preserving intrusion detection architecture tailored for smart home environments, addressing the dual challenge of maintaining data confidentiality while enabling accurate anomaly detection. The proposed system replaces conventional raw data analysis with a proof-driven mechanism leveraging Zero-Knowledge Proofs (ZKPs). Behavioral patterns from smart devices such as motion sensors, door contacts, and environmental monitors are abstracted into cryptographic representations, which are then processed by a zk-SNARK-compatible machine learning model. Inference results are accompanied by cryptographic proofs verifying the correctness of each decision without disclosing the input data. A private blockchain layer, implemented using Ethereum smart contracts, records event hashes, proof metadata, and decision outcomes to ensure tamper-evident logging and automated response handling. Experimental simulations on synthetic home automation datasets demonstrate that the architecture achieves over 92% anomaly detection accuracy while ensuring zero exposure of raw sensor streams. The system also exhibits low-latency proof generation (~400 ms) and end-to-end response time under 1.2 seconds, confirming its suitability for real-time smart home applications.
This research focuses on the design and development of a blockchain-based plastic waste tracking system aimed at enhancing transparency, efficiency, and accountability in plastic waste management. The system utilizes Hyperledger Fabric as a permissioned blockchain platform and integrates smart contracts to manage transactions between organizations, including waste generators, collectors, sorting warehouses, and final processing warehouses. This system records each stage of the plastic waste journey, from creation to final processing, in a permanent, transparent, and immutable manner. The testing results demonstrate that the system can accurately record the status and history of waste, manage transfers between organizations, and process plastic waste into recycled products. Moreover, the system shows a significant potential for carbon emission reduction, with an estimated reduction of up to 50% compared to traditional plastic waste management methods, such as incineration or landfilling. The study also explores how the implementation of blockchain can support global efforts in mitigating the environmental impacts of plastic waste. The blockchain-based system also provides real-time monitoring, ensuring that each transaction is verified and recorded immediately, contributing to more effective management. The implementation of smart contracts further guarantees that waste-related activities are executed automatically when predefined conditions are met, reducing administrative overhead. The study also explores how the implementation of blockchain can support global efforts in mitigating the environmental impacts of plastic waste. Ultimately, this system presents a scalable solution that could be adopted in various regions to improve global waste management strategies.
Rajasekaran P, M. Duraipandian, Johny Renoald Albert
The increasing rate of growth of the Internet of Things (IoT) in cloud-hospitality health has brought in data storage, transmission, and security challenges with the advent of quantum-enabled threats. Traditional compression methods struggle with computational inefficiency and the threat of invasion of privacy. This paper proposes a Quantum-Enhanced Zero-Knowledge Healthcare Compression Network for solving these challenges by combining Zero-Knowledge Proofs and Quantum-Inspired Deep Learning. The main goal is to provide privacy-preserving, efficient data compression along with optimizing computation costs and safeguarding sensitive healthcare records. Drawbacks in present cryptographic techniques, e.g., high computational costs in homomorphic encryption and scalability limitations in blockchain, require a novelty Adaptive Quantum-Assisted Zero-Knowledge Verification and Quantum Fusion-AutoCNN Encoder (QF-AutoCNN) to overcome this research. This work’s originality lies in combining Quantum zk-SNARKs, Hybrid Quantum Feature Encoding, and Reinforcement Learning-Based Challenge Optimization to provide better security, compression ratio, and verification efficiency. Experimental results show better accuracy (0.9816), improved F-measure (0.9709), and less computational overhead, better than other current methods such as convolutional neural networks-encryption and proxy re-encryption. This research greatly adds to safe cloud healthcare IoT by lessening privacy threats, maximizing storage space, and minimizing processing time, guaranteeing real-time handling of medical information.
The COVID-19 pandemic has accelerated the adoption of digital health solutions such as telemedicine, Internet of Medical Things (IoMT), and AI-based diagnostics, enabling remote monitoring and contactless consultations. While IoMT devices—including wearable sensors and implantables—have enhanced continuous healthcare delivery, they have also introduced challenges related to security, privacy, interoperability, and latency. Traditional blockchain frameworks, though effective in ensuring decentralized trust and immutability, are resource-intensive and unsuitable for constrained IoMT environments. To address these limitations, this study proposes a Lightweight Blockchain–IoMT framework tailored for secure remote healthcare in the post-pandemic era. The proposed architecture follows a three-tier design: (i) the IoMT Device Layer for real-time physiological data collection, (ii) the Fog/Edge Layer functioning as blockchain gateways for authentication and pre-processing, and (iii) the Cloud Layer for storage, analytics, and decision support. By incorporating lightweight consensus mechanisms such as Proof-of-Authentication (PoAh) or Delegated Proof-of-Stake (DPoS), the system minimizes latency and energy consumption compared to Proof-of-Work. Security is reinforced through elliptic curve cryptography (ECC) and smart contracts, ensuring data confidentiality, integrity, and controlled access, while complying with global standards such as HIPAA and GDPR. Experimental analysis demonstrates that the lightweight blockchain–IoMT framework outperforms conventional blockchain models in transaction throughput, scalability, and energy efficiency. Moreover, the integration of machine learning within the cloud layer supports predictive analytics and personalized care.
Srinivas. D, C M Preeti, P. Tirumala, Nitesh Ghodichor · 6 authors
It is anticipated that blockchain-based technologies will significantly alter a wide range of corporate processes and applications, which will have a significant impact on ecommerce. This study introduces a blockchain-based ecommerce transaction model, emphasising how it might improve efficiency, security, and transparency. By merging the state-of-the-art Blockchain Consent Algorithm (BCA) with Distributed Ledger Technology (DLT), particularly Decentralised Identifiable Distributed Ledger Technology (DIDLT), this study investigates how to improve the security and privacy of ecommerce transactions. This study examines the ways in which BCA, a state-of-the-art consensus algorithm created for blockchain environments, and DIDLT, which integrates decentralised identity management with blockchain technology, cooperate to safeguard e-commerce transactions against data manipulation, payment fraud, and identity theft. By offering a thorough examination of their application, this study elucidates the potential of BCA and DIDLT to revolutionise the e-commerce security and safety sector. The network's safety efficacy is greatly increased by using the integrated DIDLT-BCA model, which leads to 99.1% security, up to 150 milliseconds in performance times, and times of mining of up to 0.99 seconds.
Transparent and secure communication is crucial in the digital age. The use of blockchain algorithms to improve communication security and transparency is examined in this article. We solve flaws in conventional communication networks by utilizing decentralized ledger technology, which guarantees data integrity, secrecy, and authenticity. We assess different consensus algorithms and show how well they work to secure communication channels, such as Proof of Stake and Proof of Work. We demonstrate the benefits and possible drawbacks of utilizing blockchain technology for secure communication through empirical investigation. This study sheds light on potential uses of blockchain in the communication industry in the future.
This paper presents the design and implementation of a blockchain-secured system for monitoring driver sobriety and real-time geolocation. The proposed platform integrates a Modular Sensor Battery (MSB) for detecting alcohol concentration in exhaled air, a centralized Data Collection Platform (DC Platform) for real-time data visualization and storage, and a complementary physiological monitoring device—the IoT Fit-Bit Smart Band (IFSB)—which captures heart rate and blood oxygen saturation as alternative indicators when breath-based sensing may be compromised. The MSB, the DC Platform, integration with the IoT FitBit Smart Band, and the blockchain-based data management architecture represent the authors’ direct contribution to both the conceptual design and technical implementation. These elements are introduced as part of a unified, fully integrated system designed to enable non-invasive sobriety monitoring and secure data integrity in vehicular contexts. To ensure data authenticity, a custom Ethereum smart contract stores cryptographic hashes of sensor readings, enabling decentralized, tamper-evident verification without exposing sensitive medical information. The system was validated in a controlled experimental environment, confirming its operational robustness and demonstrating its potential to improve road safety through secure, real-time sobriety detection and geolocation tracking.
Due to the high rate of fragmentation, India's healthcare system is faced with stagnant patient data that is isolated in silos involving thousands of clinics and healthcare centers. This disunity is linked to significant gaps in patient care, as individuals carry actual documents and medical professionals struggle to obtain a comprehensive clinical history when necessary. In this study, the authors analyze how blockchain can be used to resolve the healthcare interoperability crisis in India. Compared to the traditional centralized databases, blockchain establishes a distributed ledger, through which records between patients remain securely shared within approved healthcare networks. Immutable feature maintains data integrity and the decentralized system offers high security against cyber-attacks that are being perpetrated more commonly on healthcare institutions. There are three main benefits to the implementation of a blockchain identified in the study. One is increased security due to distributed storage of data that removes single-point failover which hackers usually target. Second, the enhanced interoperability will enable the easy flow of information among the public and private healthcare providers eliminating repetitive tests and delays in care. Third, greater transparency allows the patients more influence on their healthcare data, as well as improves the ability to track clinical trials and outcomes. Nevertheless, a number of problems make the adoption of blockchain in the Indian healthcare industry difficult. The cost of implementation is still extremely high in many institutions especially the smaller clinics and the rural health care providers. The current electronic health records systems have to be upgraded significantly to connect to the blockchain networks. The regulatory frameworks will have to change, such as the Personal Data Protection Bill in 2020 in India, and ensure that they support decentralized data management alongside standards of compliance. Based on the conducted research, it can be concluded that the implementation of blockchain requires the joint work of governmental institutions, healthcare facilities, and technology corporations. Though there exist some technical and financial obstacles, the capability of blockchain to build a single, safe ecosystem in the healthcare industry may bring incredible changes in patient outcomes of the wide variety of healthcare in India.
The integration of IoMT is IoMT is medical image encrypting blockchain's paradigm in privacy has IoT advancements in healthcare have been modified by blockchain technology. The Internet of Medical Things is using blockchain frameworks and alongside other decentralized technologies to establish interoperability in health monitoring and safeguard medical data through patient remote monitoring. IoMT networks comprise numerous autonomous IoT-based systems working synergistically to facilitate patient health maintenance. Various methodologies for securing medical images, like PACS and IPFS, as well as off-chain storage with cryptographic hash referencing, have been devised. Every approach has its strengths and weaknesses, and the research considered the security, cost, scalability, and integrity of data. In this way, systems can enable remote monitoring of patients while preserving the confidentiality of their sensitive data. The RPM framework empowered by blockchain is also essential in improving health systems, engaging patients, and streamlining clinical workflows. This paper analyzes the intersection between IoMT and blockchain technology focusing on remote patient monitoring (RPM) systems.