Blockchain Data Research Articles

Database energy saving strategy using blockchain and Internet of Things

This paper aims to construct a green environmental protection system by advancing database energy-saving techniques and optimizing the energy-saving mechanism against the backdrop of blockchain integration. The protocol classification of wireless sensor networks is examined within the context of the rapid growth of information technology. The analysis draws upon the database storage and sharing model and recent research examples that connect blockchain and database technology. Additionally, the paper investigates the blockchain data structure and storage path by analyzing the spatial–temporal correlation properties of blockchain data. The findings demonstrate that increasing the number of network nodes in the database system within a reasonable range can reduce the overall execution time. Additionally, as time progresses, the query traffic of the system database continues to increase. The research has practical implications in the domains of databases and the Internet of Things.

A dataset of Uniswap daily transaction indices by network

Decentralized Finance (DeFi) is reshaping traditional finance by enabling direct transactions without intermediaries, creating a rich source of open financial data. Layer 2 (L2) solutions are emerging to enhance the scalability and efficiency of the DeFi ecosystem, surpassing Layer 1 (L1) systems. However, the impact of L2 solutions is still underexplored, mainly due to the lack of comprehensive transaction data indices for economic analysis. This study bridges that gap by analyzing over 50 million transactions from Uniswap, a major decentralized exchange, across both L1 and L2 networks. We created a set of daily indices from blockchain data on Ethereum, Optimism, Arbitrum, and Polygon, offering insights into DeFi adoption, scalability, decentralization, and wealth distribution. Additionally, we developed an open-source Python framework for calculating decentralization indices, making this dataset highly useful for advanced machine learning research. Our work provides valuable resources for data scientists and contributes to the growth of the intelligent Web3 ecosystem.

A Secure Data Sharing Model Utilizing Attribute-Based Signcryption in Blockchain Technology.

With the rapid development of the Internet of Things (IoT), the scope of personal data sharing has significantly increased, enhancing convenience in daily life and optimizing resource management. However, this also poses challenges related to data privacy breaches and holdership threats. Typically, blockchain technology and cloud storage provide effective solutions. Nevertheless, the centralized storage architecture of traditional cloud servers is susceptible to single points of failure, potentially leading to system outages. To achieve secure data sharing, access control, and verification auditing, we propose a data security sharing scheme based on blockchain technology and attribute-based encryption, applied within the InterPlanetary File System (IPFS). This scheme employs multi-agent systems and attribute-based signcryption algorithms to process data, thereby enhancing privacy protection and verifying data holdership. The encrypted data are then stored in the distributed IPFS, with the returned hash values and access control policies uploaded to smart contracts, facilitating automated fine-grained access control services. Finally, blockchain data auditing is performed to ensure data integrity and accuracy. The results indicate that this scheme is practical and effective compared to existing solutions.

An improved practical Byzantine fault tolerance algorithm for aggregating node preferences

Consensus algorithms play a critical role in maintaining the consistency of blockchain data, directly affecting the system’s security and stability, and are used to determine the binary consensus of whether proposals are correct. With the development of blockchain-related technologies, social choice issues such as Bitcoin scaling and main chain forks, as well as the proliferation of decentralized autonomous organization (DAO) applications based on blockchain technology, require consensus algorithms to reach consensus on a specific proposal among multiple proposals based on node preferences, thereby addressing the multi-value consensus problem. However, existing consensus algorithms, including Practical Byzantine Fault Tolerance (PBFT), do not support nodes expressing preferences. Instead, the proposal to reach consensus is directly decided by specific nodes, with other nodes merely verifying the proposal’s validity, which can easily result in monopolistic or dictatorial outcomes. In response, we proposed the Aggregating Preferences with Practical Byzantine Fault Tolerance (AP-PBFT) consensus algorithm, which allows nodes to express preferences for multiple proposals. AP-PBFT ensures the validity of consensus results through a consensus output protocol, and incentivizes nodes to act honestly during the consensus process by incentive mechanism. First, AP-PBFT leverages Verifiable Random Function to select both consensus nodes and a primary node from the candidates. The primary node gathers proposals, assembles them into a proposal package, and broadcasts it to other consensus nodes. The consensus nodes independently vote to express their preferences for different proposals in the package, execute the consensus output protocol to reach local consensus, and the primary node aggregates these results to form the global consensus. Once the global consensus is finalized, AP-PBFT evaluates node behavior based on the consensus output protocol, penalizes nodes that acted maliciously, and rewards those that adhered to the protocol. Additionally, nodes can interact and adopt different strategies while executing the consensus output protocol, which can influence the consensus outcome. Therefore, we established an evolutionary game model based on hypergraph to analyze these interactions. Theoretical analysis shows that the incentive mechanism in AP-PBFT effectively encourages nodes to honestly follow the consensus output protocol, ensuring that AP-PBFT satisfies the properties of consistency, validity, and termination. Finally, the simulation results demonstrate that the AP-PBFT algorithm possesses good scalability and the capability to handle dynamic changes in nodes, surpassing some mainstream consensus algorithms in terms of transaction throughput and consensus achievement time. Moreover, AP-PBFT can incentivize honest behavior among consensus nodes, thereby enhancing the reliability of consensus and strengthening the security of the network.

A Dynamic Trapdoor Redactable Blockchain Schemes

The permanent storage of blockchain data exposes certain security risks in practical applications, necessitating the ability to edit data in specific scenarios. The chameleon hash function, as a trapdoor one-way hash function, has emerged as a key technology for achieving redactable blockchains. However, existing redactable blockchain schemes based on chameleon hash function face security vulnerabilities related to trapdoor misuse and leakage. This paper proposes a dynamic trapdoor redactable blockchain schemes, utilizing a dynamic trapdoor chameleon hash (DTCH) that allows for the dynamic update of trapdoors while overcoming the inherent immutability of blockchain, thus reducing the risk of malicious nodes retaining the trapdoor long-term. The proposed scheme employs controllable randomness, where the trapdoor key automatically updates after a certain period or upon use. In this scenario, authorized nodes cannot successfully find hash collisions with old keys, effectively revoking their authorization. Consequently, only users with valid trapdoor keys can modify the data on the chain. Relevant security tests and experimental results indicate that the proposed scheme offers advantages in security and maintains good efficiency at various stages, achieving higher security guarantees with minimal computational cost. Received: 27 September 2024 | Revised: 5 November 2024 | Accepted: 4 December 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Shuqiao Li: Conceptualization, Methodology, Software, Validation, Formal analysis, Formal analysis, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Xiaoming Hu: Conceptualization, Methodology, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Yan Liu: Formal analysis, Formal analysis, Resources, Writing – review & editing, Visualization, Supervision. Shuangjie Bai: Validation, Formal analysis, Writing – review & editing, Visualization.

ABBDAC: A Novel Attribute-Based Blockchain Data Access Control Scheme in Cloud Environment

ABBDAC: A Novel Attribute-Based Blockchain Data Access Control Scheme in Cloud Environment

Predicting Blockchain Application Performance with Machine Learning Techniques

Blockchain technology is a distributed ledger designed to record all transactions within its network, characterized by its decentralized nature, resistance to tampering, and attributes such as consistency, anonymity, and traceability. However, evaluating blockchain applications' performance can be complex due to their intricate and distributed infrastructure. This research employs machine learning model-based methods to predict blockchain systems' performance using predetermined configuration parameters. The data used in this study is derived from a blockchain simulator, generating blockchain data to facilitate performance predictions. The simulation process involves using simulated data and configuration settings for each run, including parameters such as the number of nodes, the number of miners, consensus algorithm, maximum block size, and transaction quantities, among others. Output metrics such as the total number of blocks, transaction rate, block propagation time, and latency are utilized to assess network performance. The simulator was run 184 times with various configurations. Our findings indicate that the Random Forest model outperformed other models used in the experiments, achieving the highest R² scores for multiple metrics, such as 0.987 for total number of transactions and 0.765 for average block propagation time, while also demonstrating lower RMSE values, indicating more accurate predictions.

Decoding blockchain data for research in marketing: New insights through an analysis of share of wallet

Decoding blockchain data for research in marketing: New insights through an analysis of share of wallet

Fuzzy Neural Network for Detecting Anomalies in Blockchain Transactions

This publication focuses on the use of the artificial intelligence for detecting anomalies, especially in the blockchain network. The research methodology includes the selection of anomalies to be detected and the processing of blockchain data. Various artificial intelligence methods were implemented for anomaly detection as part of the tests, and one new solution—a Fuzzy Neural Network—was presented. The findings indicate the possibility of detecting selected anomalies in the blockchain using artificial intelligence, which is of significant importance for the security of this technology. The conclusions present a discussion on limitations, future research prospects, and guidelines for future work.

Examining market volatility arbitrage in cryptocurrencies with the perspective of Beldex coin trading dynamics in India

Cryptocurrency trading has gained significant adhesion in financial markets, making it essential to understand the factors influencing trading intentions. This study investigates the psychological and knowledge-based determinants of trading intentions towards Beldex coins among crypto traders in India. This study aims to evaluate how risk management, hedonic motivation, investment desire, market knowledge, peer participation, and earning desires impact trading intentions. A survey was conducted with 369 crypto traders in India, and multiple regression analysis was employed to analyze the data. The results indicate that all six factors significantly influence trading intentions, with risk management (β = 0.342, p < 0.001) and earning desires (β = 0.378, p < 0.001) having the strongest impact on Indian Cryptocurrency market arbitrage. The regression model explained 53% of the variance in trading intentions (R² = 0.53). Cryptocurrency market information is analyzed through the CoinGecko tool that provides charts, market capitalization, and blockchain data; multiple regression analysis is utilized to test the hypothesized relationships. This study reveals that traders’ investment decisions in cryptocurrencies are primarily driven by financial motivations, including potential high returns, diversification, and inflation hedging, as well as technological factors of decentralized finance, blockchain technology, and digitalized transactions. AcknowledgmentThe authors would like to convey their gratitude to Prof. Balakumar Pitchai, Director/Research, Training & Publications at the Office of Research & Development, Periyar Maniammai Institute of Science & Technology (Deemed to be University), India for his suggestions to improve the language of the manuscript.

M-Net based stacked autoencoder for ransomware detection using blockchain data

Ransomware is a kind of malevolent program software that encrypts the items on the hard disc and prevents the clients from accessing them until they are paid a ransom. Associations like monetary establishments and medical care areas (i.e., smart medical care) are mostly targeted by ransomware attacks. Ransomware assaults are crucial holes still in blockchain technology and prevent effective data communication in networks. This study aims to introduce an efficient system, named M-Net-based Stacked Autoencoder (M-Net_SA) for ransomware detection using blockchain data. Initially, the input data is taken from a dataset and then sent to the feature extraction process, which utilizes sequence-based statistical features. After that, data transformation is completed using the Yeo-Johnson transformation to transform the data into a usable format. After that, feature fusion is executed using a Deep Q-network (DQN) with Lorentzian similarity to enhance the representativeness of the target features. Finally, ransomware detection is accomplished by the proposed M-Net_SA, which is the integration of MobileNet and Deep Stacked Autoencoder (DSAE). The experimental validation of the proposed M-Net_SA is compared with other conventional techniques and the proposed model attained maximum accuracy, sensitivity, and specificity of 0.959, 0.967, and 0.957 respectively.

Stabilizing food supply chains with Blockchain technology during periods of economic inflation

This study explores the potential of blockchain technology to stabilize food supply chains during periods of economic inflation. Economic inflation poses significant challenges to food security, as rising prices can disrupt supply chain transparency, efficiency, and trust among stakeholders. This research develops a conceptual model demonstrating how blockchain can enhance transparency and stability in food supply chains, ultimately improving resilience against inflationary pressures. By providing a decentralized, immutable ledger, blockchain technology allows for real-time tracking of food products from farm to table, enabling better inventory management and demand forecasting. The proposed model integrates key components, including smart contracts, which automate transactions and ensure compliance with quality and safety standards. This feature not only reduces delays but also fosters trust among supply chain participants, such as farmers, distributors, retailers, and consumers. Additionally, the model emphasizes the role of data analytics in harnessing blockchain data to provide insights into pricing trends and inventory levels, facilitating proactive decision-making in response to inflation. Future research directions are identified, focusing on innovations within blockchain technology that could further mitigate the effects of inflation on food supply chains. Potential areas of exploration include integrating artificial intelligence (AI) for predictive analytics, utilizing Internet of Things (IoT) devices for real-time data collection, and developing decentralized finance (DeFi) solutions to enhance liquidity and investment in the agricultural sector. This study contributes to the growing body of literature on blockchain applications in supply chain management by highlighting its potential as a tool for enhancing food security in inflationary environments. By ensuring transparency, improving stakeholder collaboration, and providing actionable insights, blockchain technology can play a pivotal role in stabilizing food supply chains amidst economic fluctuations. Ultimately, this research advocates for the adoption of blockchain solutions as part of comprehensive strategies to safeguard food systems from the adverse impacts of inflation. Keywords: Blockchain Technology, Food Supply Chains, Economic Inflation, Transparency, Stability, Smart Contracts, Predictive Analytics, Data Analytics, Food Security, Decentralized Finance.

AI-Enhanced Blockchain for Scalable IoT-Based Supply Chain

Purpose: The integration of AI with blockchain technology is investigated in this study to address challenges in IoT-based supply chains, specifically focusing on latency, scalability, and data consistency. Background: Despite the potential of blockchain technology, its application in supply chains is hindered by significant limitations such as latency and scalability, which negatively impact data consistency and system reliability. Traditional solutions such as sharding, pruning, and off-chain storage introduce technical complexities and reduce transparency. Methods: This research proposes an AI-enabled blockchain solution, ABISChain, designed to enhance the performance of supply chains. The system utilizes beliefs, desires, and intentions (BDI) agents to manage and prune blockchain data, thus optimizing the blockchain’s performance. A particle swarm optimization method is employed to determine the most efficient dataset for pruning across the network. Results: The AI-driven ABISChain platform demonstrates improved scalability, data consistency, and security, making it a viable solution for supply chain management. Conclusions: The findings provide valuable insights for supply chain managers and technology developers, offering a robust solution that combines AI and blockchain to overcome existing challenges in IoT-based supply chains.

DC-SoC: Optimizing a Blockchain Data Dissemination Model Based on Density Clustering and Social Mechanisms

Due to its partially decentralized and highly scalable features, the consortium blockchain has currently overtaken other blockchain technologies as the one most frequently used and studied across various industries. However, performance issues such as low transaction efficiency and redundant communication processes continue to hinder the development of consortium blockchains. In the Hyperledger Fabric consortium blockchain system, transaction efficiency is largely influenced by the consensus protocol and broadcast protocol. This paper introduces a novel consortium blockchain network model, DC-SoC, focused on optimizing broadcast protocols. By incorporating the concept of density clustering, a stable propagation structure is established for the blockchain network, thus optimizing data dissemination in the Gossip protocol. Additionally, the concept of social networks is integrated, using trustworthiness scores and economic incentives to evaluate node security. The experimental results demonstrate that when DC-SoC is applied in a large-scale consortium blockchain environment, it significantly improves communication performance between nodes and ensures transmission reliability.

The determinants of funding liquidity risk in decentralized lending

Decentralized lending in the DeFi ecosystem mirrors traditional financial intermediation but poses significant risks, particularly funding liquidity risk, due to the volatility and composbility of digital assets, high leverage, and the absence of regulatory protections. This study applies traditional financial intermediation theories to DeFi lending and empirically test which internal factors such as interest rates and user market power, as well as external factors like the USD Index, influence funding liquidity risk in DeFi lending. Analyzing high-frequency blockchain data using the ARDL model and a novel dynamic ARDL simulation from major pools such as Wrapped Bitcoin (WBTC) and Wrapped Ethereum (WETH), the research finds that current algorithmic interest rate models fail to function as effective self-stabilization mechanisms. Additionally, lower deposit concentration in these pools may exacerbate, rather than mitigate, funding liquidity risk.

On the Suitability of Process Mining for Enhancing Transparency of Blockchain Applications

AbstractBlockchain technology is known for its transparency properties due to its publicly available, immutable data. Yet, as data availability does not inherently ensure transparency, further analytical methods may be required for human interpretation of data traces. Process mining has emerged as a popular toolbox for understanding processes and how they are executed in practice. The paper studies process mining as a method to enhance the transparency of blockchain data. To this end, two popular Ethereum applications were analyzed using process mining: the prediction and betting marketplace Augur and the network marketing platform Forsage. Observations from the process-mining analyses are used to discuss if process mining can serve as a method to establish transparency of a blockchain. For both applications, new insights are generated for usage scenarios such as application redesign, security analysis, user behavior analysis, and revealing blind spots in Augur’s and Forsage’s documentation. The paper concludes that there is evidence that process mining can serve as a method to enhance transparency in blockchains at the cost of technical setup and knowledge acquisition.

Skip index: Supporting efficient inter-block queries and query authentication on the blockchain

Decentralized applications, the driving force behind the new Web3 paradigm, require continuous access to blockchain data. Their adoption, however, is hindered by the constantly increasing size of blockchains and the sequential scan nature of their read operations, which introduce a clear inefficiency bottleneck. Also, the growing amount of data recorded on the blockchain makes resource-constrained light nodes dependent on untrusted full nodes for fetching information, with a consequent need for query authentication protocols ensuring result integrity. Motivated by these reasons, in this paper we propose the skip index, an indexing data structure that allows users to quickly retrieve information simultaneously from multiple blocks of a blockchain. Our solution is also designed to be used as an authenticated data structure to guarantee the integrity of query results for light nodes. We discuss the theoretical properties of skip indices, propose efficient algorithms for their construction and querying, and detail their computational complexity. Finally, we assess the effectiveness of our proposal through an experimental evaluation on the Ethereum blockchain. As a reference use case, we focus on the popular CryptoKitties application and simulate a scenario where users seek to retrieve the events generated by the service. Our experimental results suggest that the use of skip indices offers a constant multiplicative speedup, thanks to search times that are at most logarithmic within a chosen search window. This allows to reduce the number of visited blocks by up to two orders of magnitude if compared to the naive sequential approach currently in use.

Sustainable seafood: advances in traceability, assessment, monitoring and resource management

This review presents a synopsis of advances in some techniques and technologies that support seafood traceability while addressing some of the complexities currently challenging the integration necessary to coordinate commercial, national and international efforts in achieving seafood sustainability. These advances rely on tools such as blockchain data handling, eDNA or eRNA (together ‘eNA’) sampling, DNA metabarcoding, artificial intelligence and mobile applications. The microbiota associated with seafood organisms can also be monitored using eNA, to enable detection and management of any potential pathogenic developments in wild or aquaculture stocks. For successful and efficient traceability, it is necessary to guarantee data reliability and to support regulatory auditing. To ensure sustainability, however, also requires sampling, monitoring and assessments of the resource status as well as appropriate management. Effective traceability of seafood-related products will also help to resolve other problems, such as endangered species entering supply chains for aquaculture feed. Considering the still large proportion of seafood that is currently obtained and processed illegally, there is a pressing need for coherent local, national and international policies rooted in shared goals among stakeholders and supported by even-handed enforcement. This has been recently facilitated through a trend to shift liability from fishers to importers, and onwards throughout the value chain. In hand with these regulatory changes, the technologies described here will play a central role in achieving the vision for sustainable seafood supplies by the end of this decade.

Food provenance assurance and willingness to pay for blockchain data security: A case of Australian consumers

Blockchain technology has properties that improve supply chain transparency, traceability, and accountability, but how important are these security features to the consumer? This study investigates, consumers' willingness to choose and pay a premium for blockchain-certified food products. The major findings of this study are that consumers show positive receptiveness towards blockchain and disfavour unethical food production methods revealing sustainability consciousness guiding their consumption. We find that females place a greater value on food transparency and product labelling verification and are more willing to pay a premium. In addition, the results have important marketing implications according to our choice modeling findings.

Blockchain data asset management technology based on a two-way transaction algorithm

Blockchain data asset management technology based on a two-way transaction algorithm