Pub Date : 2025-01-02DOI: 10.1016/j.bcra.2024.100273
Xiaogang Wang , Chenhao Wang , Jie Yang , Jianhai Chen , Xiang Chen , Gang Li , Butian Huang , Shoujun Peng
In recent years, Building Information Modeling (BIM) has been widely used in the field of rail transit and plays an important role. Due to the numerous and complex elements of BIM file data, they are shared and used by multiple departments. Traditional BIMs that exist in the form of files are stored independently on centralized servers or local machines, lacking systematic management methods. Data update and maintenance are quite cumbersome. Besides, multi-departmental interaction and collaboration are inefficient and untrustworthy. Therefore, we propose a blockchain-based trusted sharing method for rail transit BIM data. First, a blockchain-based distributed BIM data sharing system architecture is proposed. Second, smart contracts are designed to achieve on-chain protection and shared use of digital resources such as rail transit BIM models. Third, the BIM data access control mechanism based on attribute-based encryption is proposed, and we implement a prototype of a trusted shared access system with permission control for multiple departments based on the InterPlanetary File System (IPFS) and the security mechanisms of Software Guard Extensions (SGX). Finally, the feasibility of the method is verified through scheme comparison, security analysis, and prototype system performance testing.
{"title":"A blockchain-based trusted sharing method for railway transportation BIM data","authors":"Xiaogang Wang , Chenhao Wang , Jie Yang , Jianhai Chen , Xiang Chen , Gang Li , Butian Huang , Shoujun Peng","doi":"10.1016/j.bcra.2024.100273","DOIUrl":"10.1016/j.bcra.2024.100273","url":null,"abstract":"<div><div>In recent years, Building Information Modeling (BIM) has been widely used in the field of rail transit and plays an important role. Due to the numerous and complex elements of BIM file data, they are shared and used by multiple departments. Traditional BIMs that exist in the form of files are stored independently on centralized servers or local machines, lacking systematic management methods. Data update and maintenance are quite cumbersome. Besides, multi-departmental interaction and collaboration are inefficient and untrustworthy. Therefore, we propose a blockchain-based trusted sharing method for rail transit BIM data. First, a blockchain-based distributed BIM data sharing system architecture is proposed. Second, smart contracts are designed to achieve on-chain protection and shared use of digital resources such as rail transit BIM models. Third, the BIM data access control mechanism based on attribute-based encryption is proposed, and we implement a prototype of a trusted shared access system with permission control for multiple departments based on the InterPlanetary File System (IPFS) and the security mechanisms of Software Guard Extensions (SGX). Finally, the feasibility of the method is verified through scheme comparison, security analysis, and prototype system performance testing.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 3","pages":"Article 100273"},"PeriodicalIF":5.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-02DOI: 10.1016/j.bcra.2024.100271
Kejia Chen , Jiawen Zhang , Xuanming Liu , Zunlei Feng , Xiaohu Yang
Federated learning (FL) is predicated on the provision of high-quality data by multiple clients, which is then used to train global models. A plethora of incentive mechanism studies have been conducted with the objective of promoting the provision of high-quality data by clients. These studies have focused on the distribution of benefits to clients. However, the incentives of federated learning are transactional in nature, and the issue of the atomicity of transactions has not been addressed. Furthermore, the data quality of individual clients participating in training varies, and they may participate negatively in training out of privacy leakage concerns.
Consequently, we propose an inaugural atomistic incentive scheme with privacy preservation in the FL setting: πFL (privacy, atomic, incentive). This scheme establishes a more dependable training environment based on Shapley valuation, secure multi-party computation, and smart contracts. Consequently, it ensures that each client's contribution can be accurately measured and appropriately rewarded, improves the accuracy and efficiency of model training, and enhances the sustainability and reliability of the FL system. The efficacy of this mechanism has been demonstrated through comprehensive experimental analysis. It is evident that this mechanism not only protects the privacy of trainers and provides atomic training rewards but also improves the model performance of FL, with an accuracy improvement of at least 8%.
{"title":"πFL: Private, atomic, incentive mechanism for federated learning based on blockchain","authors":"Kejia Chen , Jiawen Zhang , Xuanming Liu , Zunlei Feng , Xiaohu Yang","doi":"10.1016/j.bcra.2024.100271","DOIUrl":"10.1016/j.bcra.2024.100271","url":null,"abstract":"<div><div>Federated learning (FL) is predicated on the provision of high-quality data by multiple clients, which is then used to train global models. A plethora of incentive mechanism studies have been conducted with the objective of promoting the provision of high-quality data by clients. These studies have focused on the distribution of benefits to clients. However, the incentives of federated learning are transactional in nature, and the issue of the atomicity of transactions has not been addressed. Furthermore, the data quality of individual clients participating in training varies, and they may participate negatively in training out of privacy leakage concerns.</div><div>Consequently, we propose an inaugural atomistic incentive scheme with privacy preservation in the FL setting: <em>π</em>FL (<strong>p</strong>rivacy, <strong>a</strong>tomic, <strong>i</strong>ncentive). This scheme establishes a more dependable training environment based on Shapley valuation, secure multi-party computation, and smart contracts. Consequently, it ensures that each client's contribution can be accurately measured and appropriately rewarded, improves the accuracy and efficiency of model training, and enhances the sustainability and reliability of the FL system. The efficacy of this mechanism has been demonstrated through comprehensive experimental analysis. It is evident that this mechanism not only protects the privacy of trainers and provides atomic training rewards but also improves the model performance of FL, with an accuracy improvement of at least 8%.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 2","pages":"Article 100271"},"PeriodicalIF":6.9,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increasing popularity of cryptocurrencies and blockchain technologies, smart contracts have become a prominent feature in developing decentralized applications. However, these smart contracts are susceptible to vulnerabilities that hackers can exploit, resulting in significant financial losses. In response to this growing concern, various initiatives have emerged. Notably, the Smart Contract Weakness Classification (SWC) list plays an important role in raising awareness and understanding of smart contract weaknesses. However, the SWC list lacks maintenance and has not been updated with new vulnerabilities since 2020. To address this gap, this paper introduces the Smart Contract Weakness Enumeration (SWE), a comprehensive and practical vulnerability list up until 2023. We collect 273 vulnerability descriptions from 86 top conference papers and journal papers, employing the open card-sorting method to deduplicate and categorize these descriptions. This process results in the identification of 40 common contract weaknesses, which are further classified into 20 sub-research fields through thorough discussion and analysis. The SWE provides a systematic and comprehensive list of smart contract vulnerabilities, covering existing and emerging vulnerabilities in the last few years. Moreover, the SWE is a scalable and continuously iterative program. We propose two update mechanisms for the maintenance of the SWE. Regular updates involve the inclusion of new vulnerabilities from future top papers, while irregular updates enable individuals to report new weaknesses for review and potential addition to the SWE.
{"title":"To healthier Ethereum: a comprehensive and iterative smart contract weakness enumeration","authors":"Jiachi Chen, Mingyuan Huang, Zewei Lin, Peilin Zheng, Zibin Zheng","doi":"10.1016/j.bcra.2024.100258","DOIUrl":"10.1016/j.bcra.2024.100258","url":null,"abstract":"<div><div>With the increasing popularity of cryptocurrencies and blockchain technologies, smart contracts have become a prominent feature in developing decentralized applications. However, these smart contracts are susceptible to vulnerabilities that hackers can exploit, resulting in significant financial losses. In response to this growing concern, various initiatives have emerged. Notably, the Smart Contract Weakness Classification (SWC) list plays an important role in raising awareness and understanding of smart contract weaknesses. However, the SWC list lacks maintenance and has not been updated with new vulnerabilities since 2020. To address this gap, this paper introduces the Smart Contract Weakness Enumeration (SWE), a comprehensive and practical vulnerability list up until 2023. We collect 273 vulnerability descriptions from 86 top conference papers and journal papers, employing the open card-sorting method to deduplicate and categorize these descriptions. This process results in the identification of 40 common contract weaknesses, which are further classified into 20 sub-research fields through thorough discussion and analysis. The SWE provides a systematic and comprehensive list of smart contract vulnerabilities, covering existing and emerging vulnerabilities in the last few years. Moreover, the SWE is a scalable and continuously iterative program. We propose two update mechanisms for the maintenance of the SWE. Regular updates involve the inclusion of new vulnerabilities from future top papers, while irregular updates enable individuals to report new weaknesses for review and potential addition to the SWE.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 2","pages":"Article 100258"},"PeriodicalIF":6.9,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-27DOI: 10.1016/j.bcra.2024.100253
Sepideh HajiHosseinKhani , Arash Habibi Lashkari , Ali Mizani Oskui
With the advent of blockchain networks, there has been a transition from traditional contracts to Smart Contracts (SCs), which are crucial for maintaining trust within these networks. Previous methods for analyzing SCs vulnerabilities typically suffer from a lack of accuracy and effectiveness. Many of them, such as rule-based methods, machine learning techniques, and neural networks, also struggle to detect complex vulnerabilities due to limited data availability. This study introduces a novel approach to detecting, identifying, and profiling SC vulnerabilities, comprising two key components: an updated analyzer named SCsVulLyzer (V2.0) and an advanced Genetic Algorithm (GA) profiling method. The analyzer extracts 240 features across different categories, while the enhanced GA, explicitly designed for profiling SC vulnerabilities, employs techniques such as penalty fitness function, retention of elites, and adaptive mutation rate to create a detailed profile for each vulnerability. Furthermore, due to the lack of comprehensive validation and evaluation datasets with sufficient samples and diverse vulnerabilities, this work introduces a new dataset named BCCC-SCsVul-2024. This dataset consists of 111,897 Solidity source code samples, ensuring the practical validation of the proposed approach. Additionally, three types of taxonomies are established, covering SC literature review, profiling techniques, and feature extraction. These taxonomies offer a systematic classification and analysis of information, enhancing the efficiency of the proposed profiling technique. Our proposed approach demonstrated superior capabilities with higher precision and accuracy through rigorous testing and experimentation. It not only showed excellent results for evaluation parameters but also proved highly efficient in terms of time and space complexity. Moreover, the concept of the profiling technique makes our model highly transparent and explainable. These promising results highlight the potential of GA-based profiling to improve the detection and identification of SC vulnerabilities, contributing to enhanced security in blockchain networks.
{"title":"Unveiling smart contract vulnerabilities: Toward profiling smart contract vulnerabilities using enhanced genetic algorithm and generating benchmark dataset","authors":"Sepideh HajiHosseinKhani , Arash Habibi Lashkari , Ali Mizani Oskui","doi":"10.1016/j.bcra.2024.100253","DOIUrl":"10.1016/j.bcra.2024.100253","url":null,"abstract":"<div><div>With the advent of blockchain networks, there has been a transition from traditional contracts to Smart Contracts (SCs), which are crucial for maintaining trust within these networks. Previous methods for analyzing SCs vulnerabilities typically suffer from a lack of accuracy and effectiveness. Many of them, such as rule-based methods, machine learning techniques, and neural networks, also struggle to detect complex vulnerabilities due to limited data availability. This study introduces a novel approach to detecting, identifying, and profiling SC vulnerabilities, comprising two key components: an updated analyzer named SCsVulLyzer (V2.0) and an advanced Genetic Algorithm (GA) profiling method. The analyzer extracts 240 features across different categories, while the enhanced GA, explicitly designed for profiling SC vulnerabilities, employs techniques such as penalty fitness function, retention of elites, and adaptive mutation rate to create a detailed profile for each vulnerability. Furthermore, due to the lack of comprehensive validation and evaluation datasets with sufficient samples and diverse vulnerabilities, this work introduces a new dataset named BCCC-SCsVul-2024. This dataset consists of 111,897 Solidity source code samples, ensuring the practical validation of the proposed approach. Additionally, three types of taxonomies are established, covering SC literature review, profiling techniques, and feature extraction. These taxonomies offer a systematic classification and analysis of information, enhancing the efficiency of the proposed profiling technique. Our proposed approach demonstrated superior capabilities with higher precision and accuracy through rigorous testing and experimentation. It not only showed excellent results for evaluation parameters but also proved highly efficient in terms of time and space complexity. Moreover, the concept of the profiling technique makes our model highly transparent and explainable. These promising results highlight the potential of GA-based profiling to improve the detection and identification of SC vulnerabilities, contributing to enhanced security in blockchain networks.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 2","pages":"Article 100253"},"PeriodicalIF":6.9,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1016/j.bcra.2024.100241
Francesco Donini , Alessandro Marcelletti , Andrea Morichetta , Andrea Polini
In Service Oriented Computing (SOC), different services interact and exchange information to reach specific objectives. To model interorganizational SOC systems, choreography modeling languages have emerged to represent the distributed coordination among the involved organizations. From the realization perspective, blockchain technology is emerging as a promising run-time supporting peer-to-peer communication technology without the need for a central coordinator, thanks to its intrinsic security, trust, and decentralization characteristics. However, while blockchain can bring many advantages, technological barriers still limit its adoption in organizations, due to the costly and time-consuming learning process. For this reason, we propose RESTChain, a framework that automatically enables the interactions that take place among the participants in a service choreography exploiting blockchain technology. Starting from a choreography specification, the framework provides a set of mediators and automatically generates a smart contract that coordinates the service interactions. The mediators are software components that are directly connected with the smart contracts and expose REpresentational State Transfer (REST) APIs in compliance with the role played by the organizations in the choreography. In this way, the services deployed by one organization can communicate with the services made available by another organization through the blockchain in a secure and transparent manner. The proposed approach has been implemented on the Layer 2 Polygon blockchain and validated in a market retail case study analyzing its efficiency in terms of time and cost.
在面向服务的计算（SOC）中，不同的服务相互作用并交换信息以达到特定的目标。为了对组织间的SOC系统进行建模，出现了编排建模语言来表示相关组织之间的分布式协调。从实现的角度来看，区块链技术正在成为一种有前途的运行时支持点对点通信技术，而不需要中央协调器，这得益于其固有的安全性、信任度和去中心化特征。然而，尽管区块链可以带来许多优势，但由于学习过程昂贵且耗时，技术障碍仍然限制了它在组织中的采用。出于这个原因，我们提出RESTChain，这是一个框架，它可以自动启用利用区块链技术的服务编排参与者之间发生的交互。从编排规范开始，框架提供了一组中介，并自动生成协调服务交互的智能合约。中介是与智能合约直接连接的软件组件，并根据组织在编排中所扮演的角色公开REpresentational State Transfer (REST) api。这样，一个组织部署的服务就可以通过区块链以安全和透明的方式与另一个组织提供的服务进行通信。该方法已在第2层多边形区块链上实现，并在市场零售案例研究中进行了验证，分析了其在时间和成本方面的效率。
{"title":"Coordinating REST interactions in service choreographies using blockchain","authors":"Francesco Donini , Alessandro Marcelletti , Andrea Morichetta , Andrea Polini","doi":"10.1016/j.bcra.2024.100241","DOIUrl":"10.1016/j.bcra.2024.100241","url":null,"abstract":"<div><div>In Service Oriented Computing (SOC), different services interact and exchange information to reach specific objectives. To model interorganizational SOC systems, choreography modeling languages have emerged to represent the distributed coordination among the involved organizations. From the realization perspective, blockchain technology is emerging as a promising run-time supporting peer-to-peer communication technology without the need for a central coordinator, thanks to its intrinsic security, trust, and decentralization characteristics. However, while blockchain can bring many advantages, technological barriers still limit its adoption in organizations, due to the costly and time-consuming learning process. For this reason, we propose RESTChain, a framework that automatically enables the interactions that take place among the participants in a service choreography exploiting blockchain technology. Starting from a choreography specification, the framework provides a set of mediators and automatically generates a smart contract that coordinates the service interactions. The mediators are software components that are directly connected with the smart contracts and expose REpresentational State Transfer (REST) APIs in compliance with the role played by the organizations in the choreography. In this way, the services deployed by one organization can communicate with the services made available by another organization through the blockchain in a secure and transparent manner. The proposed approach has been implemented on the Layer 2 Polygon blockchain and validated in a market retail case study analyzing its efficiency in terms of time and cost.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"6 1","pages":"Article 100241"},"PeriodicalIF":6.9,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143100649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.bcra.2024.100223
Desh Deepak Sharma , S.N. Singh , Jeremy Lin
In the networked enlarged electric vehicle (EV) charging infrastructures, the security and authenticity of the stakeholders involved in the EV energy market pool are prime important. This paper proposes an EV network hub (EVNH) comprising EVs, EV aggregators (EVAs), and charging nodes in the networked EV energy market pool. The various EVAs implement different heterogeneous blockchains. The EVNH facilitates blockchain-based secure and resilient energy trading under grid to vehicle and vehicle to grid systems. The paper emphasizes interoperability challenges involving different blockchains to communicate and transfer assets or data between them. We suggest secure and trustworthy energy trading across various EVAs using multiple EV tokens for EV energy trading through cross-chain communications. The EVAs consider a Nash equilibrium-seeking strategy to find the Nash equilibrium in the noncooperative game of EVAs. The effectiveness of the proposed EVNH is tested using MATLAB, Solidity, and Python software.
{"title":"Blockchain-enabled secure and authentic Nash equilibrium strategies for heterogeneous networked hub of electric vehicle charging stations","authors":"Desh Deepak Sharma , S.N. Singh , Jeremy Lin","doi":"10.1016/j.bcra.2024.100223","DOIUrl":"10.1016/j.bcra.2024.100223","url":null,"abstract":"<div><div>In the networked enlarged electric vehicle (EV) charging infrastructures, the security and authenticity of the stakeholders involved in the EV energy market pool are prime important. This paper proposes an EV network hub (EVNH) comprising EVs, EV aggregators (EVAs), and charging nodes in the networked EV energy market pool. The various EVAs implement different heterogeneous blockchains. The EVNH facilitates blockchain-based secure and resilient energy trading under grid to vehicle and vehicle to grid systems. The paper emphasizes interoperability challenges involving different blockchains to communicate and transfer assets or data between them. We suggest secure and trustworthy energy trading across various EVAs using multiple EV tokens for EV energy trading through cross-chain communications. The EVAs consider a Nash equilibrium-seeking strategy to find the Nash equilibrium in the noncooperative game of EVAs. The effectiveness of the proposed EVNH is tested using MATLAB, Solidity, and Python software.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 4","pages":"Article 100223"},"PeriodicalIF":6.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The last few years have witnessed the widespread use of blockchain technology in several works because of its effectiveness in terms of privacy, security, and trustworthiness. However, the challenges of cyber-attacks represent a real threat to systems based on this technology. The resort to the systems of anomaly detection focused on deep learning, also called deep anomaly detection, is an appropriate and efficient means to tackle cyber-attacks on the blockchain. This paper provides an overview of the blockchain technology concept, including its characteristics, challenges and limitations, and its system taxonomy. Numerous blockchain cyber-attacks are discussed, such as 51% attacks, selfish mining attacks, double spending attacks, and Sybil attacks. Furthermore, we survey an overview of deep anomaly detection systems with their challenges and unresolved issues. In addition, this article gives a glimpse of various deep learning approaches implemented for anomaly detection in the blockchain environment and presents several methods that enhance the security features of anomaly detection systems. Finally, we discuss the benefits and drawbacks of these recent advanced approaches in light of three categories—discriminative learning, generative learning, and hybrid learning—with other methods based on graphs, and we highlight the ability of the proposed approaches to perform real-time anomaly detection.
{"title":"A review on deep anomaly detection in blockchain","authors":"Oussama Mounnan , Otman Manad , Larbi Boubchir , Abdelkrim El Mouatasim , Boubaker Daachi","doi":"10.1016/j.bcra.2024.100227","DOIUrl":"10.1016/j.bcra.2024.100227","url":null,"abstract":"<div><div>The last few years have witnessed the widespread use of blockchain technology in several works because of its effectiveness in terms of privacy, security, and trustworthiness. However, the challenges of cyber-attacks represent a real threat to systems based on this technology. The resort to the systems of anomaly detection focused on deep learning, also called deep anomaly detection, is an appropriate and efficient means to tackle cyber-attacks on the blockchain. This paper provides an overview of the blockchain technology concept, including its characteristics, challenges and limitations, and its system taxonomy. Numerous blockchain cyber-attacks are discussed, such as 51% attacks, selfish mining attacks, double spending attacks, and Sybil attacks. Furthermore, we survey an overview of deep anomaly detection systems with their challenges and unresolved issues. In addition, this article gives a glimpse of various deep learning approaches implemented for anomaly detection in the blockchain environment and presents several methods that enhance the security features of anomaly detection systems. Finally, we discuss the benefits and drawbacks of these recent advanced approaches in light of three categories—discriminative learning, generative learning, and hybrid learning—with other methods based on graphs, and we highlight the ability of the proposed approaches to perform real-time anomaly detection.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 4","pages":"Article 100227"},"PeriodicalIF":6.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Blockchain technology has garnered substantial interest due to its capacity to transform numerous industries by amplifying transparency and bolstering security measures. Despite the increasing interest, there is a void in existing literature regarding the alignment of actors with the Diffusion of Innovation (DOI) principles in the context of blockchain adoption. This gap restricts comprehension of the factors influencing adoption. This research addresses this void by investigating how actors align with the DOI principles in making decisions about blockchain adoption. The DOI model is combined with the innovation translation concept derived from Actor-Network Theory (ANT) to explore these complex dynamics in more detail. The results indicate that the decision-making process for blockchain adoption corresponds to the knowledge, persuasion, and decision stages, mirroring the phases found in the innovation translation approach. This research offers theoretical insights and practical knowledge that can be beneficial to individuals and organisations looking to promote the successful implementation of blockchain technology.
{"title":"Navigating blockchain adoption: An examination of actor alignment with the Diffusion of Innovation principles","authors":"Shipra Chhina , Mehmood Chadhar , Selena Firmin , Arthur Tatnall","doi":"10.1016/j.bcra.2024.100228","DOIUrl":"10.1016/j.bcra.2024.100228","url":null,"abstract":"<div><div>Blockchain technology has garnered substantial interest due to its capacity to transform numerous industries by amplifying transparency and bolstering security measures. Despite the increasing interest, there is a void in existing literature regarding the alignment of actors with the Diffusion of Innovation (DOI) principles in the context of blockchain adoption. This gap restricts comprehension of the factors influencing adoption. This research addresses this void by investigating how actors align with the DOI principles in making decisions about blockchain adoption. The DOI model is combined with the innovation translation concept derived from Actor-Network Theory (ANT) to explore these complex dynamics in more detail. The results indicate that the decision-making process for blockchain adoption corresponds to the knowledge, persuasion, and decision stages, mirroring the phases found in the innovation translation approach. This research offers theoretical insights and practical knowledge that can be beneficial to individuals and organisations looking to promote the successful implementation of blockchain technology.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 4","pages":"Article 100228"},"PeriodicalIF":6.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to recent fluctuations in cryptocurrency prices, Ethereum has gained recognition as an investment asset. Given its volatile nature, there is a significant demand for accurate predictions to guide investment choices. This paper examines the most influential features of the daily price trends of Ethereum using a novel approach that combines the Random Forest classifier and the ReliefF method. Integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Short-Time Fourier Transform (STFT) results in high accuracy and performance metrics for Ethereum price trend predictions. This method stands out from prior research, primarily based on time series analysis, by enhancing pattern recognition across time and frequency domains. This adaptability leads to better prediction capabilities with accuracy reaching 76.56% in a highly chaotic market such as cryptocurrency. The STFT's ability to reveal cyclical trends in Ethereum's price provides valuable insights for the ANFIS model, leading to more precise predictions and addressing a notable gap in cryptocurrency research. Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.
{"title":"Data-driven price trends prediction of Ethereum: A hybrid machine learning and signal processing approach","authors":"Ebenezer Fiifi Emire Atta Mills , Yuexin Liao , Zihui Deng","doi":"10.1016/j.bcra.2024.100231","DOIUrl":"10.1016/j.bcra.2024.100231","url":null,"abstract":"<div><div>Due to recent fluctuations in cryptocurrency prices, Ethereum has gained recognition as an investment asset. Given its volatile nature, there is a significant demand for accurate predictions to guide investment choices. This paper examines the most influential features of the daily price trends of Ethereum using a novel approach that combines the Random Forest classifier and the ReliefF method. Integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Short-Time Fourier Transform (STFT) results in high accuracy and performance metrics for Ethereum price trend predictions. This method stands out from prior research, primarily based on time series analysis, by enhancing pattern recognition across time and frequency domains. This adaptability leads to better prediction capabilities with accuracy reaching 76.56% in a highly chaotic market such as cryptocurrency. The STFT's ability to reveal cyclical trends in Ethereum's price provides valuable insights for the ANFIS model, leading to more precise predictions and addressing a notable gap in cryptocurrency research. Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 4","pages":"Article 100231"},"PeriodicalIF":6.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Renewable energy projects, particularly wind and solar farms, have garnered significant attention as a potential solution to global energy challenges. Despite the energy production obstacles, the steady availability of fossil fuels continues to compete due to established distribution systems, as societies increasingly rely on electricity. Hydrogen fuel has emerged as a promising avenue for energy production, storage, and distribution, involving converting surplus renewable electricity into hydrogen through electrolysis, storing it, and distributing it. Our novel approach leverages blockchain technology to enhance the efficiency and traceability of hydrogen fuel production, offering a unique synergy of transparency, security, and decentralized governance. We showcase its viability and effectiveness using the ERC-1155 token standard to tokenize renewable resources and convert them into hydrogen fuel. Within our tokenized fuel blockchain architecture, we simulate the forecasted growth in hydrogen production and vehicle demand, highlighting our approach's efficiency, traceability, and transparency. This integration showcases the potential for a sustainable hydrogen fuel ecosystem. The gas consumption data analysis indicates that the daily gas consumption remains below 194 million Gas for refilling 3245 vehicles (including the cost of one-time contract deployment), demonstrating the feasibility and efficiency of our approach.
{"title":"Blockchain-enhanced hydrogen fuel production and distribution for sustainable energy management","authors":"Yash Madhwal , Yury Yanovich , Matteo Coveri , Ninoslav Marina","doi":"10.1016/j.bcra.2024.100229","DOIUrl":"10.1016/j.bcra.2024.100229","url":null,"abstract":"<div><div>Renewable energy projects, particularly wind and solar farms, have garnered significant attention as a potential solution to global energy challenges. Despite the energy production obstacles, the steady availability of fossil fuels continues to compete due to established distribution systems, as societies increasingly rely on electricity. Hydrogen fuel has emerged as a promising avenue for energy production, storage, and distribution, involving converting surplus renewable electricity into hydrogen through electrolysis, storing it, and distributing it. Our novel approach leverages blockchain technology to enhance the efficiency and traceability of hydrogen fuel production, offering a unique synergy of transparency, security, and decentralized governance. We showcase its viability and effectiveness using the ERC-1155 token standard to tokenize renewable resources and convert them into hydrogen fuel. Within our tokenized fuel blockchain architecture, we simulate the forecasted growth in hydrogen production and vehicle demand, highlighting our approach's efficiency, traceability, and transparency. This integration showcases the potential for a sustainable hydrogen fuel ecosystem. The gas consumption data analysis indicates that the daily gas consumption remains below 194 million Gas for refilling 3245 vehicles (including the cost of one-time contract deployment), demonstrating the feasibility and efficiency of our approach.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"5 4","pages":"Article 100229"},"PeriodicalIF":6.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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