【Author】 Khashan, Osama A.
CCF-C
【影响因子】8.139
【主题类别】
区块链技术-协同技术-机器学习
【Abstract】In wireless sensor networks (WSNs), the presence of malicious nodes (MNs) poses significant challenges to data integrity, network stability, and system reliability. These issues are intensified by energy resource constraints and limitations within centralized authentication systems, necessitating an energy-efficient solution to ensure realtime responsiveness. Although artificial intelligence-driven approaches enhance detection capabilities, they overcome challenges related to data volume, coordination overhead, and latency in centralized control. This study introduces blockchain-machine learning (BC-ML), a novel hybrid model that seamlessly integrates blockchain and machine learning (ML) techniques to effectively identify MNs in WSNs. The model establishes an energy-efficient blockchain among cluster heads (CHs) for robust node authentication, incorporating a Schnorrlike zero-knowledge-proof technique to validate node data during communication initiation. Utilizing a hybrid lightweight approach with both symmetric and asymmetric ciphers enhances the security of node data transmission. A new proof-of-authority method is introduced, which leverages node digital certificates instead of conventional data transactions. This consensus mechanism reduces the processing overhead associated with larger data sizes in traditional proof-of-work methods, thereby improving both energy efficiency and scalability. To address dataset imbalances, the model employs a hybrid unsupervised ML technique, combining adaptive synthetic sampling with a convolutional neural network for efficient analysis of nodes and network features. The ML model, hosted on a robust data server, ensures ongoing oversight by updating CHs with security levels for detected MNs, thereby reducing storage and mitigating coordination challenges. Comprehensive analyses validate the effectiveness of the BC-ML model for detecting MNs, optimizing resource utilization, minimizing delays, and prolonging node and network lifetimes. Security analysis further confirms the ability of the model to mitigate diverse attacks and meet the stringent WSN security requirement.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Blockchain; Lightweight encryption; Machine learning (ML); Malicious node detection; Proof-of-authority (PoA); Wireless sensor network (WSN)
【发表时间】2024
【收录时间】2024-10-18
【文献类型】 理论模型
【Author】 Jirou, Ismail Jebabli, Ikram Lahiani, Amine
【影响因子】6.143
【主题类别】
区块链治理-技术治理-交易预测
【Abstract】This study introduces a finetuned hybrid forecasting model combining both Discrete Wavelet Transform (DWT) and Long Short-Term Memory network (LSTM) to predict dirty and clean cryptocurrency returns (Bitcoin and Ripple). The findings show that the proposed DWT-LSTM model outperforms a large set of benchmark models in terms of forecasting accuracy. We investigate a broader set of predictors involving financial markets (other cryptocurrencies and commodities) and external variables (blockchain information, Twitter economic uncertainty, and CO2 emissions). Our findings underline the comparable performance of the considered predictors, with the Twitter Economic Uncertainty index being the best predictor of Bitcoin returns and S&P GSCI Energy being the best predictor of Ripple returns. We also highlight the superior performance of the trading strategies based on our forecasting results.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Forecasting; Financial markets; Blockchain information; Twitter economic uncertainty; CO2 emissions
【发表时间】2025
【收录时间】2024-10-18
【文献类型】 案例研究
CCF-A
【影响因子】6.075
【主题类别】
区块链应用-虚拟经济-元宇宙
【Abstract】The development of the mobile Metaverse has garnered increasing attention in the next-generation Internet, fueled by the rapid advancements of mobile Internet, communication, and computing technologies. With the resource limitations faced by mobile Metaverse users (MUs), the mobile Metaverse market is flourishing. This market enables MUs to access high-quality immersive experiences by trading resources with Metaverse service providers (MSPs) across geographically distributed resource pools. However, the mobile Metaverse market still faces several challenges, including the hierarchical mobile Metaverse service structure, temporal dependencies, and heterogeneous incentive mechanisms. To address these problems, this paper proposes a dual-level resources trading approach for mobile Metaverse based on blockchain. This approach employs a dual-level structure consisting of resource provisioning and heterogeneous auction mechanisms. Specifically, we formulate the resource provisioning as a temporal-dependent average delay minimization problem at the low level. To solve this low-level problem, we introduce a novel algorithm called LyDif, which leverages Lyapunov optimization techniques and diffusion models. At the high level, we propose a price-guided double dutch auction (PG-DDA) mechanism to match heterogeneous resources and determine pricing strategies. The PG-DDA smart contract is deployed on a consortium blockchain platform, facilitating resource trading management and transaction monitoring. Based on a real trace of edge-cloud service requests, our experimental results demonstrate the effectiveness of our proposed scheme in achieving optimal latency and social welfare.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Blockchain; double auction; mobile metaverse; resource provisioning; Blockchain; double auction; mobile metaverse; resource provisioning
【发表时间】2024
【收录时间】2024-10-18
【文献类型】 理论模型
【DOI】 10.1109/TMC.2024.3377211
【Author】 Hu, Tianyuan Li, Bixin Pan, Zhenyu Qian, Chen
CCF-C
【影响因子】5.883
【主题类别】
区块链治理-技术治理-智能合约漏洞检测
【Abstract】Smart contract security is one of the core issues in any application based on blockchain. There are many techniques focusing on smart contract security, however, due to the diversity of Solidity versions and limitations of detection time, it is difficult for them to comprehensively localize defects in different versions of smart contracts. In this article, we propose a static defect detection method based on the knowledge graph of the Solidity language and present a defect detection tool called SoliDetector. First, we define the ontology layer of the knowledge graph and construct the instance layer in which syntactic and logical relationships are captured. Second, we introduce the defect pattern to describe each defect and design inference rules to infer complex relationships and judge whether a defect exists. Finally, we localize defects by executing SPARQL queries. SoliDetector can support the detection of 20 kinds of defects and the automatic SPARQL query generation. We conducted several experiments on multiple datasets. SoliDetector obtains a high F-score (i.e., 92.97% on Dataset1 and 91.54% on the SmartBug dataset). To compare SoliDetector with SmartCheck, Slither, and Mythril, we conducted experiments on a labeled benchmark Dataset3 and real-world contracts. SoliDetector has a high F-score of 94.04% and is faster than other tools with an average time of 0.37 s for each contract.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Defect pattern; inference rule; knowledge graph; SPARQL query; smart contract
【发表时间】2024
【收录时间】2024-10-18
【文献类型】 理论模型
【DOI】 10.1109/TR.2023.3233999
【影响因子】4.996
【主题类别】
区块链应用-实体经济-供应链
【Abstract】The recent pandemic caused by COVID-19 is considered an unparalleled disaster in history. Developing a vaccine distribution network can provide valuable support to supply chain managers. Prioritizing the assigned available vaccines is crucial due to the limited supply at the final stage of the vaccine supply chain. In addition, parameter uncertainty is a common occurrence in a real supply chain, and it is essential to address this uncertainty in planning models. On the other hand, blockchain technology, being at the forefront of technological advancements, has the potential to enhance transparency within supply chains. Hence, in this study, we develop a new mathematical model for designing a COVID-19 vaccine supply chain network. In this regard, a multi-channel network model is designed to minimize total cost and maximize transparency with blockchain technology consideration. This addresses the uncertainty in supply, and a scenario-based multi-stage stochastic programming method is presented to handle the inherent uncertainty in multi-period planning horizons. In addition, fuzzy programming is used to face the uncertain price and quality of vaccines. Vaccine assignment is based on two main policies including age and population-based priority. The proposed model and method are validated and tested using a real-world case study of Iran. The optimum design of the COVID-19 vaccine supply chain is determined, and some comprehensive sensitivity analyses are conducted on the proposed model. Generally, results demonstrate that the multi-stage stochastic programming model meaningfully reduces the objective function value compared to the competitor model. Also, the results show that one of the efficient factors in increasing satisfied demand and decreasing shortage is the price of each type of vaccine and its agreement.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Multiple channels; COVID-19 vaccine supply chain; Fuzzy programming; Multi-stage stochastic programming; Blockchain technology
【发表时间】2024
【收录时间】2024-10-18
【文献类型】 案例研究
【Author】 Fiszeder, Piotr Malecka, Marta Molnar, Peter
【影响因子】3.875
【主题类别】
区块链治理-技术治理-交易预测
【Abstract】Traditional volatility models do not work well when volatility changes rapidly and in the presence of outliers. Therefore, two lines of improvements have been developed separately in the existing literature. Range-based models benefit from efficient volatility estimates based on low and high prices, while robust methods deal with outliers. We propose a range-based GARCH model with a bounded M-estimator, which combines these two improvements with a third new improvement: a modified robust method, which adds elasticity in treating the outliers. We apply this model to Bitcoin, Ethereum Classic, Ethereum, and Litecoin and find that it forecasts variances, value at risk, and expected shortfall more accurately than the standard GARCH model, the standard range-based GARCH model, and the GARCH model with the robust estimation. Utilization of high and low prices joined with a novel treatment of outliers makes our model perform well during extreme periods when traditional volatility models fail.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Cryptocurrency; Bitcoin; Volatility models; Value at risk; Expected shortfall; High-low range; Robust estimation; Outliers
【发表时间】2024
【收录时间】2024-10-18
【文献类型】 理论模型
【Author】 Bodo, Balazs de Filippi, Primavera
【影响因子】3.203
【主题类别】
区块链治理-法律治理-区块链监管制度
【Abstract】Trust is a key resource in financial transactions. Traditional financial institutions, and novel blockchain-based decentralized financial (DeFi) services rely on fundamentally different sources of trust and confidence. The former relies on heavy regulation, trusted intermediaries, clear rules (and restrictions) on market competition, and long-standing informal expectations on what banks and other financial intermediaries are supposed to do or not to do. The latter rely on blockchain technology to provide confidence in the outcome of rules encoded in protocols and smart contracts. Their main promise is to create confidence in the way the blockchain architecture enforces rules, rather than to trust banks, regulators, and markets. In this article, we compare the trust architectures surrounding these two financial systems. We provide a deeper analysis of how proposed regulation in the blockchain space affects the code- and confidence-based architectures which so far have underwrote DeFi. We argue that despite the solid safeguards and guarantees which code can offer, the confidence in DeFi is still very much dependent on more traditional trust-enhancing mechanisms, such as code governance, and antifraud regulation to address some of the issues which currently plague this domain, and which have no immediate, purely software-based solutions. What is more, given the risks of bugs or scams in the DeFi space, regulation and trusted intermediaries may need to play a more active role, in order for DeFi to gain the trust of the next generation of users.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】blockchain; decentralized finance; finance; institutional analysis; regulation; trust
【发表时间】2024
【收录时间】2024-10-18
【文献类型】 案例研究
【DOI】 10.1111/rego.12637
【Author】 Li, Yige
【影响因子】2.777
【主题类别】
区块链应用-实体经济-物联网
【Abstract】To explore the security performance of logistics data transmission in an Intelligent Logistics System (ILS) under the Internet of Things (IoT) and Blockchain (BC), IoT is introduced to optimize the logistics transportation process. First, BC is adopted to improve the ILS under IoT, and ILS under IoT combined with BC is established. Next, an analysis of the relationship between the information service provider and the transportation provider in the logistics transaction process is conducted to perform data analysis and to establish an incentive mechanism for logistics transportation under BC. Finally, the simulation analysis of the constructed model is implemented. The results reveal that the constructed model is highly efficient. It can effectively solve the traceability problem of logistics transaction information, and the calculation cost is stable within 1,000 ms. In the comparative analysis of system transmission performance, the model exhibits outstanding results in transmission latency, consistently maintaining an average delay of approximately 350 ms. This stability is primarily attributed to the seamless integration of BC technology, which optimizes data packet transmission paths and mitigates waiting time. Furthermore, the system efficiently manages high data volumes while upholding robust data processing capabilities, owing to the optimization of smart contracts and decentralized storage mechanisms. As for energy consumption, the model notably reduces overall energy usage within the logistics network by curtailing unnecessary data transmission and computation, particularly in mobile nodes and wireless communication channels. The proposed algorithm excels across various transmission metrics, including transmission delay, data successful acceptance rate, network throughput, and energy consumption are all shown to be the best transmission performance of the proposed algorithm, in which the data successful acceptance rate is close to 1. As a result, the developed ILS model not only guarantees low latency performance but also demonstrates high data transmission security, facilitating more efficient and precise real-time information transmission. In this study, within the ILS framework, the relationship between information service providers and transportation service providers is established through BC, and an incentive mechanism is constructed. However, some shortcomings in this incentive mechanism are recognized. For instance, during the initial stages of BC integration, there are issues of information asymmetry concerning factors such as credit, transportation capacity, and effort level. Moreover, the open access policy of couriers in the model raises concerns, as the access policy itself constitutes sensitive information, which may result in privacy breaches. Hence, as part of future work, the introduction of attribute encryption schemes is planned to obfuscate access policies, thereby safeguarding the privacy of logistics personnel's access policies.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】
【发表时间】2024
【收录时间】2024-10-18
【文献类型】 案例研究