【Author】 Meier, Oliver Gruchmann, Tim Ivanov, Dmitry

【Source】TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW

【影响因子】10.047

【主题类别】

区块链应用-实体经济-供应链

【Abstract】Achieving a circular economy (CE) is considered one of the most significant challenges of our time, as environmental challenges and social discrepancies keep increasing. While companies need to transform their supply chains towards sustainability and circularity, implementing CE concepts into practice is not straightforward and requires technical and non-technical capabilities, referring to dynamic capabilities. One of the most promising digital technologies for improving sustainability and circularity is blockchain. However, the literature on relationships between blockchain technology (BCT), sustainability, and the CE is still in its infancy. This study illustrates how BCT can help implement circular supply chain management (CSCM) from a dynamic capabilities perspective. A multiple case study approach examines how BCT's potential impacts CSCM. Building on the case evidence, necessary dynamic capabilities are deduced for successful CE implementations with BCT. The analysis demonstrates that supply chain traceability and related sensing capabilities are major benefits of BCT-driven CSCM. Thus, we contribute to theory by examining which dynamic capabilities need to be developed when realizing CSCM through BCT. The results indicate that the BCT potentials depend on the business model pointing to certain contingencies.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Circular supply chain; Circular economy; Blockchain; Sustainability; Dynamic capabilities

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 综述

【DOI】 10.1016/j.tre.2023.103177

【Author】 Shen, Zhuyi Wang, Shibo Yang, Jinqiang

【Source】FINANCE RESEARCH LETTERS

【影响因子】9.848

【主题类别】

区块链治理-市场治理-代币经济学

【Abstract】This paper employs the Pareto distribution to model participants' transaction types in the dynamic digital platform adoption and valuation theory. By closed-form solutions, we derive the analytical condition determining whether the tokenized economy exhibits faster adoption than the numeraire economy. We generalize the conclusion of Cong et al. (2021) that the user adoption is faster in the tokenized economy than in the numeraire economy because of endogenous appreciation expectation based on their numerical analysis. Conversely, when the volatility effect of platform productivity outweighs the growth effect, the adoption rate of the tokenized platform is slower due to depreciation expectation.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Cryptocurrency; Cryptocurrency Numeraire; Tokenomics

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 实证数据

【DOI】 10.1016/j.frl.2023.104047

【Author】 Fernandez-Fernandez, Adriana Coronado, Estefania Erspamer, Alberto Samaras, Georgios Theodorou, Vasileios Siddiqui, Shuaib

【Source】IEEE COMMUNICATIONS MAGAZINE

【影响因子】9.030

【主题类别】

区块链应用-实体经济-通信领域

【Abstract】6G systems are perceived to be heavily softwarized, and therefore, the collaboration among all stakeholders, such as network and cloud providers, operators, application developers, service providers and device and equipment vendors, shall occur via software in a secured, distributed, and automated manner. This could be envisioned with a brokerless and immutable marketplace anchored in distributed ledger technologies, removing the need for a trusted party as a marketplace operator while avoiding this single point of failure, increasing transparency and flexibility of operational rules, and featuring automated contract negotiation and fulfillment supporting governance, delivery, and billing operations. This work goes a step forward and introduces a smart resource and service discovery application, based on intent recognition and machine learning assisted techniques, to enable the automated discovery of resources and services exposed over the marketplace. Evaluation results demonstrate the accuracy of our approach and its suitability to provide decentralized telecom marketplaces with intelligent and data-driven discovery capabilities.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Stakeholders; Training data; Classification algorithms; Biological system modeling; Blockchains; 6G mobile communication; Grammar

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 实验仿真

【DOI】 10.1109/MCOM.002.2200266

【Author】 Rathee, Geetanjali Garg, Sahil Kaddoum, Georges Hassan, Mohammad Mehedi

【Source】ALEXANDRIA ENGINEERING JOURNAL

【影响因子】6.626

【主题类别】

区块链应用-实体经济-医疗领域

【Abstract】Emotional aware intelligent healthcare mechanism is seen as one of the emergent tech-niques that can be used to overcome the traditional communication issues like storage overhead and delay in transmission process in healthcare systems. It is a smart technique primarily used to ensure faster connectivity and transmission as well as healthcare data processing through various heterogeneous networks by understanding human emotions. Researchers have focused on various processing and transmitting techniques of emotional aware intelligent systems. However, very few of them have focused on the need for security while transmitting or reading the patients' record. The aim of this paper is to propose a secure and transparent communication mechanism in intelligent emotional aware systems using Analytical Hierarchical Process (AHP), blockchain system and a mathematical model. AHP and the mathematical model are used to ensure accuracy during the data transmission process by analysing the legitimacy of each device. In addition, the identity based trust and blockchain mechanism is used to ensure the continuous analysis and transparency among net-work entities while transferring the information. The proposed framework is further analysed against existing models over various security matrices such as data alteration, report generation, accuracy etc. The out performance of proposed mechanism is approximate 89% as compare to existing mechanism.& COPY; 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Intelligent systems; Emotion aware; Blockchain scheme; Blockchain-based healthcare; Security; Trusted intelligent

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 实验仿真

【DOI】 10.1016/j.aej.2023.06.002

【Author】 Iqbal, Faiza Altaf, Ayesha Waris, Zeest Aray, Daniel Gavilanes Flores, Miguel Angel Lopez Diez, Isabel de la Torre Ashraf, Imran

【Source】SENSORS

【影响因子】3.847

【主题类别】

区块链应用-实体经济-智能家居

【Abstract】Internet of Things (IoT) has made significant strides in energy management systems recently. Due to the continually increasing cost of energy, supply-demand disparities, and rising carbon footprints, the need for smart homes for monitoring, managing, and conserving energy has increased. In IoT-based systems, device data are delivered to the network edge before being stored in the fog or cloud for further transactions. This raises worries about the data's security, privacy, and veracity. It is vital to monitor who accesses and updates this information to protect IoT end-users linked to IoT devices. Smart meters are installed in smart homes and are susceptible to numerous cyber attacks. Access to IoT devices and related data must be secured to prevent misuse and protect IoT users' privacy. The purpose of this research was to design a blockchain-based edge computing method for securing the smart home system, in conjunction with machine learning techniques, in order to construct a secure smart home system with energy usage prediction and user profiling. The research proposes a blockchain-based smart home system that can continuously monitor IoT-enabled smart home appliances such as smart microwaves, dishwashers, furnaces, and refrigerators, among others. An approach based on machine learning was utilized to train the auto-regressive integrated moving average (ARIMA) model for energy usage prediction, which is provided in the user's wallet, to estimate energy consumption and maintain user profiles. The model was tested using the moving average statistical model, the ARIMA model, and the deep-learning-based long short-term memory (LSTM) model on a dataset of smart-home-based energy usage under changing weather conditions. The findings of the analysis reveal that the LSTM model accurately forecasts the energy usage of smart homes.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Internet of Things; blockchain; edge computing; privacy; machine learning; net-metering

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 实验仿真

【DOI】 10.3390/s23115263

【Author】 Li, Ning Zhu, Xiaoyun

【Source】SOFT COMPUTING CCF-C

【影响因子】3.732

【主题类别】

区块链应用-实体经济-康复领域

【Abstract】Sports injuries have been a common concern during athlete training as the number of Chinese athletes and their training intensity have increased. These injuries frequently impact critical locations, including the waist, shoulder, and wrist, impeding training progress and intensity. Inadequate training levels, a lack of understanding of preventive measures, inappropriate movements, insufficient warm-up exercises, low physical condition, and an inability to adjust to the environment all contribute to sports injuries. As a result, athlete training and recovery suffer. To address these issues, this study presents a neural network-based sports injury risk analysis system that uses blockchain and the Internet of Things (IoT). The suggested system combines a multi-sensor data fusion technique based on blockchain and IoT to evaluate and analyze sports injuries completely. The system has three unique roles: coach, team doctor, and athlete, each with its functional modules. Sensors collect data on athletes' recovery from sports injuries by collecting and analyzing inputs inside the human action system. The system then establishes the output values of a neural network and a risk level table, allowing for the estimation of athletes' risk levels based on the given table. Comparing the traditional technique and the sports injury rehabilitation monitoring system demonstrates that the technology can reliably detect players' injury locations in as little as 0.2 s. Furthermore, a thorough examination of the system's tracking and analysis accuracy reveals a recovery rate of approximately 94.39%, demonstrating minimal fluctuations and improved stability. The experimental findings reveal that combining blockchain, IoT, and neural network technologies makes the proposed sports injury rehabilitation monitoring system useful in quickly recognizing damage positions and enabling accurate tracking and analysis.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Blockchain; Internet of Things; Neural network; Sports injury; Data acquisition; Rehabilitation monitoring system

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 实验仿真

【DOI】 10.1007/s00500-023-08677-w

【Author】 Dos Reis, Fernando Bereta Borkum, Mark Mukherjee, Monish Sebastian Cardenas, David Jonathan

【Source】IEEE ACCESS

【影响因子】3.476

【主题类别】

区块链应用-实体经济-电力领域

【Abstract】This paper explores the potential of distributed ledger technology (DLT) to improve fault-tolerant grid operations by leveraging its core features as an immutable, decentralized ledger, a distributed, consensus-based agreement process, and a distributed state-replication engine. Distribution power systems deliver electricity to millions of customers; however, they are susceptible to various threats that can result in customer interruptions. These include faults caused by adverse weather conditions, natural disasters, vegetation growth, equipment failure, and malicious attacks. To minimize the effects of these faults, fault-handling approaches rely on network knowledge to isolate affected areas and reconnect unaffected areas, reducing the number of affected customers while maintaining safety. Here, we present a trusted data-sharing architecture that enables independent, distributed actors to reconstruct the pre-fault system state by enabling distributed resources to make appropriate decisions with limited network/system information. Although the process requires some data sharing between switch-delimited areas, the approach limits the amount of private information shared, preserving customers' privacy and business-sensitive information. We include three use cases that form a foundation for third parties to develop functional solutions that can eventually be deployed in the field. The gross error detection method used within switch-delimited areas can identify sensor errors and accurately detect circuit breaker states. The evaluation of possible reconnection while preserving data ownership resulted in a voltage magnitude difference smaller than 0.01% from the OpenDSS power flow solution that has full system knowledge, which is below the expected power flow tolerance. The approach offers a promising opportunity for improving fault-tolerant distribution grid operations.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Distributed ledger technology; power distribution; power distribution faults; power system restoration

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 实验仿真

【DOI】 10.1109/ACCESS.2023.3287831

【Author】 Xu, Xiaoyan Sethi, Suresh P. Chung, Sai-Ho Choi, Tsan-Ming

【Source】IISE TRANSACTIONS

【影响因子】3.425

【主题类别】

区块链治理-市场治理-技术采用

【Abstract】In the digital age, operations can be improved by a wise use of information and technological tools. During the COVID-19 pandemic, governments faced various choices of vaccines possessing different efficacy and availability levels at different time points. In this article, we consider a two-stage vaccine ordering problem of a government from a first and only supplier in the first stage, and either the same supplier or a new second supplier in the second stage. Between the two stages, potential demand information for the vaccine is collected to update the forecast. Using dynamic programming, we derive the government's optimal vaccine ordering policy. We find that the government should select its vaccine supplier based on the disease's infection rate in the society. When the infection rate is low, the government should order nothing at the first stage and order from the supplier with a higher efficacy level at the second stage. When the disease's infection rate is high, the government should order vaccines at the first stage and switch to the other supplier with a lower efficacy level at the second stage. We extend our model to examine (i) the value of blockchain adoption and (ii) the impact of vaccines' side effects.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Vaccine supply chain; two-stage ordering; information updating; social welfare; blockchain; COVID-19; >

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 实证数据

【DOI】 10.1080/24725854.2023.2204329

【Author】 Beheshti, Moein ZareRavasan, Ahad Mahdiraji, Hannan Amoozad Jafari-Sadeghi, Vahid Sakka, Georgia

【Source】JOURNAL OF CONSUMER BEHAVIOUR

【影响因子】3.199

【主题类别】

区块链治理-元分析-领域分析

【Abstract】This review aims to assess the most prevalent disruptive technologies of the past decade and evaluate them from a dual perspective. This study utilises the technology life cycle framework to classify disruptive technologies into three phases, (i) emerging, (ii) industry, and (iii) customer-focused research outcomes. Subsequently, entities using six thematic categories via topic modelling are assessed to determine the research trajectory about customers, industries, and theory. The present study utilises the Latent Dirichlet Allocation framework for topic modelling, a quantitative tool that employs advanced statistical methodology to extract semantic knowledge from large text corpora. This approach offers a more comprehensive and insightful categorisation of information compared to traditional research review models such as archive review, meta-review analysis, and systematic literature review. After the review, over 750 articles were analysed and classified according to their unique themes and technological emphasis. The study suggests practical applications that could enhance industrial progress through disruptive technology. Furthermore, by identifying gaps, the authors suggest multiple research prospects for forthcoming investigations on disruptive technologies and their impact on consumer behaviour. This research is novel in various aspects. This research is by far the only research record in the last decade that provides a systematic literature review of disruptive technologies with an extensive focus on customers. Considering the methodological perspective, this research uses topic modelling as a new literature review model, enabling analysing more research outputs and yielding more valuable findings than traditional methodologies.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 综述

【DOI】 10.1002/cb.2204

【Author】 Islam, Md Didarul

【Source】INFORMATION SYSTEMS CCF-B

【影响因子】3.180

【主题类别】

区块链应用-实体经济-供应链

【Abstract】Supply chain networks are becoming more complex and vulnerable to various attacks. We must tackle these attacks properly to ensure the required supply chain security. In this paper, I have classified major supply chain security issues with some real-life examples. I have also discussed the most recent countermeasures of these attacks. Furthermore, blockchain is a promising distributed information technology that creates opportunities for a new approach in the supply chain area. In my research work, I have described how to integrate blockchains into supply chain architectures to create more secure systems. I also discussed the benefits and challenges of introducing blockchain into the supply chain in this paper. To explain that better, I explored the performances of some in-practice blockchain platforms that we can apply to supply chains. I further mentioned the possible solutions for the challenges of integrating blockchain into supply chains.& COPY; 2023 Elsevier Ltd. All rights reserved.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Blockchain; Integration; Supply chain; Supply chain attacks; Supply chain security

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 综述

【DOI】 10.1016/j.is.2023.102232

【Author】 Zhou, Bao Zhao, Junsan Chen, Guoping Yin, Ying

【Source】APPLIED SCIENCES-BASEL

【影响因子】2.838

【主题类别】

区块链技术-协同技术-大数据

【Abstract】Spatiotemporal big data are a kind of data that marks time information and geographic location and has been widely applied in various fields. However, there are always security issues with spatiotemporal big data, especially in data collection and authentication. Traditional authentication protocols are less efficient in the face of ultra-large-scale IoT (Internet of Things, IoT) device verification, and the threat of single-point failure is relatively large. Given these complications, a group authentication scheme is proposed in this paper with blockchain spatiotemporal big data. The decentralization of the blockchain is utilized to solve the single point of failure, and the single-point authentication is combined with the group authentication, the authentication efficiency is improved through the group authentication, and the illegal nodes are accurately identified using the single-point authentication. The simulation results demonstrate that using the MHT (Merkel Hash Tree, MHT) algorithm for group authentication can effectively improve the authentication efficiency of the entire system when the number of users exceeds 200. The time overhead is only 4 ms when the number of users is 16,000. It can have a large throughput (400-500 tps) and a low latency (1-2 s) at the same time when the block size is 1500 KB. This study not only verifies the legitimacy of each device and protects the security of spatiotemporal big data, but also significantly reinforces the authentication efficiency compared with similar schemes.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】spatio-temporal big data; Internet of Things; blockchain; group authentication; MHT

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 实验仿真

【DOI】 10.3390/app13116641

【Author】 Yazdinejad, Abbas Rabieinejad, Elnaz Hasani, Tahereh Srivastava, Gautam

【Source】CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS

【影响因子】2.303

【主题类别】

区块链应用-实体经济-供应链

【Abstract】Drug Supply Chain Management (DSCM) can be one of the most affected streams in healthcare due to pandemics. The delivery of medicine to patients through DSCM is a complex process. Also, DSCM has several challenges, including counterfeiting, fraud, and the availability of medicine. Therefore, there is a need for security and intelligence strategies to remove pharmaceutical fraud, which remains a significant challenge since ensuring fair and secure access to medicine, services, and assistance is essential in Cyber-Physical Systems (CPS)-based DSCM. The existing CPS-based DSCM systems do, however, have some limitations in security, intelligence, planning, scheduling, quality, and logistics. This paper proposes a secure drug supply chain management framework that can acheive more security and intelligence via machine learning models. The proposed framework utilizes Bidirectional Encoder Representations from Transformers (BERT)-based and machine learning-based attack detection modules to provide more intelligence and security in block chain-based DSCM. Evaluation results show that BERT-based recommender systems ideally suggest appropriate alternative drugs that are close to 99% similar to the prescribed medication based on public datasets. Moreover, attack detection in the proposed framework provides significant accuracy, precision, recall, and F-measure results in threat detection (phishing, scamming, and abnormal transactions) in the blockchain layer:

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Supply chain; DSCM; CPS; BERT; Blockchain; Machine learning

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 实验仿真

【DOI】 10.1007/s10586-023-04088-6

【Author】 Choudhary, Ashutosh Kumar Rahamatkar, Surendra

【Source】JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

【影响因子】1.737

【主题类别】

区块链技术-协同技术-物联网

【Abstract】DoS, GH, Sybil, Masquerading, Spoofing, Man in the Middle, etc. constantly attack IoT networks. Internal or external attacks reduce end-to-end delay, throughput, energy use, and other metrics. To counter these attacks, researchers have proposed a number of security & privacy mechanisms with varying computational complexity and security levels. Immutability, traceability, transparency, and distributed nature make blockchain-based models secure. QoS depends on blockchain length, so these models aren't scalable. Researchers say sidechaining improves QoS while remaining secure. Splitting or merging complex sidechains requires machine learning. Low-power IoT networks can't use models. This text suggests a lightweight MGWO Model that helps establish initial routes by choosing high-trust nodes, reducing sidechaining power consumption, and incorporating fault-aware trust establishment. MGWO Model determines blockchain piece count for high QoS. MGWO Model uses Q-Learning to detect network faults. Fault identification is controlled by a stochastically modelled and activated Intrinsic Genetic Algorithm (IGA). Q-Learning, MGWO, and IGA can mitigate Sybil, Masquerading, Grey Hole, DDoS, and MITM attacks. Even when attacked, the proposed model maintains high QoS, improving real-time deployment efficiency. The proposed model improves energy efficiency by 15.9%, throughput by 10.6%, communication speed by 8.3%, and packet delivery by 0.8% for different network scenarios.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Trust; wireless; IoT; blockchain; sidechain; MITM; MGWO; Q learning; IGA; DDoS; Sybil; QoS

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 实验仿真

【DOI】 10.3233/JIFS-223316

【Author】 Sato, Hiroshi Kubo, Masao

【Source】ARTIFICIAL INTELLIGENCE FOR COMMUNICATIONS AND NETWORKS, AICON 2022

【影响因子】

【主题类别】

区块链技术-协同技术-进化计算

【Abstract】Various population-based optimization methods have been proposed following the development of evolutionary computation. Optimization is achieved through the interactions of many individuals in these systems. However, reliability becomes an issue when the system is implemented in a distributed environment. It may not be possible to trust others in such an environment. Many factors, such as malfunction of distributed parts or failure to synchronize, will break the trust. Therefore, there must be some mechanism that can build trust between distributed individuals. The record of past actions is usually a good source for trust building. This paper utilizes the blockchain mechanism for the population-based optimization system to make a trust management system. By using blockchain, we can implement trust without a central authority. In the system, all interactions are reviewed and get feedback, and the feedback is used to calculate the trust score.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Trusted system; Blockchain; Surrogate Assisted; Evolutionary Computation

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 实验仿真

【DOI】 10.1007/978-3-031-29126-5_12

【Author】 Li, Da Chen, Shuai Li, Jiangtao Wang, Xinnan Zhang, Zhe Ran, Dongchuan

【Source】PROCEEDINGS OF THE 3RD INTERNATIONAL SYMPOSIUM ON NEW ENERGY AND ELECTRICAL TECHNOLOGY

【影响因子】

【主题类别】

区块链应用-实体经济-电力领域

【Abstract】The inherent financial attribute of blockchain technology can be well applied to power transactions. However, due to the throughput constraints and network delay of blockchain, it is difficult to improve the transaction confirmation throughput while ensuring the delay. This paper proposes a blockchain power trading model with hierarchical governance and layer by layer confirmation, which provides a reference for the State Grid Corporation of China to use blockchain technology in power transaction, as well as for distributed high-throughput blockchain application scenarios.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Hierarchical blockchain; Power trading

【发表时间】2023

【收录时间】2023-07-19

【文献类型】 理论模型

【DOI】 10.1007/978-981-99-0553-9_11