Requirements for Big Data Adoption for Railway Asset Management

McMahon, Paul; Zhang, Tieling; Dwight, Richard

doi:10.1109/access.2020.2967436

Cited by 36 publications

(16 citation statements)

References 135 publications

(137 reference statements)

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“… • Blockchain • Predictive analytics Mayer ( 2019 ) Railway asset management Blockchain and big data analytics can be used for railways asset condition management. • Descriptive analytics • Diagnostic analytics • Predictive analytics • Prescriptive analytics McMahon et al ( 2020 ) …”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

et al. 2022

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Artificial intelligence (AI) and blockchain are the two disruptive technologies emerging from the Fourth Industrial Revolution (IR4.0) that have introduced radical shifts in the industry. The amalgamation of AI and blockchain holds tremendous potential to create new business models enabled through digitalization. Although research on the application and convergence of AI and blockchain exists, our understanding of the utility of its integration for business remains fragmented. To address this gap, this study aims to characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business. Using bibliometric analysis, this study reveals the most influential articles on the subject based on their publications, citations, and importance in the intellectual network. Using content analysis, this study sheds light on the subject’s intellectual structure, which is underpinned by four major thematic clusters focusing on supply chains, healthcare, secure transactions, and finance and accounting. The study concludes with 10 application areas in business that can benefit from these technologies.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

et al. 2022

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“…In [33], these technical difficulties are suggested to be solved using DRL for altering block size and interval. Blockchain is also recommended for privacy preserving and efficient database management in railway vehicles [34].…”

Section: Blockchain Based Vehicular Communicationsmentioning

confidence: 99%

A Proof-of-Quality-Factor (PoQF)-Based Blockchain and Edge Computing for Vehicular Message Dissemination

Ayaz

Sheng

Tian

et al. 2021

IEEE Internet Things J.

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Blockchain applications in vehicular networks can offer many advantages including decentralization and improved security. However, most of consensus algorithms in blockchain are difficult to be implemented in a Vehicular Ad-Hoc Networks (VANET) without the help of edge computing services. For example, the connectivity in VANET only remains for a short period of time, which is not sufficient for highly time consuming consensus algorithms, e.g., Proof-of-Work, running on mobile edge nodes (vehicles). Other consensus algorithms also have some drawbacks, e.g. Proof-of-Stake (PoS) is biased towards nodes with higher amount of stakes and Proof-of-Elapsed-Time (PoET) is not highly secure against malicious nodes. For these reasons, we propose a voting blockchain based on Proof-of-Quality-Factor (PoQF) consensus algorithm, where threshold number of votes is controlled by edge computing servers. Specifically, PoQF includes voting for message validation and a competitive relay selection process based on probabilistic prediction of channel quality between transmitter and receiver. The performance bounds of failure and latency in message validation are obtained. The paper also analyzes the throughput of block generation, as well as the asymptotic latency, security and communication complexity of PoQF. An incentive distribution mechanism to reward honest nodes and punish malicious nodes is further presented and its effectiveness against collusion of nodes is proved using game theory. Simulation results show that PoQF reduces failure in validation by 11% and 15% as compared to PoS and PoET, respectively, and is 68 ms faster than PoET.

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“…This adoption process is widely studied in different sectors such as healthcare (Chen et al, 2020 ), industrial (McMahon et al, 2020 ), or tourism (Yadegaridehkordi et al, 2020 ) although all of them refer to generic Big Data techniques while there is little literature on the adoption process of Big Data Analytics (Maroufkhani et al, 2020 ) as can be seen check from the appropriate literature review (Inamdar et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

et al. 2021

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The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting model with the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model (MLP) for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications.

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Requirements for Big Data Adoption for Railway Asset Management

Cited by 36 publications

References 135 publications

Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

A Proof-of-Quality-Factor (PoQF)-Based Blockchain and Edge Computing for Vehicular Message Dissemination

Predicting Big Data Adoption in Companies With an Explanatory and Predictive Model

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