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Emerging Industrial Applications: Orchestration of Machine Learning, the IoT, and Blockchain

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A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (24 December 2023) | Viewed by 58822

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Special Issue Editors

Dr. Soumya Banerjee

E-Mail Website
Guest Editor

1. Senior Vice President Innovation, Trasna-Solutions Ltd. (Europe), Millstreet, Ireland
2. Associated Senior Researcher, INRIA-AIO Paris, 75012 Paris, France
3. Associated Researcher, CNAM-Cedric, 75003 Paris, France
Interests: machine learning; IoT; security and blockchain
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Samia Bouzefrane

E-Mail Website
Guest Editor

CEDRIC Lab, Conservatoire National des Arts et Métiers, 75003 Paris, France
Interests: IoT/CPS; IoT security; trusted computing; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The significant trend of digital phase-shifts has been synergized by different artefacts—machine learning (ML) and the Internet of Things (IoT) and their associated security measures with the help of Blockchain and distributed ledger technologies. However, these technologies are developing independently, even though they are converging closer and closer to each other. We envisage a steady orchestration and combination of these technologies, formulating real, balanced industrial use-cases and challenges to be resolved—for example, training a binary hash with a machine learning algorithm to fit the result toward distributed ledger and blockchain toward IoT technologies. We also anticipate overlaps with existing consensus algorithms for use in the industrial IoT. It is therefore pivotal to organize and accumulate all such research ideas under a single issue.

This Special Issue invites papers on innovative ideas in, but not limited to

Lightweight deep learning models and blockchain-based architectures for the IoT;
ML impressed blockchain for emerging IoT applications;
Privacy and accountability of ML-enabled IoT systems;
Energy-efficient computing architecture for secure IoT applications;
Analytical aspects of ML and blockchain converged IoT/Edge devices;
Energy-efficient communication protocols for IoT systems powered by AI and blockchain;
Intelligent and optimized consensus algorithms;
Different versions of reinforcement learning and federated learning for IoT and blockchain implementations.

Dr. Soumya Banerjee
Prof. Dr. Samia Bouzefrane
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

IoT
machine learning
blockchain
consensus

Published Papers (7 papers)

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Research

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21 pages, 2839 KiB

Open AccessArticle

IoTBChain: Adopting Blockchain Technology to Increase PLC Resilience in an IoT Environment

by Philipp Schmid, Alisa Schaffhäuser and Rasha Kashef

Information 2023, 14(8), 437; https://doi.org/10.3390/info14080437 - 02 Aug 2023

Cited by 3 | Viewed by 1731

Abstract

The networks on a centralized cloud architecture that interconnect Internet of Things (IoT) gadgets are not limited by national or jurisdictional borders. To ensure the secure sharing of sensitive user data among IoT gadgets, it is imperative to maintain security, resilience and trustless authentication. As a result, blockchain technology has become a viable option to provide such noteworthy characteristics. Blockchain technology is foundational for resolving many IoT security and privacy issues. Blockchain’s safe decentralization can solve the IoT ecosystem’s security, authentication and maintenance constraints. However, blockchain, like any innovation, has drawbacks, mainly when used in crucial IoT systems such as programmable logic controller (PLC) networks. This paper addresses the most recent security and privacy issues relating to the IoT, including the perception, network and application layers of the IoT’s tiered architecture. The key focus is to review the existing IoT security and privacy concerns and how blockchain might be used to deal with these problems. This paper proposes a novel approach focusing on IoT capabilities and PLC device security. The new model will incorporate a proof-of-work-based blockchain into the (PLC) IoT ecosystem. This blockchain enables the transmission of binary data and the data logging of the (PLC) networks’ signals. This novel technique uses fewer resources than other sophisticated methods in that PLC devices communicate data while maintaining a high transmission, encryption and decoding speed. In addition to ensuring repeatability, our new model addresses the memory and tracing problems that different PLC manufacturers encounter. Full article

(This article belongs to the Special Issue Emerging Industrial Applications: Orchestration of Machine Learning, the IoT, and Blockchain)

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30 pages, 546 KiB

Open AccessArticle

Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models

by Dean Fantazzini

Information 2023, 14(5), 254; https://doi.org/10.3390/info14050254 - 23 Apr 2023

Viewed by 2179

Abstract

In this paper, we analyzed a dataset of over 2000 crypto-assets to assess their credit risk by computing their probability of death using the daily range. Unlike conventional low-frequency volatility models that only utilize close-to-close prices, the daily range incorporates all the information provided in traditional daily datasets, including the open-high-low-close (OHLC) prices for each asset. We evaluated the accuracy of the probability of death estimated with the daily range against various forecasting models, including credit scoring models, machine learning models, and time-series-based models. Our study considered different definitions of “dead coins” and various forecasting horizons. Our results indicate that credit scoring models and machine learning methods incorporating lagged trading volumes and online searches were the best models for short-term horizons up to 30 days. Conversely, time-series models using the daily range were more appropriate for longer term forecasts, up to one year. Additionally, our analysis revealed that the models using the daily range signaled, far in advance, the weakened credit position of the crypto derivatives trading platform FTX, which filed for Chapter 11 bankruptcy protection in the United States on 11 November 2022. Full article

(This article belongs to the Special Issue Emerging Industrial Applications: Orchestration of Machine Learning, the IoT, and Blockchain)

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15 pages, 2903 KiB

Open AccessArticle

Prediction Machine Learning Models on Propensity Convicts to Criminal Recidivism

by Olha Kovalchuk, Mikolaj Karpinski, Serhiy Banakh, Mykhailo Kasianchuk, Ruslan Shevchuk and Nataliya Zagorodna

Information 2023, 14(3), 161; https://doi.org/10.3390/info14030161 - 03 Mar 2023

Cited by 10 | Viewed by 2843

Abstract

Increasing internal state security requires an understanding of the factors that influence the commission of repetitive crimes (recidivism) since the crime is not caused by public danger but by the criminal person. Against the background of informatization of the information activities of law enforcement agencies, there is no doubt about the expediency of using artificial intelligence algorithms and blockchain technology to predict and prevent crimes. The prediction machine-learning models for identifying significant factors (individual characteristics of convicts), which affect the propensity to commit criminal recidivism, were applied in this article. For predicting the probability of propensity for criminal recidivism of customers of Ukrainian penitentiary institutions, a Decision Tree model was built to suggest the probability of repeated criminal offenses by convicts. It was established that the number of convictions to the actual punishment and suspended convictions is the main factors that determine the propensity of customers of penitentiary institutions to commit criminal recidivism in the future. Decision Tree models for the classification of convicts prone or not prone to recidivism were built. They can be used to predict new cases for decision-making support in criminal justice. In our further research, the possibility of using the technology of distributed registers/blockchain in predictive criminology will be analyzed. Full article

(This article belongs to the Special Issue Emerging Industrial Applications: Orchestration of Machine Learning, the IoT, and Blockchain)

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26 pages, 16128 KiB

Open AccessArticle

Using Crypto-Asset Pricing Methods to Build Technical Oscillators for Short-Term Bitcoin Trading

by Zixiu Yang and Dean Fantazzini

Information 2022, 13(12), 560; https://doi.org/10.3390/info13120560 - 29 Nov 2022

Viewed by 2865

Abstract

This paper examines the trading performances of several technical oscillators created using crypto-asset pricing methods for short-term bitcoin trading. Seven pricing models proposed in the professional and academic literature were transformed into oscillators, and two thresholds were introduced to create buy and sell signals. The empirical back-testing analysis showed that some of these methods proved to be profitable with good Sharpe ratios and limited max drawdowns. However, the trading performances of almost all methods significantly worsened after 2017, thus indirectly confirming an increasing financial literature that showed that the introduction of bitcoin futures in 2017 improved the efficiency of bitcoin markets. Full article

(This article belongs to the Special Issue Emerging Industrial Applications: Orchestration of Machine Learning, the IoT, and Blockchain)

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Review

Jump to: Research

31 pages, 8951 KiB

Open AccessReview

Mapping Metaverse Research: Identifying Future Research Areas Based on Bibliometric and Topic Modeling Techniques

by Abderahman Rejeb, Karim Rejeb and Horst Treiblmaier

Information 2023, 14(7), 356; https://doi.org/10.3390/info14070356 - 22 Jun 2023

Cited by 9 | Viewed by 4565

Abstract

The metaverse represents an immersive digital environment that has garnered significant attention as a result of its potential to revolutionize various industry sectors and its profound societal impact. While academic interest in the metaverse has surged, a dearth of comprehensive review articles employing bibliometric techniques remains. This study seeks to address this gap by analyzing 595 metaverse-related journal articles using bibliometric and topic modeling techniques, marking the first of its kind to investigate the bibliometric profile of metaverse research. The findings reveal exponential growth in metaverse research since 2020, identifying major trends, prolific authors, and the most active journals in the field. A keyword co-occurrence analysis further uncovers four significant clusters of metaverse-related interests, highlighting its unique facets and underscoring its far-reaching implications across various sectors, including education, healthcare, retail, and tourism. This study emphasizes the need for more research and collaboration in advancing the metaverse field and presents 27 research questions for future investigation. This comprehensive analysis serves as a foundation for understanding the current state of metaverse research and its potential trajectory. Full article

(This article belongs to the Special Issue Emerging Industrial Applications: Orchestration of Machine Learning, the IoT, and Blockchain)

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19 pages, 750 KiB

Open AccessReview

Smart Contracts in Blockchain Technology: A Critical Review

by Hamed Taherdoost

Information 2023, 14(2), 117; https://doi.org/10.3390/info14020117 - 13 Feb 2023

Cited by 40 | Viewed by 26600

Abstract

By utilizing smart contracts, which are essentially scripts that are anchored in a decentralized manner on blockchains or other similar infrastructures, it is possible to make the execution of predetermined procedures visible to the outside world. The programmability of previously unrealized assets, such as money, and the automation of previously manual business logic are both made possible by smart contracts. This revelation inspired us to analyze smart contracts in blockchain technologies written in English between 2012 and 2022. The scope of research is limited to the journal. Reviews, conferences, book chapters, theses, monographs, and interview-based works, and also articles in the press, are eliminated. This review comprises 252 articles over the last ten years with “Blockchain”, “block-chain”, “smart contracts”, and “smart contracts” as keywords. This paper discusses smart contracts’ present status and significance in blockchain technology. The gaps and challenges in the relevant literature have also been discussed, particularly emphasizing the limitations. Based on these findings, several research problems and prospective research routes for future study that will likely be valuable to academics and professionals are identified. Full article

(This article belongs to the Special Issue Emerging Industrial Applications: Orchestration of Machine Learning, the IoT, and Blockchain)

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12 pages, 1606 KiB

Open AccessReview

Non-Fungible Tokens (NFT): A Systematic Review

by Hamed Taherdoost

Information 2023, 14(1), 26; https://doi.org/10.3390/info14010026 - 31 Dec 2022

Cited by 24 | Viewed by 16907

Abstract

Non-fungible tokens (NFTs) are gaining in popularity and are already extensively implemented. New use cases for NFTs are constantly developing. NFTs may prevent counterfeiting since each token carries the owner’s digital signature and is thus unique. For the usage of NFTs to progress in an institutional environment, the potential for using NFTs must be investigated in detail. This discovery prompted a comprehensive examination of NFTs developed between 2012 and 2022. The scope is confined to the journal and the keywords “Blockchain”, “Block-chain”, “Non-fungible Token”, and “NFT” are used. Also excluded are studies based on interviews, articles in the press, non-English articles, reviews, conferences, book chapters, dissertations, and monographs. This evaluation includes 34 papers from the last decade. This research examines the current state and development trends of NFT. In addition, the gaps and difficulties in the related literature have been explored, with an emphasis on the limits. These results highlight many unsolved research questions and potential future research avenues that would likely be beneficial to academics and professionals. Full article

(This article belongs to the Special Issue Emerging Industrial Applications: Orchestration of Machine Learning, the IoT, and Blockchain)

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