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Knowledge Discovery and Machine Learning on Cryptocurrency in IoT Environment
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Knowledge Discovery and Machine Learning on Cryptocurrency in IoT Environment

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 25316

Special Issue Editors

Dr. Zeinab Shahbazi

E-Mail Website
Guest Editor
Department of Mathematics and Informatics, University of Barcelona, 08007 Barcelona, Spain
Interests: machine learning; NLP; knowledge discovery; data mining; blockchain
Special Issues, Collections and Topics in MDPI journals
Dr. Faisal Jamil

E-Mail Website
Guest Editor
Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
Interests: Internet of Things; machine learning; WBAN; blockchain; microgrid; indoor localization; task orchestration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning and knowledge discovery are key to any research on recent technologies and help researchers to obtain better results using their proposed approach. The cryptocurrency framework is another famous approach that has gained a lot of interest, especially in research on blockchains and digital coins. The aim of this Special Issue is to provide a collection of papers from leading experts in the areas of machine learning, blockchains, and cryptocurrencies.

The topics covered in this Special Issue will include but are not limited to the following:

  • The combination of machine learning and blockchain approaches to improve system performance.
  • Academic research on price prediction system security for blockchains and cryptocurrencies.
  • Industrial applications of blockchain and cryptocurrencies.
  • Applications of fintech in academia and industry.
  • The economics of blockchain technology.
  • Financial analysis and risk management with cryptocurrencies.
  • Time series prediction.
  • Knowledge discovery using social media content.

Dr. Zeinab Shahbazi
Dr. Faisal Jamil
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • cryptocurrency
  • blockchain
  • machine learning
  • knowledge discovery
  • natural language processing
  • prediction
  • time series information

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

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28 pages, 3167 KiB  
Article
Effectiveness of the Relative Strength Index Signals in Timing the Cryptocurrency Market
by Marek Zatwarnicki, Krzysztof Zatwarnicki and Piotr Stolarski
Sensors 2023, 23(3), 1664; https://doi.org/10.3390/s23031664 - 2 Feb 2023
Cited by 4 | Viewed by 6769
Abstract
In 2020 and 2021, the cryptocurrency market attracted millions of new traders and investors. Lack of regulation, high liquidity, and modern exchanges significantly lowered the entry threshold for new market participants. In 2021, over 5 million Americans were regularly involved in cryptocurrency trading. [...] Read more.
In 2020 and 2021, the cryptocurrency market attracted millions of new traders and investors. Lack of regulation, high liquidity, and modern exchanges significantly lowered the entry threshold for new market participants. In 2021, over 5 million Americans were regularly involved in cryptocurrency trading. At that time, the interest in market indicators and trading strategies remained low, leading to the conclusion that most investors did not use decision-support indicators. The correct and backtested use of technical analysis signals can give the trader a significant advantage over most market participants. This work introduces an algorithmic approach to examining the effectiveness of the signals generated by one of the most popular market indicators, the Relative Strength Index (RSI). A model corresponding to an actual cryptocurrency exchange was used to backtest the strategies. The results show that the RSI as a momentum indicator in the cryptocurrency market involves high risk. Using alternative RSI applications can allow traders to gain an advantage in the cryptocurrency market. Comparing the results with the traditional buy and hold strategy shows the credible potential of the indicated method and the usage of signals generated by the technical analysis indicators. Full article
(This article belongs to the Special Issue Knowledge Discovery and Machine Learning on Cryptocurrency in IoT Environment)
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16 pages, 1708 KiB  
Article
Analysis of the Security and Reliability of Cryptocurrency Systems Using Knowledge Discovery and Machine Learning Methods
by Zeinab Shahbazi and Yung-Cheol Byun
Sensors 2022, 22(23), 9083; https://doi.org/10.3390/s22239083 - 23 Nov 2022
Cited by 4 | Viewed by 3812
Abstract
Cryptocurrency, often known as virtual or digital currency, is a safe platform and a key component of the blockchain that has recently attracted much interest. Utilizing blockchain technology, bitcoin transactions are recorded in blocks that provide detailed information on all financial transactions. Artificial [...] Read more.
Cryptocurrency, often known as virtual or digital currency, is a safe platform and a key component of the blockchain that has recently attracted much interest. Utilizing blockchain technology, bitcoin transactions are recorded in blocks that provide detailed information on all financial transactions. Artificial intelligence (AI) has significant applicability in several industries because of the abundance and processing capacity of large data. One of the main issues is the absence of explanations for AI algorithms in the current decision-making standards. For instance, there is no deep-learning-based reasoning or control for the system’s input or output processes. More particularly, the bias for adversarial attacks on the process interface and learning characterizes existing AI systems. This study suggests an AI-based trustworthy architecture that uses decentralized blockchain characteristics such as smart contracts and trust oracles. The decentralized consensuses of AI predictors are also decided by this system using AI, enabling secure cryptocurrency transactions, and utilizing the blockchain technology and transactional network analysis. By utilizing AI for a thorough examination of a network, this system’s primary objective is to improve the performance of the bitcoin network in terms of transactions and security. In comparison to other state-of-the-art systems, the results demonstrate that the proposed system can achieve very accurate output. Full article
(This article belongs to the Special Issue Knowledge Discovery and Machine Learning on Cryptocurrency in IoT Environment)
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Review

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28 pages, 416 KiB  
Review
Security of Cryptocurrencies: A View on the State-of-the-Art Research and Current Developments
by Paweł Weichbroth, Kacper Wereszko, Helena Anacka and Jolanta Kowal
Sensors 2023, 23(6), 3155; https://doi.org/10.3390/s23063155 - 15 Mar 2023
Cited by 18 | Viewed by 13899
Abstract
[Context] The goal of security is to protect digital assets, devices, and services from being disrupted, exploited or stolen by unauthorized users. It is also about having reliable information available at the right time. [Motivation] Since the inception in 2009 of the first [...] Read more.
[Context] The goal of security is to protect digital assets, devices, and services from being disrupted, exploited or stolen by unauthorized users. It is also about having reliable information available at the right time. [Motivation] Since the inception in 2009 of the first cryptocurrency, few studies have been undertaken to analyze and review the state-of-the-art research and current developments with respect to the security of cryptocurrencies. [Purpose] We aim to provide both theoretical and empirical insights into the security landscape, in particular focusing on both technical solutions and human-related facets. [Methodology] We used an integrative review which could help in building science and scholarly research, the basis for conceptual and empirical models. [Results] Successful defense against cyberattacks depends on technical measures on the one hand, as well as on self-education and training with the aim to develop competence, knowledge, skills and social abilities, on the other. [Contribution] Our findings provide a comprehensive review for the major achievements and developments of the recent progress on the security of cryptocurrencies. [Future research] Since there is increasing interest in adoption of the current solutions within the central bank digital currencies, the future research should explore the development and inception of effective measures against social engineering attacks, which still remain the main concern. Full article
(This article belongs to the Special Issue Knowledge Discovery and Machine Learning on Cryptocurrency in IoT Environment)
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