Symmetry in Internet of Things and Blockchain

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (14 July 2023) | Viewed by 5129

Special Issue Editors


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Guest Editor
School of Creative Convergence, Andong National University, 1375 Gyeongdong-ro, Yongsang-dong, Andong, Korea
Interests: Big Data; analysis; analysis optimization; reinforcement learning; block chain

Special Issue Information

Dear Colleagues,

Blockchain has become an integral part of modern digital service systems that is required to provide highly efficient and convenient performance while guaranteeing a much higher level of security than the existing systems can offer, especially when operating AI systems or their hardware.

As competition within the IoT industry is intensifying and the network system technologies are advancing rapidly, a robust blockchain system is considered indispensable. This Special Issue (SI) attempts to focus on the significance of blockchain and its technologies in our current digital society, creating a forum for discussion where contributors are able to debate existing or future blockchain technologies or associated topics. The contributors may select a topic from the list provided below or find another one as long as it serves the SI’s purpose. Major technological aspects and their utility in IoT and/or blockchain systems should be described, along with their respective symmetries that contribute to these systems.

  • Electronic blockchain services prioritizing human beings and their lives;
  • Electronic solutions involving blockchain, IoT, or Big Data;
  • Electronic solutions adopting blockchain, IoT, or Big Data;
  • Means of aiding and serving people in need, especially the disabled or the elderly;
  • Electronic blockchain symmetry/blockchain engineering/blockchain mathematics/blockchain theories that would greatly affect science and industry;
  • Intelligent blockchain techniques and services for improved systems engineering;
  • An open engineering integration system for future systems;
  • Symmetry and asymmetry in mathematics and blockchain;
  • Mathematics and symmetry in IoT/IoE and blockchain;
  • Symmetry and asymmetry in computer and engineering science and blockchain;
  • Symmetry and asymmetry in physics and blockchain;
  • Symmetry and asymmetry in biology and blockchain;
  • Symmetry and asymmetry in chemistry and blockchain.

Dr. Se-Hoon Jung
Prof. Dr. Jun-Ho Huh
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. Symmetry 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 2400 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

  • blockchain
  • symmetry
  • IoT
  • internet of things
  • IoE
  • internet of everything
  • data science
  • big data
  • blockchain data

Published Papers (2 papers)

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Research

10 pages, 637 KiB  
Communication
The k + 1 Symmetric Test Pattern for Smart Contracts
by Tomasz Górski
Symmetry 2022, 14(8), 1686; https://doi.org/10.3390/sym14081686 - 14 Aug 2022
Cited by 14 | Viewed by 2662
Abstract
A smart contract is a pivotal notion in blockchain technology. Distributed applications contain smart contracts verifying the fulfillment of the conditions, which determine the execution of transactions between the blockchain network nodes. Those software-controlled logical conditions are called verification rules. As the number [...] Read more.
A smart contract is a pivotal notion in blockchain technology. Distributed applications contain smart contracts verifying the fulfillment of the conditions, which determine the execution of transactions between the blockchain network nodes. Those software-controlled logical conditions are called verification rules. As the number of conditions increases, the complexity of smart contract testing rapidly grows. This paper aims to propose a smart contract testing pattern that significantly limits the needed number of test cases. For evaluation expression with four verification rules, the pattern usage reduces the number of test cases by 68.75% in relation to the full coverage of logical value combinations. With the increase in the number of logical conditions, not only the number of test cases but also their percentage decreases. Starting from seven verification rules in the evaluation expression, the percentage reduction of test cases exceeds 90%. As a result, the cost of preparing and maintaining test case suites may be substantially cut. It should be emphasized that test execution time can be reduced even by 3 orders of magnitude (from seconds to milliseconds). Such an approach is highly important for regression testing, especially when used in continuous software integration, delivery, and deployment approaches. Full article
(This article belongs to the Special Issue Symmetry in Internet of Things and Blockchain)
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16 pages, 3335 KiB  
Article
Position Prediction in Space System for Vehicles Using Artificial Intelligence
by Won-Chan Lee, You-Boo Jeon, Seong-Soo Han and Chang-Sung Jeong
Symmetry 2022, 14(6), 1151; https://doi.org/10.3390/sym14061151 - 02 Jun 2022
Cited by 3 | Viewed by 2089
Abstract
This paper deals with the prediction of the future location of vehicles, which is attracting attention in the era of the fourth industrial revolution and is required in various fields, such as autonomous vehicles and smart city traffic management systems. Currently, vehicle traffic [...] Read more.
This paper deals with the prediction of the future location of vehicles, which is attracting attention in the era of the fourth industrial revolution and is required in various fields, such as autonomous vehicles and smart city traffic management systems. Currently, vehicle traffic prediction models and accident prediction models are being tested in various places, and considerable progress is being made. However, there are always errors in positioning when using wireless sensors due to various variables, such as the appearance of various substances (water, metal) that occur in the space where radio waves exist. There have been various attempts to reduce the positioning error in such an Internet of Things environment, but there is no definitive method with confirmed performance. Of course, location prediction is also not accurate. In particular, since a vehicle moves rapidly in space, it is increasingly affected by changes in the environment. Firstly, it was necessary to develop a spatial positioning algorithm that can improve the positioning accuracy. Secondly, for the data generated by the positioning algorithm, a machine learning method suitable for position prediction was developed. Based on the above two developed algorithms, through experiments, we found a means to reduce the error of positioning through radio waves and to increase the accuracy of positioning. We started with the idea of changing the positioning space itself from a three-dimensional space into a two-dimensional one. With changes in the time and space of radio wave measurement, the location was measured by transforming the spatial dimension to cope with environmental changes. This is a technology that predicts a location through machine learning on time series data using a direction angle classification technique. An experiment was conducted to verify the performance of the proposed technology. As a result, the accuracy of positioning was improved, and the accuracy of location prediction increased in proportion to the learning time. It was possible to confirm the prediction accuracy increase of up to 80% with changes. Considering that the accuracy result for location prediction presented by other researchers is 70%, through this study, the result was improved by 10% compared to the existing vehicle location prediction accuracy. In conclusion, this paper presents a positioning algorithm and machine learning methodology for vehicle positioning. By proving its usefulness through experiments, this study provides other researchers with a new definition of space for predicting the location of a vehicle, and a machine learning method using direction angles. Full article
(This article belongs to the Special Issue Symmetry in Internet of Things and Blockchain)
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