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Mathematics
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Scalability, Sustainability and Security: Searching for New Blockchain Solutions
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Scalability, Sustainability and Security: Searching for New Blockchain Solutions

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 4042

Special Issue Editors

Dr. Yu Guo

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing Normal University, Beijing, China
Interests: cloud computing security; network security; privacy-preserving data processing; blockchain technology
Prof. Dr. Rongfang Bie

E-Mail Website
Guest Editor
School of Artificial Intelligence, Beijing Normal University, Beijing, China
Interests: data mining and knowledge engineering; blockchain technology and educational applications; machine learning methods in biofeedback

Special Issue Information

Dear Colleagues,

We invite you to submit your latest applied research on the field of blockchains to this Special Issue, entitled “Scalability, Sustainability and Security: Searching for New Blockchain Solutions”. The aim of the Special Issue is to expand the applicability of blockchains and commit to the search for scalable, sustainable, and secure blockchain solutions. Any experimental research or empirical study on theoretical developments in blockchains is highly welcome. Additionally, research papers presenting solution methods and/or studying their computational complexity, as well as proposing new algorithms with which to solve blockchain problems in an effective and efficient manner, are also welcome. We are looking forward to receiving innovative approaches that apply, in practical settings, state-of-the art techniques from blockchain technology, protocols, and algorithms based on blockchains, with the aim of offering effective solutions to complex blockchain problems. Such types of papers will address the scalability, sustainability, and security of using blockchain technologies in addition to the enhancement of them by using intelligent methods to treat real-life problems from various disciplines.

Dr. Yu Guo
Prof. Dr. Rongfang Bie
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. Mathematics 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

  • theories of blockchain and distributed ledger technology.
  • new blockchain architecture. 
  • distributed consensus and fault tolerance mechanisms. 
  • security, privacy, and trust of blockchains. 
  • applications and services based on blockchains. 
  • protocols and algorithms based on blockchains. 
  • blockchains in the Internet of things (IoT).
  • blockchains in social networking. 
  • blockchains in supply chain management. 
  • blockchains in edge and cloud computing. 
  • blockchains and artificial intelligence. 
  • meta applications of blockchains. 
  • DeFi, NFTs, and GameFi

Published Papers (4 papers)

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Research

40 pages, 8764 KiB  
Article
Sea Shield: A Blockchain Technology Consensus to Improve Proof-of-Stake-Based Consensus Blockchain Safety
by Sana Naz and Scott Uk-Jin Lee
Mathematics 2024, 12(6), 833; https://doi.org/10.3390/math12060833 - 12 Mar 2024
Viewed by 508
Abstract
In a blockchain network, a rule set called consensus mechanism is used to create and finalize a block. In a proof-of-stake (PoS), consensus-based blockchain network, nodes become validators, minters, or stakeholders’ nodes to complete the consensus mechanism. In these networks, when a node [...] Read more.
In a blockchain network, a rule set called consensus mechanism is used to create and finalize a block. In a proof-of-stake (PoS), consensus-based blockchain network, nodes become validators, minters, or stakeholders’ nodes to complete the consensus mechanism. In these networks, when a node becomes a validator node, its details need to be saved because the details of the validators are used in the network for many important decisions, such as selecting block proposers for the consensus process. In this paper, we present Sea Shield, which uses a validator chain to save a node’s information when it becomes a validator or leaves its responsibility as a validator in the PoS-based blockchain network. The validator chain is a blockchain that can run with the main chain of a PoS-based blockchain. The internal features of the validator chain are similar to those of the blockchain. We designed and simulated a consensus mechanism to create and finalize the block for the validator chain with no forks. We present a process by which a node may join or unjoin as a validator in a PoS-based blockchain network to improve the overall security of the main chain-consensus process. Full article
(This article belongs to the Special Issue Scalability, Sustainability and Security: Searching for New Blockchain Solutions)
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18 pages, 9293 KiB  
Article
Data-Driven Consensus Protocol Classification Using Machine Learning
by Marco Marcozzi, Ernestas Filatovas, Linas Stripinis and Remigijus Paulavičius
Mathematics 2024, 12(2), 221; https://doi.org/10.3390/math12020221 - 09 Jan 2024
Cited by 1 | Viewed by 882
Abstract
The consensus protocol plays a vital role in the performance and security of a specific Distributed Ledger Technology (DLT) solution. Currently, the traditional classification of consensus algorithms relies on subjective criteria, such as protocol families (Proof of Work, Proof of Stake, etc.) or [...] Read more.
The consensus protocol plays a vital role in the performance and security of a specific Distributed Ledger Technology (DLT) solution. Currently, the traditional classification of consensus algorithms relies on subjective criteria, such as protocol families (Proof of Work, Proof of Stake, etc.) or other protocol features. However, such classifications often result in representatives with strongly different characteristics belonging to the same category. To address this challenge, a quantitative data-driven classification methodology that leverages machine learning—specifically, clustering—is introduced here to achieve unbiased grouping of analyzed consensus protocols implemented in various platforms. When different clustering techniques were used on the analyzed DLT dataset, an average consistency of 78% was achieved, while some instances exhibited a match of 100%, and the lowest consistency observed was 55%. Full article
(This article belongs to the Special Issue Scalability, Sustainability and Security: Searching for New Blockchain Solutions)
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17 pages, 1234 KiB  
Article
Addressing the Transaction Validation Issue in IOTA Tangle: A Tip Selection Algorithm Based on Time Division
by Yinfeng Chen, Yaofei Wang, Baojun Sun and Junxin Liu
Mathematics 2023, 11(19), 4116; https://doi.org/10.3390/math11194116 - 28 Sep 2023
Cited by 1 | Viewed by 1282
Abstract
IOTA is a new public chain system specifically designed for the Internet of Things (IoT), which provides strong support for the high concurrency, scalability, and zero handling fees of the IoT. The distributed ledger of IOTA, called the tangle, adopts a Directed Acyclic [...] Read more.
IOTA is a new public chain system specifically designed for the Internet of Things (IoT), which provides strong support for the high concurrency, scalability, and zero handling fees of the IoT. The distributed ledger of IOTA, called the tangle, adopts a Directed Acyclic Graph (DAG) structure. However, compared to the single-chain architecture, the tangle is more complex and highly vulnerable to security threats. The existing transaction verification methods still cannot simultaneously meet the need for accelerating approval speed and improving security to resist illegal transactions, such as lazy tips and permanent tips. In this work, we propose TDTS, a tip-selection algorithm based on time division to improve the efficiency of transaction verification. The main idea of the algorithm is to quickly determine two tips of an incoming transaction that need to be confirmed by sorting tip values in a time slot. It shortens the transaction verification time and reduces the number of lazy tips and permanent tips. A comprehensive theoretical analysis confirmed the effectiveness of our proposed algorithm. Based on 1000 IOTA nodes, the evaluations showed that TDTS can select tips quickly like URTS and resist lazy tips like MCMC. Full article
(This article belongs to the Special Issue Scalability, Sustainability and Security: Searching for New Blockchain Solutions)
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25 pages, 673 KiB  
Article
Enabling High-Quality Machine Learning Model Trading on Blockchain-Based Marketplace
by Chunxiao Li, Haodi Wang, Yu Zhao, Yuxin Xi, Enliang Xu and Shenling Wang
Mathematics 2023, 11(12), 2636; https://doi.org/10.3390/math11122636 - 09 Jun 2023
Viewed by 917
Abstract
Machine learning model sharing markets have emerged as a popular platform for individuals and companies to share and access machine learning models. These markets enable more people to benefit from the field of artificial intelligence and to leverage its advantages on a broader [...] Read more.
Machine learning model sharing markets have emerged as a popular platform for individuals and companies to share and access machine learning models. These markets enable more people to benefit from the field of artificial intelligence and to leverage its advantages on a broader scale. However, these markets face challenges in designing effective incentives for model owners to share their models, and for model users to provide honest feedback on model quality. This paper proposes a novel game theoretic framework for machine learning model sharing markets that addresses these challenges. Our framework includes two main components: a mechanism for incentivizing model owners to share their models, and a mechanism for encouraging the honest evaluation of model quality by the model users. To evaluate the effectiveness of our framework, we conducted experiments and the results demonstrate that our mechanism for incentivizing model owners is effective at encouraging high-quality model sharing, and our reputation system encourages the honest evaluation of model quality. Full article
(This article belongs to the Special Issue Scalability, Sustainability and Security: Searching for New Blockchain Solutions)
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