The digitalization of the architecture, engineering, and construction (AEC) industry has demonstrated the revolutionary potential of integrating blockchain technology with building information modelling (BIM). However, the selection of the most appropriate blockchain solution is a multiple-criteria decision-making (MCDM) problem, which is usually influenced by conflicting criteria and deep uncertainty. To overcome this, the present study proposes an extended Fermatean fuzzy weighted aggregated sum product assessment (Extended FF-WASPAS) model. Unlike existing Fermatean fuzzy WASPAS (FF-WASPAS) methods, which are based on a single expert and may be biased, the proposed model incorporates the evaluations of multiple decision makers (DMKs) through a consensus-driven mechanism to ensure unbiased and accurate results. A case study is conducted to evaluate five leading blockchain platforms, Hyperledger Fabric, Polkadot, Tezos, Ethereum, and Algorand, under eight BIM-related criteria. The result indicates that Ethereum is the best blockchain platform to digitalize BIM compared to the other platforms because it is scalable, interoperable, secure, and has a wide range of applications in the real world. Sensitivity analysis over a wide range of parameter values, as well as DMKs assigned with different weight sets, confirmed the stability of the ranking. Furthermore, a quantitative comparative analysis with FF multiple criteria group decision-making (FF-MCGDM) and FF-WASPAS approaches, as well as a qualitative analysis with existing models in various fuzzy environments, confirms the robustness and reliability. Overall, the study provides a strong, interpretable, and consensus-based decision-support framework with high practical value for AEC stakeholders who wish to deploy secure, transparent, and efficient blockchain-enabled BIM solutions.
The rapid progress in artificial intelligence technologies in recent years has been largely driven by advances in reinforcement learning (RL). RL methods have proven to be highly effective in solving many practical problems. Distributed ledger technologies are finding wide application in the internet of things (IoTs), providing new approaches to solving problems of traditional IoT systems. Consensus is a fundamental component of distributed ledger technologies, responsible for ensuring data consistency between nodes, its security and accuracy. This paper is devoted to the study of the optimal choice of blockchain consensus protocol for IoT networks based on a combination of multi-criteria decision making (MCDM) and RL methods. The paper discusses the potential of merging MCDM and RL methods for selecting blockchain consensus protocols in IoT networks. It suggests a combined framework for effective protocol selection and management.
Open access
Blockchain Technology Applications and Security
Advanced Research in Systems and Signal Processing
Francisco J. Quesada, Francisco Moya, Mercedes Rodríguez-García, Bapi Dutta
Tendering processes aim to provide transparency in the trade of services or goods but often fall short, leading to corruption and loss of trust. The emergence of Distributed Ledger Technologies (DLTs), such as blockchain, has prompted research into their application for enhancing transparency in tendering. However, adopting DLT usually incurs extra costs, network fees, and high carbon footprints. This paper conducts a Multi-Criteria Decision Making (MCDM) process to select the most suitable DLT for tendering processes. As a result, a novel tendering process based on IOTA is proposed, which improves transparency, ensures ecological sustainability, and avoids extra costs. The IOTA-based approach also fosters collaboration between human and computer capabilities in selecting the tender winner. Our method is compared with existing approaches, demonstrating the highest transparency.
Abstract Decentralized finance is upending the financial system through innovative, open, and interoperable financial solutions. Decentralized finance is a rapidly emerging field based on distributed ledger technology. Decentralized banking protocols are witnessing a perfect storm in terms of growth. However, because these financial innovations pose specific risks to consumers, creators, regulators, and other stakeholders, this emerging subject demands careful investigation. The current study tries to categorize and rate the risks connected with decentralized finance. The current study seeks to identify the multiple risks associated with decentralized finance through a thorough literature analysis. Data gathered from specialists in prior research were incorporated into the study used for empirical analysis. As MCDM techniques, IVFF-based DEMATEL, AHP, and TOPSIS are first used, and then the IVFF-ARAS method and sensitivity analysis are used for performance evaluation and verification. The findings of this study have several ramifications for legislators, businesspeople, technologists, and practitioners. These stakeholders can concentrate on these weaknesses in the future and provide longer-lasting solutions.
Weizhong Wang, Yu Chen, Yi Wang, Muhammet Deveci · 6 authors
Abstract Many attempts have been made to identify barriers to blockchain adoption in supply chain; however, barriers to blockchain adoption in supply chain finance (SCF) are underexplored. This study prioritizes barriers to blockchain adoption in SCF and evaluates the barrier level of each alternative participant. We propose an integrated decision model to prioritize the barriers and evaluate their levels of alternative participants. To determine the barriers, we conducted a literature review. We then introduce an integrated weight calculation method by combining interval-valued Fermatean fuzzy (IVFF)-optimistic-pessimistic-utility values-based and IVFF-RS (ranking sum) methods to determine the barrier weights. To evaluate the barrier level of each alternative participant in SCF, the integrated IVFF-RAFSI (Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval) model is presented to rank the barrier, which uses a power-weighted aggregation operator to fuse experts’ opinions. A case study demonstrates the practicality of the integrated IVFF-RAFSI model. The results show that uncertain and competitive markets (weighted at 0.0676) are the most significant barriers. This finding also suggests that small and medium-sized processing enterprises have the highest barriers to blockchain adoption. Sensitivity and comparative analyses validate the steadiness and competency of the proposed model. These results indicate that the proposed methodology provides a systematic technique for analyzing barriers to blockchain applications in SCF.
This study explores the integration of Plithogenic Hypersoft Sets (PHSS) in Multi-Criteria Decision Making (MCDM) for cryptocurrency investment analysis. Given the volatile and unpredictable nature of the cryptocurrency market, traditional decision-making models often fail to capture the complexities and uncertainties present. By incorporating the advanced concept of PHSS, which accommodates multiple membership degrees (fuzzy, intuitionistic fuzzy, neutrosophic), we propose a more robust framework for investment decision-making. The study normalizes cryptocurrency data and applies PHSS to a set of cryptocurrencies (e.g., BTC, ETH, BNB, SOL, and XRP), analyzing key attributes such as price changes, market cap, volume, and circulating supply. Our results demonstrate that PHSS can enhance the accuracy and reliability of financial decision-making, providing valuable insights for investors.
Momna Akhtar, Muhammad Rahim, Agaeb Mahal Alanzi, Sadique Ahmad · 7 authors
This paper presents novel operational laws forp,q,r–spherical fuzzy sets (p,q,r–SFSs) by harnessing the Dombi t-norm (DTN) and t-conorm (DTCN). These laws serve as the foundation for a set of aggregation operators (AOs) designed to consolidatep,q,r–spherical fuzzy (p,q,r–SF) information. Additionally, a multi-criteria decision-making (MCDM) method is outlined for addressing practical decision-making (DM) challenges. To demonstrate the application of the proposed approach, a numerical example is offered. Furthermore, we conducted a comparative study to validate the efficacy of the suggested approach. Finally, we discuss both the advantages and limitations of this innovative approach.
Rohit Raj, Vimal Kumar, Priyanka Verma, Suriya Klangrit
Purpose Though academic study on the subject is still in its early stages, there is growing interest in using blockchain technology for transforming the supply chain. The academic literature is divided and yet only includes studies evaluating how the supply chain has changed organizations. To comprehend the new phenomena, this study aims to investigate the factors of blockchain technology in driving supply chain transformation. To be more precise, the authors developed from the literature the most prevalent criteria for determining if supply chain transformations are ready to be scaled up. Design/methodology/approach This study followed a combination of two multi-criteria decision making methods evaluation based on distance from average solution and complex proportional assessment) methodology in this research: planning, investigating, executing out, establishing a rating of the criteria and evaluating it. Findings The study shows that the “organizational driver” and the “technology driver” are the factors most important to the transformation of the supply chain, whereas the “financial driver” and the “regulatory driver” are less important. This study also makes some managerial recommendations to address the factors impeding the supply chain’s transformation. Each factor’s significance was explored, and a proposed study agenda was also presented. Research limitations/implications Although the main forces behind the transformation of the supply chain have been recognized, further research into statistical correlation is required to confirm how the various elements interact. Practical implications This research aids decision-makers in comprehending the key forces behind supply chain transformation. Managers and decision-makers might better predict and allocate the necessary resources to start the road toward digitization and make well-informed choices once these aspects have been investigated and understood. Originality/value In light of the pandemic’s effects on the world and the increase in businesses embracing the digital economy, the supply chain transformation is more important than ever. Beyond blockchain deployment and the pilot studies on digital transformation, there is a gap. The topics and factors this study uncovered will operate as a framework and recommendations for more theoretical investigation and practical applications.
The rise of connected and smart devices has heightened the demand for robust Internet of Things (IoT) applications, necessitating enhanced data storage and processing. Traditional cloud-based IoT systems, often distant from users, grapple with issues like server load, slow responses, and limited mobility. Edge computing counters some of these problems by bringing processing closer to users. Selecting appropriate nodes also addresses common IoT challenges such as network lifetime, resource allocation, and data reliability, ensuring optimal intervals for node selection. Integrating edge and blockchain tech in IoT introduces new perspectives on access control design. This study introduces an innovative approach using fuzzy logic for node selection in blockchain-powered edge IoT. Fuzzy logic processes numeric and linguistic data concurrently, alongside the Technique of Preference Ranking by Similarity of Ideal Solution (TOPSIS) for Multi-Criteria Decision Making (MCDM). This fuzzy-based method enhances IoT node selection efficiency, with three entry criteria, significantly ameliorating considered parameters, as demonstrated by experimental results.
Environmental impact and sustainability challenges in the cryptocurrencies has become increasingly examined in the literature. However, studies of the multiple attribute group decision making (MAGDM) method for major selection of cryptocurrencies in advancing sustainability are still at an early stage. In particular, research on the fuzzy-MAGDM method in the evaluation of sustainability in cryptocurrencies is scarce. This paper adds contributions by developing a novel MAGDM approach to evaluate the sustainability development of major cryptocurrencies. It proposes a similarity measure for interval-valued Pythagorean fuzzy numbers (IVPFNs) based on whitenisation weight function and membership function in grey systems theory for IVPFNs. It further developed a novel generalised interval-valued Pythagorean fuzzy weighted grey similarity (GIPFWGS) measure approach to provide a more rigorous evaluation in complex decision marking problem with embedding ideal solution and membership degree. It also conducts a sustainability evaluation model of major cryptocurrencies as a numerical application and performs a robustness assessment with different variations of the expert's weight to test how different values of parameter θ can affect the ranking results of alternatives. The results suggest that Stellar is the most sustainable cryptocurrency, while Bitcoin with its intensive energy consumption, high mining cost and high computing power provides the least effective support for its sustainable development. A comparative analysis with the average value method and Euclidean distance method was performed to validate the reliability of the proposed decision-making model and provides evidence that the GIPFWGS has better fault tolerance.
The cryptocurrency, often known as virtual or digital currency, is one of the most significant breakthroughs brought about by digitalization. It has been mentioned recently, an innovative system for stockholders in particular. Cryptocurrencies of all kinds, including Bitcoin, Shib, Dogecoin, and Tether are decentralized. Categorization complicates decision-making (DM) as well as the transfer of uncertainty and digital currency validation. The multi-attribute group decision-making (MAGDM) method is defined in this article using the Frank weighted averaging and Frank weighted geometric aggregation operators (AOs). This research examines the uniqueness of the q-rung orthopair fuzzy hypersoft set (qROFHSS) that is responsive to volatilities, vagueness, doubt, and inaccurate data. Additionally, certain essential qROFHSS topics are covered in this study. The framework presented here is the greatest alternative for understanding electronic money. This research supports the difficulty of situations involving making choices that must take into account a variety of qualities and sub-attributes to select the best choice. We see that Bitcoin has a variety of applications and that cryptocurrencies have an excellent chance to emerge as a prominent benefit lesson in financial choice-making.
Group Decision-making (GDM) is very much required and practiced in healthcare, also through the healthcare decision support system (HDSS). However, it may lack transparency and trust in balancing the risk and expected decision. Also, the absence of trust can heavily undermine the decision quality. We here propose a blockchain-based solution using smart contract to facilitate the decision-making process in healthcare based on the decentralized technology. In general, in a GDM and particularly in healthcare, the opinions expressed by the expert may have some degree of risk associated with it, and we address this issue through 2-Tuple fuzzy preference representation. We propose a consensus-based GDM model that uses a 2-Tuple fuzzy preference relation for smart contract. Remix IDE is used to develop and test the smart contract code in the Ethereum blockchain environment. We demonstrate the applicability of the proposed blockchain-based solution and its security and cost analysis.
With the rapid development of e-commerce systems, the centralized service model gradually fails to meet the needs of SMEs. In the existing centralized e-commerce system, users’ transaction data and reputation scores are stored in a centralized cloud server, which has high storage cost, low processing efficiency, and the data is vulnerable to attacks and leaks. However, the existing decentralized e-commerce systems more than its reputation system to store the average credit score evaluation are receiving unfair evaluations against risk. The system of malicious nodes and no disciplinary measures, is not conducive to system development. To solve this problem, this paper proposes a blockchain-based decentralized e-commerce transaction system. The system commodity information is stored in the Interplanetary File System (IPFS) and the returned commodity addresses are stored in the blockchain to enhance the service performance. This paper proposes a reputation evaluation model based on multi-criteria decision making (MCDM), which can effectively resist unfair evaluation and collusion attacks, and proposes an incentive mechanism based on reputation value to reward and punish nodes, thus promoting the good circulation of the system. We implement the proposed system based on Ethereum. The experimental results show that the system has a small communication cost, accurately reflects the user’s reputation value, and has good availability and reliability.
Nada A. Nabeeh, Mohamed Abdel‐Basset, Abduallah Gamal, Victor Chang
A blockchain, as a form of distributed ledger technology, represents the unanimity of replication, synchronization, and sharing of data among various geographical sites. Blockchains have demonstrated impressive and effective applications throughout many aspects of the business. Blockchain technology can lead to the advent of the construction of Digital Twins (DTs). DTs involve the real representation of physical devices digitally as a virtual representation of both elements and dynamics prior to the building and deployment of actual devices. DT products can be built using blockchain-based technology in order to achieve sustainability. The technology of DT is one of the emerging novel technologies of Industry 4.0, along with artificial intelligence (AI) and the Internet of Things (IoT). Therefore, the present study adopts intelligent decision-making techniques to conduct a biased analysis of the drivers, barriers, and risks involved in applying blockchain technologies to the sustainable production of DTs. The proposed model illustrates the use of neutrosophic theory to handle the uncertain conditions of real-life situations and the indeterminate cases evolved in decision-makers’ judgments and perspectives. In addition, the model applies the analysis of Multi-criteria Decision Making (MCDM) methods through the use of ordered weighted averaging (OWA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to achieve optimal rankings for DT production providers based on consistent weighted decision-maker’s judgments in order to maintain and to assure sustainability. An empirical study is applied to the uncertain environment to aid decision-makers in achieving ideal decisions for DT providers with respect to various DT challenges, promoting sustainability and determining the best service providers. The Monte Carlo simulation method is used to illustrate, predict, and forecast the importance of the weights of decision-makers’ judgments as well as the direct impact on the sustainability of DT production.
Ümit Cali, Muhammet Deveci, Shammya Shananda Saha, Ugur Halden · 5 authors
The modern power systems are evolving in parallel to the development of other technological trends such as decarbonization and digitalization. While the penetration of renewable energy resources is increasing within the national and regional energy mix, emerging digitalization technologies, such as artificial intelligence and blockchain technology are shaping modern power systems. Especially blockchain technology has a very high potential to disrupt the current and future energy sector landscape by enabling various use cases in this domain. This paper aims to prioritize different energy use cases where blockchain technology can actively be utilized to create additional value. This study proposes a Type-2 Neutrosophic Number (T2NN) based Evaluation based on Distance from Average Solution (EDAS) to evaluate and rank a set of existing use cases of an energy blockchain system. Testing and validation of the model is done through a comparison against one alternative T2NN based Multi-Criteria Decision Making (MCDM) model and an existing approach from literature. In addition, a sensitivity analysis is performed, revealing that changing criteria weightings do not affect the ranking order of the use cases of the energy blockchain system. Prioritizing the use cases can assist the companies, standardization bodies, and related government authorities to make better decisions for their operations, such as ranking the investment decisions.
Rana Muhammad Zulqarnain, Imran Siddique, Sayed M. Eldin, Shahid Hussain Gurmani
One of the substantial innovations achieved through digitalization is cryptocurrencies, also known as simulated or digital currencies, which have been deliberated in the modern era as a new platform particularly suitable for financiers. Several cryptocurrencies, such as Bitcoin, Ethereum, Binance Coin, and Tether, do not trust a dominant expert. The classification and conduction of insecurity and the confirmation of digital currencies complicate decision-making. q-rung orthopair fuzzy hypersoft sets are an emerging arena of research intended to report the confidential restrictions of q-rung orthopair fuzzy soft sets on multiparameter indefinite functions. Such a function maps a tuple of sub-parameters to a power set of the universe. It emphasizes allocating attributes to their corresponding sub-attribute values in disjoint sets. These structures sort it an innovative systematic tool for addressing the obstacles of hesitancy. The q-rung orthopair fuzzy hypersoft set (q-ROFHSS) expertly compacts with tentative and ambagious facts equated to the existing q- rung orthopair fuzzy soft set and Pythagorean fuzzy hypersoft set (PFHSS). It is the most compelling mode for enlarging imprecise data in decision-making (DM). This investigation’s ultimate impartiality is presenting interactional algebraic operational laws for q-ROFHSS. Furthermore, some interaction aggregation operators (AOs) have been anticipated via our proposed operational laws, such as q-rung orthopair fuzzy hypersoft interactive weighted average (q-ROFHSIWA) and q-rung orthopair fuzzy hypersoft interactive weighted geometric (q-ROFHSIWG) operators with their essential properties. In reality, a mathematical illustration of DM obstacles is pondered to substantiate the proven technique’s dominance. Based on the projected interaction AOs, robust multi-criteria group decision-making (MCGDM) design has been offered, which carries the most practical consequences associated with predominant MCGDM methods. The significance spectacle is that the intentional methodology is more operative and steady in bearing weird facts based on q-ROFHSS.
Salma Khan, Muhammad Gulistan, Nasreen Kausar, Sajida Kousar · 6 authors
One of the most important innovations brought by digitization is the cryptocurrency, also called virtual or digital currency, which has been discussed in recent years and in particular is a new platform for investors. Different types of cryptocurrencies such as Bitcoin, Ethereum, Binance Coin, and Tether do not depend on a central authority. Decision making is complicated by categorization and transmission of uncertainty, as well as verification of digital currency. The weighted average and weighted geometric aggregation operators are used in this article to define a multi‐attribute decision‐making approach. This work investigates the uniqueness of q‐rung orthopair fuzzy hypersoft sets (q‐ROFHSS), which respond to instabilities, uncertainty, ambiguity, and imprecise information. This research also covers some fundamental topics of q‐ROFHSS. The model offered here is the best option for learning about electronic currency. This study validates the complexity of decision‐making problems with different attributes and subattributes to obtain an optimal choice. We conclude that Bitcoin has a diverse set of applications and that crypto assets are well positioned to become an important asset class in decision making.