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Review

Blockchain Consensus Mechanisms: A Bibliometric Analysis (2014–2024) Using VOSviewer and R Bibliometrix

by
Joongho Ahn
1,
Eojin Yi
2 and
Moonsoo Kim
3,*
1
Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea
2
The National Assembly of the Republic of Korea, Seoul 07233, Republic of Korea
3
CEO Business School, Seoul 04520, Republic of Korea
*
Author to whom correspondence should be addressed.
Information 2024, 15(10), 644; https://doi.org/10.3390/info15100644
Submission received: 23 September 2024 / Revised: 11 October 2024 / Accepted: 13 October 2024 / Published: 16 October 2024
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Abstract

:
Blockchain consensus mechanisms play a critical role in ensuring the security, decentralization, and integrity of distributed networks. As blockchain technology expands beyond cryptocurrencies into broader applications such as supply chain management and healthcare, the importance of efficient and scalable consensus algorithms has grown significantly. This study provides a comprehensive bibliometric analysis of blockchain and consensus mechanism research from 2014 to 2024, using tools such as VOSviewer and R’s Bibliometrix package. The analysis traces the evolution from foundational mechanisms like Proof of ork (PoW) to more advanced models such as Proof of Stake (PoS) and Byzantine Fault Tolerance (BFT), with particular emphasis on Ethereum’s “The Merge” in 2022, which marked the historic shift from PoW to PoS. Key findings highlight emerging themes, including scalability, security, and the integration of blockchain with state-of-the-art technologies like artificial intelligence (AI), the Internet of Things (IoT), and energy trading. The study also identifies influential authors, institutions, and countries, emphasizing the collaborative and interdisciplinary nature of blockchain research. Through thematic analysis, this review uncovers the challenges and opportunities in decentralized systems, underscoring the need for continued innovation in consensus mechanisms to address efficiency, sustainability, scalability, and privacy concerns. These insights offer a valuable foundation for future research aimed at advancing blockchain technology across various industries.

Graphical Abstract

1. Introduction

Blockchain technology, first introduced through Bitcoin, has fundamentally transformed digital transactions by removing the need for a central authority to mediate exchanges [1]. At the core of blockchain’s success are consensus mechanisms—protocols that enable decentralized networks to agree on the validity of transactions, ensuring the integrity and security of the distributed ledger [2]. Since Bitcoin’s launch in 2009, the Proof-of-Work (PoW) consensus algorithm has been focal in securing blockchain networks. However, as blockchain technology has evolved, so too have the demands on consensus mechanisms, leading to the development of alternative approaches, such as Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and Byzantine Fault Tolerance (BFT) [3,4,5]. Table 1 provides an overview of popular consensus mechanisms [3].
Over the past decade, research into blockchain consensus mechanisms has grown substantially, reflecting the expanding scope and complexity of blockchain applications. This growth is evidenced by the emergence of various consensus algorithms tailored to address specific challenges, such as scalability, security, and energy efficiency [6,7]. Previous studies provide a comprehensive overview of these mechanisms and their implications for network performance, emphasizing the necessity of adapting protocols to meet the evolving demands of blockchain use cases [5]. Systematic literature reviews further illustrate the continuous evolution and adaptation of these protocols in response to technological advancements and market needs [4,8,9,10,11,12,13]. Additionally, bibliometric analyses have been instrumental in quantifying the impact, trends, and structures within the field of blockchain and consensus mechanisms, offering insights into the shifting focus and future directions of research [14,15,16,17,18,19].
Previous studies provide broad overviews of consensus mechanisms and their impact on network performance but often focus on individual algorithms like PoW or PoS. While some have discussed Ethereum’s transition from PoW to PoS during “The Merge” in 2022, which significantly improved energy efficiency and sustainability, they lack a comprehensive bibliometric analysis that tracks the evolution of consensus mechanisms over a decade-long period. This study fills this gap by focusing on the 2014–2024 timeframe, a critical decade in blockchain research marked by significant milestones, including the introduction of Ethereum in 2015 and subsequent network upgrades that have greatly influenced consensus mechanisms [20]. A detailed summary of the changes in Ethereum’s consensus mechanisms from 2015 to 2024 is provided in Table 2 [3]. These developments, especially “The Merge”, highlight the dynamic nature of blockchain technology and the substantial shifts in research focus during this decade [21].
By thoroughly examining these trends, this study aims to identify key milestones and shifts in research focus that have shaped the development of blockchain consensus mechanisms. Through mapping these changes, the paper provides a comprehensive understanding of how technological advancements—such as the transition from PoW to PoS—have influenced both the theoretical foundations and practical implementations of consensus protocols. Additionally, this research explores emerging challenges and opportunities within the blockchain ecosystem, offering insights into future developments in decentralized systems.
The evolution of blockchain technology and its consensus mechanisms has been characterized by significant advancements driven by the increasing complexity of applications and the growing need for efficient, secure, and scalable solutions [22]. This paper contributes to the literature by providing bibliometric analysis of trends in blockchain consensus mechanisms, along with their implications for future research and applications. Our analysis focuses on three distinct periods of development, each marked by significant milestones in blockchain technology:
  • Foundational research period (2014–2019): This period marks the groundwork for understanding and developing consensus mechanisms. It includes the launch of Ethereum in 2015, which introduced the first major alternative to PoW with its vision of smart contracts and decentralized applications. Ethereum’s introduction, followed by the significant network upgrades in 2019 (Constantinople and Istanbul), laid the foundation for exploring new consensus models like PoS and set the stage for future developments.
  • Expansion and application period (2020–2021): This phase saw a broadening of research to include more diverse applications and integration with emerging technologies. The upgrades during this period, particularly those leading up to Ethereum 2.0, reflected the blockchain community’s growing interest in optimizing scalability, security, and energy efficiency. Research during this time focused on enhancing existing consensus mechanisms and exploring their applications in various domains beyond cryptocurrencies [23].
  • Advanced applications and challenges period (2022–2024): This period has been characterized by advanced applications and emerging challenges in scalability, security, and efficiency in consensus protocols. The transition of Ethereum from PoW to PoS in 2022, known as “The Merge”, represented a significant shift in the consensus mechanism landscape. This event spurred new research into the implications of PoS and other consensus models on the broader blockchain ecosystem, particularly concerning sustainability, decentralization, and security [24,25].
To guide this review, we address the following research questions, specifically tailored to focus on both theoretical development and practical implementation aspects of blockchain consensus mechanisms:
  • RQ1: How have blockchain and consensus mechanism research publications evolved over time in terms of volume and subject focus?
  • RQ2: Which authors, institutions, and countries have had the most influence on blockchain and consensus research?
  • RQ3: What are the key subject areas and emerging themes in blockchain and consensus research?
  • RQ4: What are the major trends in blockchain and consensus mechanisms, and how have they shifted over time?
  • RQ5: How have innovations in consensus mechanisms, such as Ethereum’s shift from PoW to PoS, influenced the direction and practical applications of blockchain research?
The remainder of the paper is structured as follows:
Section 2 outlines the methods and data, describing the tools used (VOSviewer and R Biblioshiny) and the data collection process. Section 3 presents the VOSviewer analysis (focusing on citation networks, co-authorship, and keyword co-occurrence) and the R Biblioshiny analysis (covering science mapping and thematic evolution), integrating the results and discussion to provide a holistic view of blockchain consensus research trends. Section 4 synthesizes the key insights, offering a comprehensive conclusion, and highlights future research directions based on the inferences drawn from this bibliometric review analysis. By providing a comprehensive bibliometric analysis of blockchain consensus mechanisms, this study seeks to offer valuable insights for researchers and practitioners, helping to enhance the security, efficiency, and scalability of decentralized networks.

2. Methods and Data

2.1. Data Collection

The data for this bibliometric analysis were sourced from the Web of Science (WoS) database, renowned for its extensive and high-quality coverage of peer-reviewed scientific literature [26,27]. This study focused on examining research trends related to blockchain consensus mechanisms using WoS’s advanced search features.
To ensure relevance, we focused on documents containing both “blockchain” and “consensus” in the title (TI), topic (TS), or abstract (AB). The search query was formulated as follows:
TI = (blockchain) AND TI = (consensus) OR TS = (“blockchain” AND “consensus”) OR AB = (“blockchain” AND “consensus”)
This advanced search retrieved publications that included both “blockchain” and “consensus”, covering the research period from 2014 to 2024 to capture the most recent developments in the field. The data collection took place on 2 August 2024, resulting in a total of 1872 publications. The dataset was then analyzed in two stages using VOSviewer and the R Bibliometrix package to examine relationships, performance, and trends in the field of blockchain consensus mechanisms.

2.2. Bibliometric Analysis with VOSviewer and R Biblioshiny

In the first stage of analysis, VOSviewer, a tool designed for constructing and visualizing bibliometric networks, was employed to explore the relationships among keywords, authors, and institutions in the collected dataset [18,28]. Key analyses using VOSviewer included the following:
  • Citation analysis: Citation patterns were examined to identify the most influential authors, organizations, and countries, providing insight into the key contributors and leading research within the blockchain consensus domain.
  • Co-authorship analysis: By mapping collaboration patterns among authors, institutions, and countries, this analysis identified the major research networks and tracked changes in collaboration over time.
  • Keyword co-occurrence and clustering analysis: The most frequently occurring keywords were visualized to identify the dominant research themes and emerging trends, offering a comprehensive view of the research focus within the field.
In the second stage, R Biblioshiny—an interactive application within the R bibliometrix package—was used to conduct more detailed bibliometric analyses. Biblioshiny offers advanced capabilities for performance analysis, science mapping, and thematic evolution studies [29]. Specific analyses conducted in this stage included the following:
  • Science mapping: Through co-word analysis, thematic mapping, and cluster analysis, the intellectual and conceptual structure of the blockchain research domain was explored, uncovering connections and thematic groupings within the literature.
  • Thematic evolution: This analysis tracked the progression of key research themes over the three distinct periods (2014–2019, 2020–2021, and 2022–2024). Using Sankey diagrams and strategic diagrams, this method visualized how research themes emerged, merged, or evolved, providing insights into the shifting focus of blockchain consensus research [30,31]. The overall analytical framework and workflow of this study is illustrated in Figure 1.
Combining VOSviewer’s network-based visualizations with R Biblioshiny’s in-depth thematic and strategic analyses, this study offers a comprehensive understanding of research trends, emerging topics, and collaborative patterns within the field of blockchain consensus mechanisms [32]. These insights can guide future research and practical applications in enhancing the security, scalability, and efficiency of decentralized networks.

3. Results and Discussion

3.1. Overview of Bibliometric Analysis

The bibliometric analysis of publications from 2014 to 2024 provided significant insights into the progression and focus within the field of blockchain consensus mechanisms. Data from the WoS demonstrated the expanding volume of scholarly work, with a notable annual growth of 39.55% in publications. As summarized in Table 3, the analysis covered a total of 1872 documents published across 1134 sources, with an average document age of 3.7 years and a citation rate of 12.78 per document. The study amassed 26,366 references, indicating the depth of research in this area. Additionally, the analysis identified 289 keywords plus and 3090 authors’ keywords, reflecting the diversity of research topics and terminologies within the field. A total of 5477 authors contributed to this body of work, with only a small fraction (85 authors) producing single-authored documents. On average, there were 3.77 co-authors per document, underscoring the collaborative nature of research into blockchain. Furthermore, the data showed a strong rate of international collaboration, with 26.5% of publications involving authors from multiple countries.
The annual production of publications (Table 4, Figure 2) reached its peak in 2019, with 378 publications. Following this highest point, there was a slight decline. This trend may indicate that the field of blockchain consensus research is entering a maturation phase or that focus is shifting towards emerging research areas.
Country-specific data (Figure 3) revealed that production over time has included significant contributions from countries like the USA, China, and the UK, with China demonstrating a steep increase in publications over recent years. Affiliative productivity (Figure 4) showcased institutions like the Chinese Academy of Sciences and Beijing University of Posts and Telecommunications as key contributors to blockchain research, leading in the number of publications. The analysis of the most relevant affiliations (Figure 5) and authors (Figure 6) provided a focus on leading entities and individuals driving the research in this area. Notable researchers such as Jelena Misic and Vojislav B. Misic emerged as prolific contributors.
The word cloud (Figure 7) visually summarizes the most frequent terms associated with blockchain and consensus research, with words like “blockchain”, “security”, and “consensus” being prominently featured. This visualization not only reflects the thematic richness of the field but also aligns with observed shifts in research focus documented over the study period.
Overall, this bibliometric analysis offers insights into the dynamics of blockchain consensus research, showcasing the key players, collaboration patterns, and evolving themes within this vital technological arena.

3.2. Bibliometric Analysis of the Citations and Publications

Using VOSviewer, we conducted an in-depth bibliometric analysis, focusing on citation networks to reveal scholarly impact and collaboration patterns among authors, organizations, and countries. The analysis used citations as the weight, measuring the influence of each entity according to the frequency with which their work has been cited in other publications [33]. In these networks, each author, organization, or country was represented as a node, where the size of the node correlated with the total number of citations received. Larger nodes indicated higher citation counts, suggesting greater influence in the field. Co-citation relationships are represented by links between nodes, with the thickness of these links reflecting the strength (or frequency) of co-citations. Entities clustered together suggest thematic or collaborative connections. To ensure that the analysis focused on significant contributors, a minimum threshold of five documents was set for authors, organizations, and countries.
In Figure 8a, 66 out of 5477 authors met this threshold. The citation links between these authors were calculated based on total link strength, identifying key figures in the blockchain consensus research. Not all authors were interconnected within the network, and the largest set of connected authors included 49 items across eight clusters, with 165 links and a total link strength of 359. Marko Vukolić, in the blue cluster, emerged as the most influential author, with 26 links, a link strength of 72, and a total of 2455 citations. His highly cited works, such as the 2018 paper “Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains”, underscore his significant contribution to the field. Key collaborations with other researchers, including Christian Cachin and Alessandro Sorniotti, further emphasize his prominence in the research landscape.
For organizations, as seen in Figure 8b, 141 out of 1682 met the criteria of having produced at least five documents. The largest connected set consisted of 137 organizations, forming 11 clusters with 1072 links and a total link strength of 1589. The IBM Corporation, positioned in the yellow cluster, was the most cited organization with 84 links, a link strength of 143, and 2377 citations, demonstrating its influence and leadership in the blockchain consensus field.
When analyzing countries, as shown in Figure 8c, 49 out of 84 countries met the threshold. The largest interconnected network involved 40 countries across 10 clusters, with 402 links and a total link strength of 3085. The USA, located in the green cluster, emerged as the most cited country, with 47 links, a link strength of 1497, and a total of 9198 citations from 401 documents. Although China contributed more documents (621), it accumulated fewer citations (7009), indicating the USA’ higher citation impact per document.

3.3. Bibliometric Analysis of the Co-Authorship

3.3.1. Network Analysis of Authors and Organizations

Bibliometric co-authorship analysis in VOSviewer revealed collaborative relationships among authors and organizations within this specific research field. This type of analysis emphasizes collaboration patterns, helping to identify key contributors and the most interconnected networks based on total link strength [34]. In this context, total link strength measures the cumulative strength of collaborative ties, giving a clear picture of how extensively and intensively researchers and institutions work together.
For the authors included in Figure 9a, a threshold of a minimum of three documents was set, resulting in 337 out of 5477 authors meeting this criterion. However, not all of these authors were connected within the co-authorship network. The largest connected group consisted of 112 authors, forming 14 clusters with 215 links and a total link strength of 393. This analysis identified prominent figures such as Aggelos Kiayias, Chair in Cyber Security and Privacy and director of the Blockchain Technology Laboratory at the University of Edinburgh. Kiayias authored 13 documents and maintained nine direct collaborative links, achieving the highest total link strength of 22, underscoring his significant collaborative presence in the blockchain research community.
Some researchers, such as Marko Vukolić, stand out for high citation counts—Vukolić, with five authored documents, amassed 2455 citations, while his total link strength of 6 reflects fewer collaborative efforts. This distinction shows that a high citation count does not necessarily correlate with extensive collaboration, as seen with Kiayias. Figure 9a illustrates how both high-impact, well-cited authors and those with broad collaborative networks contribute to the field.
For the organizations included in Figure 9b, a higher threshold of a minimum of five documents was established, resulting in 141 out of 1682 organizations meeting the criterion for inclusion in the co-authorship analysis. As with the author analysis, not all organizations were interconnected within the network. The largest connected component comprised 124 organizations, forming 14 clusters with 306 links and a total link strength of 425. The Chinese Academy of Sciences emerged as the most prominent organization, with 30 documents, 348 citations, and the highest total link strength of 33, demonstrating its strong collaborative presence in blockchain and consensus research. The Hong Kong Polytechnic University ranked second, with 17 documents, 287 citations, and a total link strength of 28, indicating a well-established network of collaborations.
In contrast, the IBM Corporation, with only five documents and a high citation count of 2377 but a low total link strength of 4, presented a different profile. While IBM’s research has had a notable impact, the organization is less embedded in co-authorship networks compared with institutions like the Chinese Academy of Sciences. This comparison between collaborative engagement and citation impact underscores the diverse roles organizations play in advancing blockchain and consensus research. Figure 8b illustrates these dynamics, demonstrating how some entities, such as the Chinese Academy of Sciences, excel through broad collaboration, while others, like IBM, achieve significant impact with fewer but high-quality publications.

3.3.2. Network Analysis of Countries’ Co-Authorship and Changes over Time

The co-authorship analysis of countries was conducted in VOSviewer with a minimum threshold of five documents, to include countries with substantial research output. Among 84 countries analyzed, 49 met this threshold and were interconnected within the co-authorship network. These 49 countries formed a network comprising 10 clusters with a total of 234 links and a cumulative link strength of 708.
In Figure 10a, China and the USA are both positioned in the blue cluster, indicating their dominant collaborative roles in the research field. China, with 621 documents and 7009 citations, had 31 links and a total link strength of 243, indicating its extensive collaboration with other nations. The USA, with 401 documents and a higher citation count of 9198, had 37 links and a total link strength of 214. This comparison reveals the USA’s greater citation impact despite having fewer publications, reflecting its substantial influence in blockchain and consensus research.
Figure 10b illustrates the temporal evolution of research collaborations among countries. In this overlay visualization, the size of each node represents the number of documents produced by each country, and the color gradient (ranging from purple and blue to green and yellow) indicates the average publication year, from 2014 to 2024. The purple nodes signify earlier research contributions, with countries such as Singapore, Germany, South Korea, and the USA playing central roles in the foundational development of blockchain and consensus research. These countries were key contributors during the initial phases of the research, helping to establish the groundwork that continues to influence the field. Their early collaborative efforts laid a solid foundation upon which recent developments have been built.
The visualization also underscores the growing strength of international collaborations over the years. As seen in Figure 10b, countries like Spain, Denmark, and Austria, represented by yellow nodes, have become increasingly active in the most recent blockchain research. This expansion of cross-border partnerships signals a promising trend, with new regions contributing to significant advancements in the field. The broadening of the research network offers fresh opportunities for collaboration, fostering innovation and strengthening connections between countries that were previously less interconnected in the field of blockchain research.
Examining the distribution of countries in the co-authorship network, the geographical spread of blockchain and consensus research publications becomes more apparent. Larger nodes, such as those representing China and the USA, indicate higher volumes of published articles. While strong collaboration already exists among key leaders in the field, the inclusion of newer contributors such as Spain, Denmark, and Austria demonstrates an encouraging expansion of international partnerships. To further advance global blockchain research, fostering increased cooperation and communication among a broader range of countries and institutions will be essential, helping to integrate emerging research hubs into the global network.

3.4. Bibliometric Analysis of the Keywords: Key Research Topics and Emerging Trends

3.4.1. Keyword Co-Occurrence Patterns and Clusters

All keywords that appeared more than five times in the WoS core database were included in the co-occurrence analysis, and a thesaurus was applied to standardize terms. Out of 3243 keywords, 171 met the threshold for inclusion. As seen in Table 5, the keyword “blockchain” held the highest total link strength of 2672, with 169 links and 1241 occurrences, solidifying its central role in the research network. Following closely was “consensus” with a total link strength of 803, 129 links, and 295 occurrences, reflecting its significant connection to other important keywords like “smart contract” and “Internet of Things”.
The co-occurrence analysis, visualized using a clustering technique, identified four distinct clusters, as shown in Figure 11a. In this visualization, each keyword is represented as a circle, with its size proportional to the keyword’s total link strength, indicating its significance and influence within the research network. The different colors represent the clusters, which group related terms together. The curved lines connecting the circles depict relationships between co-occurring keywords, with the thickness of the lines reflecting the strength of these connections. These clusters effectively illustrate the interconnections between key topics and research areas in the blockchain field.
  • Cluster 1 (Red)—Blockchain and related technologies (keywords: blockchain, Ethereum, smart contract, Hyperledger Fabric, artificial intelligence): This cluster is centered around the term “blockchain”, which is the largest node in the network, underscoring its foundational role in blockchain research. It captures efforts to understand and enhance blockchain’s underlying mechanisms and applications. Key elements in this cluster include “smart contracts”, which facilitate decentralized applications (dApps) by enabling automated and transparent transactions. The prominence of “Hyperledger Fabric”, a widely adopted framework for blockchain solutions, demonstrates its significance in both academic research and industry applications [35]. The inclusion of “artificial intelligence” (AI) in this cluster further underscores the growing convergence between blockchain and AI, particularly in enhancing decentralized systems and automating decision-making processes [4,36].
  • Cluster 2 (Green)—Internet of Things (IoT) and security (keywords: security, Internet of Things, privacy, trust, authentication): This cluster focuses on the convergence of blockchain with IoT and security concerns. It explores how blockchain technology can be utilized to improve security, privacy, and data integrity in IoT ecosystems. Key topics such as fog computing and authentication are integral to this cluster, reflecting research aimed at securing decentralized networks and effectively managing data across distributed devices [37]. Privacy concerns remain central, emphasizing the ongoing challenge of protecting sensitive information in interconnected systems [38].
  • Cluster 3 (Blue)—Consensus mechanisms and scalability (keywords: consensus, scalability, Byzantine Fault Tolerance, distributed system): This cluster revolves around consensus mechanisms, which are essential for ensuring the scalability and reliability of blockchain networks. It includes advanced models like BFT, which help maintain agreement among distributed nodes [39]. The focus on scalability reflects the challenge of managing large-scale blockchain networks while preserving performance and security [40]. This cluster emphasizes the critical role that consensus plays in maintaining the integrity and functionality of blockchain systems.
  • Cluster 4 (Yellow)—Bitcoin and advancing consensus mechanisms (keywords: Bitcoin, Proof of Work, Proof of Stake, distributed ledger technology, distributed consensus): This cluster centers on cryptocurrencies, particularly Bitcoin, and the evolution of consensus mechanisms from PoW to PoS. The transition to PoS is driven by the need to reduce energy consumption and enhance decentralization within blockchain networks [41,42,43]. Research in this area examines the implications of these consensus models for transaction validation and the broader landscape of distributed ledger technology [44,45] The focus on Bitcoin underscores its influence on the development of blockchain technology and innovations that support its operation, including the exploration of new consensus protocols that aim to improve transaction efficiency and security [42,44].
To sum up, the clustering analysis of blockchain and consensus research reveals a multifaceted landscape where various themes intersect, including foundational technologies, security in IoT, consensus mechanisms, and the evolution of cryptocurrencies. Each cluster contributes to a deeper understanding of the challenges and opportunities presented by blockchain technology.

3.4.2. Temporal Shifts in Keyword Clustering

The temporal evolution of blockchain and consensus research is a complex phenomenon that reflects the dynamic nature of the field. Figure 11b illustrates this evolution through an overlay visualization of the co-occurrence network, which emphasizes the shifting focus of research topics over time. The color gradient in the visualization serves to delineate different research phases, with earlier periods represented by purple and blue, while more recent trends are indicated by green and yellow. This approach enables a nuanced understanding of how research interests have transformed, revealing that these phases have not been strictly confined to specific clusters but have rather overlapped and evolved across different periods as follows:
  • Early Research Phase: In the initial stages of blockchain research, the primary focus was on foundational topics such as Bitcoin, PoW, and decentralization, encapsulated in Cluster 4. This period was characterized by an exploration of the mechanics of blockchain technology and its implications for financial systems [46,47]. The early interest in securing decentralized systems is reflected in the keywords associated with this phase, which emphasize the significance of Bitcoin and its underlying consensus mechanisms [48,49];
  • Mid-Era Research Phase: As the field matured, attention shifted towards practical applications of blockchain technology beyond cryptocurrencies. This transition is evident in the prominence of Cluster 1, which encompasses blockchain’s core technologies, including smart contracts, Ethereum, and Hyperledger Fabric. Concurrently, Cluster 2 gained traction, focusing on the integration of blockchain with the IoT and security concerns [8,50]. This phase marked the emergence of dApps and the application of blockchain for enhancing privacy and data management within IoT systems [51];
  • Recent Research Phase: The latest trends in blockchain research reflect an increasing integration with advanced technologies such as AI, edge computing, and federated learning [52]. In this context, Cluster 3 has gained importance, particularly concerning advanced consensus mechanisms like BFT, which address scalability and security challenges [53]. Simultaneously, Cluster 1 continues to explore blockchain applications, particularly in energy trading, smart grids, and AI, indicating a shift towards more sophisticated, multi-disciplinary applications [54,55]. This convergence of blockchain and AI underscores their transformative potential across industries [55]. The evolution from blue to yellow in the visualization underscores the ongoing transition towards advanced and scalable blockchain solutions.
In summary, the overlay analysis of blockchain research revealed a trajectory from an initial focus on cryptocurrency to a broader exploration of multi-disciplinary applications involving IoT, AI, and energy systems. The overlapping nature of the clusters across different periods emphasizes the fluidity of research interests and the continuous evolution of the blockchain landscape.

3.5. Themetic Analysis: Evolution of Themes and Future Development

3.5.1. Thematic Evolution Analysis

The thematic evolution of blockchain consensus research reveals distinct phases that demonstrate the development and progression of the field. This analysis employed co-word network analysis and clustering, following the methodology outlined by Cobo et al. [56]. The research is categorized into three key periods: foundational research (2014–2019), expansion and application (2020–2021), and advanced applications and challenges (2022–2024). Figure 12 illustrates the emergence, merging, and evolution of key research themes across these periods. The number of documents analyzed included 688 publications from 2014 to 2019, 643 from 2020 to 2021, and 541 from 2022 to 2024, as shown in Figure 13.
  • Foundational research (2014–2019): This initial phase focused on the development and understanding of the foundational consensus mechanisms critical to the security and trustworthiness of blockchain networks. Much of the research centered on Bitcoin, the pioneering cryptocurrency, exploring its underlying technology, economics, and security implications. Researchers also investigated methods to secure blockchain systems while addressing challenges related to privacy, integrity, and scalability [5,57]. During this phase, early work laid the groundwork for future decentralized applications, and there was an exploration of how blockchain could be integrated with existing internet technologies [4].
  • Expansion and application (2020–2021): The second phase marked a shift toward practical applications of blockchain beyond cryptocurrencies. There was increased focus on integrating blockchain with the IoT and other connected systems. While Bitcoin continued to be central due to its economic importance, research diversified into areas such as agreement protocols and privacy concerns, to meet the growing security needs of more complex blockchain networks [58,59]. Proof mechanisms such as PoW and PoS became more nuanced, with researchers analyzing their efficiency, scalability, and environmental impact in the context of various applications [60,61].
  • Advanced applications and challenges (2022–2024): During this period, research has focused on addressing the challenges posed by the widespread adoption of blockchain technology. One of the most significant developments in this phase has been the shift from PoW to PoS, exemplified by Ethereum’s historic “The Merge” in 2022. This transition represents a major milestone in blockchain evolution, as PoS offers improvements in efficiency, security, and sustainability compared with PoW [3,24]. Not only does this shift address the environmental and scalability limitations of PoW, but it also reflects the increasing demand for blockchain systems that can adapt to diverse applications across industries. This is particularly evident in sectors like supply chain management and healthcare, where blockchain has the potential to significantly enhance transparency, efficiency, and security [9,62,63]. As blockchain technology continues to evolve, research has increasingly turned to overcoming challenges associated with scalability and security—issues that remain at the forefront of discussions on blockchain adoption. These concerns emphasize the need for ongoing innovation in consensus mechanisms, which are critical for ensuring the technology’s long-term viability [64]. This period marks a defining moment in blockchain’s evolution, with its potential to transform industries becoming increasingly evident. Privacy also remains a critical issue, especially as blockchain applications expand into sensitive sectors such as healthcare and finance, where data protection is of paramount importance [21,65]. While consensus mechanisms and internet integration remain central themes, research has shifted toward specialized implementations, such as federated learning and edge computing. These approaches allow more sophisticated, decentralized data management and highlight the need for blockchain networks to be efficiently governed and integrated with existing technologies. Ensuring scalability while maintaining security and efficiency continues to be a key research focus [21].
In summary, the thematic evolution of blockchain consensus research over time shows a clear trajectory from foundational studies centered on Bitcoin and early consensus mechanisms to broader explorations of practical applications and advanced challenges. Each phase reflects blockchain’s evolving role in addressing technological and industrial needs while integrating with emerging technologies.

3.5.2. Strategic Analysis across the Three Distinct Periods

Strategic analysis through network mapping provides insights into the evolution of research themes by categorizing them into four distinct quadrants based on two key metrics: centrality and density [66,67]. These two-dimensional graphs represent thematic progression within the field, with each cluster signifying a research theme or a group of related themes [68]. The positioning of each cluster in the graph is determined by the following factors:
  • Relevance degree (centrality): Centrality measures the importance of a theme within the broader research network. Themes with high centrality are well connected to other themes, indicating their role in linking different areas of research.
  • Development degree (density): Density indicates the internal development of a theme. High-density themes are well-established and exhibit strong internal connections among keywords within their cluster.
Based on these metrics, the strategic diagram categorizes themes into four quadrants as follows [31]:
  • Motor themes (upper right quadrant): These themes are high in both centrality and density, meaning they are well developed and central to the research field. They represent the core focus of current research and are key drivers of progress in the field.
  • Niche themes (upper left quadrant): These themes have high density but low centrality. While they are well developed and specialized, they are less connected to the broader research field and are somewhat isolated from mainstream research.
  • Emerging or declining themes (lower left quadrant): These themes are low in both centrality and density. They represent either newly emerging areas of research or those that are losing relevance. Their future depends on how the field evolves.
  • Basic themes (lower right quadrant): These themes are high in centrality but low in density. While they are central to the research field, they are still underdeveloped and have the potential for future growth and deeper exploration.
For the strategic analysis, the review period of this study from 2014 to 2024 was divided into three sub-periods: foundational research (2014–2019), expansion and application (2020–2021), and advanced applications and challenges (2022–2024). Separate datasets were extracted for each period to generate distinct bibliometric strategic maps. The output from RStudio’s Biblioshiny provided a comprehensive examination of the dynamics in blockchain and consensus research over the past decade. This analysis allowed us to track how research themes have matured, declined, or newly emerged (Figure 14).

Analysis for the Foundational Research Period (2014–2019)

The period of foundational research in blockchain and consensus (2014–2019) was defined by key themes that shaped the direction of blockchain studies. In Figure 14a, the upper-right quadrant of the thematic map includes “consensus”, “agreement”, and “time”, representing the motor themes crucial for ensuring the efficiency and reliability of blockchain systems. Consensus mechanisms, in particular, are fundamental for maintaining security and agreement within decentralized networks. Wang et al. (2019) provided a comprehensive overview of various consensus mechanisms, emphasizing their role in improving the performance and scalability of blockchain networks [5]. Their work aligns with the identified motor themes of this period, underscoring the critical role of consensus in achieving reliable and secure blockchain operations.
In the upper-left quadrant, the “technology” cluster appears as a niche theme. While well-developed, it was less central to the broader blockchain research agenda during this period. Kizildag et al. (2019) discussed the potential applications of blockchain technology in various sectors, noting that although the technology was evolving, it remained somewhat peripheral to the core focus of blockchain research at the time [69].
The lower-left quadrant includes the “secure”, “Bitcoin”, and “model” clusters, representing emerging or declining themes. These themes, while relevant, had not yet achieved full development or centrality in the research field. Zaghloul et al. (2019) explored the security aspects of Bitcoin and blockchain, noting that while these topics were gaining attention, they were still in the early stages of exploration. This supports the idea that “Bitcoin” and “secure” were themes undergoing early investigation during this period [70].
The lower-right quadrant, featuring the “internet”, “security”, and “management” clusters, represents basic themes. Although central to blockchain research, these themes were still in their formative stages. The focus on security and management reflects their growing importance as blockchain began transitioning from theoretical exploration to practical application. Cao et al. (2019) discussed the challenges of distributed consensus in the context of the IoT, emphasizing the foundational nature of security and management themes within the broader blockchain landscape [21].
In summary, the foundational research period from 2014 to 2019 was marked by a strong focus on consensus mechanisms and security, alongside the recognition of emerging themes such as technology and management, which shaped the future direction of blockchain research.

Analysis of the Expansion and Application Period (2020–2021)

The expansion and application period (2020–2021) in blockchain consensus research was marked by a shift towards practical and applied research, with a focus on themes such as “systems”, “resource allocation”, and “research issues”, as shown in Figure 14b. These motor themes emphasize the growing need for efficient management and optimization of blockchain systems, particularly in large-scale deployments. Xiao et al. (2020) conducted a comprehensive survey of distributed consensus protocols for blockchain networks, emphasizing the role of consensus mechanisms in improving the efficiency of blockchain applications [59]. Their findings underscore the importance of addressing these issues as blockchain technology continues to expand across various industries.
In the upper-left quadrant, the niche theme of “fault tolerance” reflects ongoing efforts to enhance the reliability and performance of blockchain systems. Cai et al. (2021) explored the consensus mechanisms supporting blockchain technology, emphasizing their critical role in ensuring system robustness and security. While fault tolerance may not have been a central theme, it remains essential for maintaining the reliability of blockchain networks [71].
The lower-left quadrant reveals emerging or declining themes, particularly the increasing focus on “privacy”, reflecting broader concerns about data protection and confidentiality within blockchain systems. Zhou et al. (2020) addressed scalability challenges in blockchain, noting that privacy issues are becoming more prominent as the technology matures [72]. Meanwhile, themes like “Bitcoin” and “agreement” appear to have declined in relevance, suggesting that these once-central topics are becoming less central as blockchain research diversifies into other areas.
In the lower-right quadrant, basic themes such as “internet”, “consensus”, and “blockchain” remained foundational but were still developing in terms of depth and complexity. Aluko and Kolonin (2021) discussed the significance of consensus mechanisms in blockchain systems, emphasizing their foundational role in ensuring secure and reliable operations [73]. Their analysis indicated that while these themes remain central to the blockchain research agenda, they are still evolving as the technology integrates with other systems.
In summary, the expansion and application phase from 2020 to 2021 marked a significant transition in blockchain research, with a focus on practical applications, resource allocation, and fault tolerance. The emerging importance of privacy and the ongoing development of foundational themes like consensus further demonstrate the field’s evolving nature during this period.

Analysis of the Advanced Applications and Challenges Period (2022–2024)

The advanced applications and challenges period (2022–2024) in blockchain consensus research has been characterized by a strong focus on refining consensus mechanisms. Central to this phase are motor themes such as “agreement”, “broadcast”, and “Byzantine”, which appear in the upper-right quadrant of Figure 14c. These themes reflect ongoing efforts to optimize BFT and related protocols, crucial for ensuring the robustness and security of decentralized blockchain networks. Hossain et al. (2024) provided a comprehensive analysis of blockchain technology and consensus protocols, emphasizing the importance of consensus algorithms in enhancing security, scalability and efficiency in decentralized systems [74].
Although the strategic analysis did not explicitly feature “AI” as a standalone theme, the earlier temporal shift in keyword clustering (Section 3.4.2.) highlighted AI’s growing integration with blockchain, particularly in multi-disciplinary applications such as energy trading and smart grids. AI has converged with blockchain research to tackle scalability and efficiency challenges. Kumar et al. (2022) conducted a bibliometric-content analysis identifying trends in AI–blockchain integration, particularly in healthcare and IoT systems. Their findings align with the growing recognition that AI is becoming foundational to enhancing decentralized decision making across various industries [36]. Similarly, Tsolakis et al. (2022) proposed a framework for AI–blockchain synergy in supply chains, emphasizing sustainability and data monetization. These developments reflect the practical implications of integrating AI with blockchain and further support the notion of AI’s rising influence in these applications [75]. In this context, motor themes such as “agreement” and “Byzantine” can be seen as foundational for optimizing consensus mechanisms that AI is likely to enhance, particularly for improving decentralized decision making and efficiency.
In the upper-left quadrant, niche themes such as “complexity” and advanced theoretical concepts in consensus algorithms point to a growing interest in the complexities of distributed systems. Pillai et al. (2022) explored cross-blockchain integration, underscoring the theoretical challenges of achieving consensus across decentralized networks. This aligns with the niche themes identified in the strategic analysis, emphasizing the need for continued theoretical advancements [76].
The lower-left quadrant represents emerging or declining themes, including “network”, “architecture”, and “protocol”. These topics, while gaining attention, have yet to become central in the blockchain research landscape. However, AI-driven innovations, as noted in the keyword clustering analysis, could accelerate the maturation of these themes. AI’s role in addressing scalability and interoperability challenges may enable more efficient network architectures and system management, particularly in complex applications like smart grids [77].
Conversely, the lower-right quadrant encompasses basic and foundational themes that remain central to blockchain research but are still evolving. Themes such as “consensus”, “blockchain”, “management”, “knowledge”, and “technology” reflect the diversity of ongoing research in the field. Li and Chen (2022) explored blockchain’s role in empowering supply chains, identifying both opportunities and challenges. Their findings underscore the continued relevance of these core topics, indicating that although they are well established, they are still evolving alongside new innovations and developments [78]. The broader range of themes in this quadrant, compared with earlier periods, suggests the increasing complexity and multidimensionality of contemporary blockchain consensus research, particularly as it converges with AI. This reflects the technology’s growing maturity and its diversification into emerging applications [55,79,80].
In summary, the period of advanced applications and challenges from 2022 to 2024 highlights the complexity and multifaceted nature of blockchain research, particularly in relation to refining consensus mechanisms and integrating AI. While the strategic analysis did not explicitly feature AI as a key theme, its growing significance in the keyword clustering suggests that AI is becoming a crucial component of the broader blockchain ecosystem. This period reflects ongoing efforts to address scalability, security, and governance issues while expanding into more sophisticated applications, including those powered by AI.

4. Conclusions

This bibliometric analysis offers a comprehensive review of blockchain consensus mechanism research from 2014 to 2024, identifying key trends, shifts in focus, and major contributions across different phases of development. Through the examination of publications, authors, institutions, and thematic clusters, this study reveals critical milestones in the evolution of blockchain technology, particularly in its consensus mechanisms. By leveraging co-authorship, co-occurrence, and strategic mapping analyses, we identified distinct phases in the research landscape: foundational research (2014–2019), expansion and application (2020–2021), and advanced applications and challenges (2022–2024).
During the foundational research phase, consensus mechanisms, particularly Bitcoin’s PoW, were the central focus, addressing the fundamental issues of security, trust, and decentralization in blockchain networks. Early exploration into alternative consensus mechanisms, such as PoS, and the conceptual development of smart contracts laid the groundwork for future blockchain innovations, setting the stage for the next phase.
In the expansion and application phase, research shifted towards practical applications, especially in integrating blockchain with the IoT and decentralized systems. Research during this period focused on improving scalability and addressing energy efficiency concerns, most notably through advancements in PoS and DPoS. This phase demonstrated blockchain’s potential beyond cryptocurrencies, with applications extending into supply chain management, privacy solutions, and dApps.
The advanced applications and challenges phase represents a determining moment in the evolution of blockchain consensus mechanisms, driven by Ethereum’s historic transition from PoW to PoS during “The Merge” in 2022. This milestone has ignited extensive research into the implications of PoS for enhancing security, sustainability, and scalability, as blockchain systems strive to meet the demands of diverse, real-world applications. The shift from PoW to PoS addresses critical concerns regarding energy efficiency and adaptability, positioning blockchain as a more sustainable technology capable of supporting industries such as finance, supply chain management, and health care. Moreover, this phase has witnessed the increasing convergence of blockchain with advanced technologies like AI, federated learning, and edge computing. The integration of AI, as highlighted in the keyword clustering analysis, underscores its transformative potential in optimizing blockchain processes, enhancing decision making, and expanding applications in areas like energy trading and smart grids. This growing multidisciplinary approach introduces new challenges related to governance, scalability, and privacy, which remain focal points for future research. To sum up, the advanced applications and challenges phase reflects the maturity and complexity of blockchain technology. The shift towards PoS, coupled with the growing integration of AI and other emerging technologies, signals blockchain’s expanding influence in reshaping industries while addressing critical challenges related to efficiency, security, and scalability.
Despite its comprehensive scope, this study has some limitations. First, the bibliometric analysis relied solely on data from the WoS. While WoS is extensive, it may not capture all relevant research outputs, particularly from non-English sources, underrepresented regions, or studies indexed in other major databases such as Scopus or arXiv. Including Scopus or other databases in future analyses could provide a broader and potentially more diverse representation of blockchain research, offering further insights into emerging trends and global contributions. Additionally, this study primarily focused on blockchain and its consensus mechanisms, potentially overlooking alternative technologies such as directed acyclic graphs (DAGs) or hybrid consensus models that could significantly influence the future development of consensus-based systems. Exploring these alternative technologies in future work will be crucial for understanding their potential to complement or replace current blockchain systems. Furthermore, this study’s focus on historical and current trends may not fully reflect rapidly emerging areas such as quantum-resistant blockchain protocols or privacy-enhancing technologies. Another limitation is the uncertain nature of the relationship between AI and blockchain—whether their integration is causal or merely correlated remains unclear, necessitating further research into how AI directly influences blockchain performance and vice versa.
Future research could address these limitations by incorporating data from additional databases, such as Scopus, arXiv, and Google Scholar, to provide a more comprehensive and global perspective on blockchain research [81,82,83]. Interdisciplinary collaborations between blockchain, AI, and quantum computing could also offer new insights into the next phase of blockchain innovation, particularly in enhancing scalability, security, and privacy [84,85,86]. A deeper exploration of emerging technologies, such as quantum-resistant blockchain protocols, privacy-enhancing mechanisms, and alternative consensus systems like DAGs, is crucial for addressing future security challenges and advancing the field of consensus mechanisms [73,87,88]. Additionally, further investigation is needed to clarify the causal relationship between AI integration and blockchain performance, an area that remains largely underexplored. Moreover, further research on the socio-economic impact of blockchain technology, especially in developing regions, is also necessary, as it holds significant potential for fostering financial inclusion and economic development [89]. Understanding how consensus mechanisms can be optimized for such regions and how alternative technologies can play a role in improving accessibility will be vital to ensuring blockchain’s global success and applicability.
In conclusion, this study underscores the dynamic evolution of blockchain consensus mechanisms over the past decade. The findings shed light on their growing importance in ensuring the security, efficiency, and scalability of decentralized networks. As blockchain technology continues to mature, its transformative role in reshaping industries, safeguarding data, and facilitating global transactions will become even more significant. The insights presented here provide a solid foundation for future research and development as blockchain continues to evolve to meet the demands of an increasingly interconnected and decentralized world.

Author Contributions

Conceptualization, J.A. and M.K.; methodology, J.A. and E.Y.; software, J.A.; validation, J.A., E.Y. and M.K.; formal analysis, J.A.; investigation, J.A.; resources, J.A.; data curation, J.A.; writing—original draft preparation, J.A.; writing—review and editing, E.Y. and M.K.; visualization, J.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data source of this study was the Web of Science Core Collection.

Conflicts of Interest

All authors declare that they have no conflicts of interest.

References

  1. Nakamoto, S. Bitcoin: A Peer-to-Peer Electronic Cash System. 2009. Available online: www.bitcoin.org (accessed on 4 July 2024).
  2. Buterin, V. A Next Generation Smart Contract & Decentralized Application Platform. Ethereum White Pap. 2014, 3, 1–36. [Google Scholar]
  3. Ahn, J.; Yi, E.; Kim, M. Ethereum 2.0 Hard Fork: Consensus Change and Market Efficiency. J. Br. Blockchain Assoc. 2024, 7, 1–14. [Google Scholar] [CrossRef] [PubMed]
  4. Zhou, S.; Li, K.; Xiao, L.; Cai, J.; Liang, W.; Castiglione, A. A Systematic Review of Consensus Mechanisms in Blockchain. Mathematics 2023, 11, 2248. [Google Scholar] [CrossRef]
  5. Wang, W.; Hoang, D.T.; Hu, P.; Ziong, Z. A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks. IEEE Access 2019, 7, 22328–22370. [Google Scholar] [CrossRef]
  6. Zheng, X.; Zhu, Y.; Si, X. A Survey on Challenges and Progresses in Blockchain Technologies: A Performance and Security Perspective. Appl. Sci. 2019, 9, 4731. [Google Scholar] [CrossRef]
  7. Zhao, C.; Han, D.; Li, C. A Blockchain Consensus Mechanism to Optimize Reputation-Based Distributed Energy Trading in Urban Energy System. IEEE Access 2024, 12, 53698–53712. [Google Scholar] [CrossRef]
  8. Rejeb, A.; Rejeb, K.; Simske, S.; Keogh, J.G. Exploring Blockchain Research in Supply Chain Management: A Latent Dirichlet Allocation-Driven Systematic Review. Information 2023, 14, 557. [Google Scholar] [CrossRef]
  9. Oriekhoe, O.I.; Omotoye, G.B.; Oyeyemi, O.P.; Tula, S.T.; Daraojimba, A.I.; Adefemi, A. Blockchain in Supply Chain Management: A Systematic Review: Evaluating the Implementation, Challenges, and Future Prospects of Blockchain Technology in Supply Chains. Eng. Sci. Technol. J. 2024, 5, 128–151. [Google Scholar] [CrossRef]
  10. Kushwaha, S.S.; Joshi, S.; Singh, D.; Kaur, M.; Lee, H.-N. Systematic Review of Security Vulnerabilities in Ethereum Blockchain Smart Contract. IEEE Access 2022, 10, 6605–6621. [Google Scholar] [CrossRef]
  11. Kushwaha, S.S.; Joshi, S.; Singh, D.; Kaur, M.; Lee, H.-N. Ethereum Smart Contract Analysis Tools: A Systematic Review. IEEE Access 2022, 10, 57037–57062. [Google Scholar] [CrossRef]
  12. Kademeteme, E.; Bvuma, S. Using Blockchain Technology to Improve the Integrity and Transparency of Procurement Processes between SMMEs and Government: A Systematic Literature Review. J. Br. Blockchain Assoc. 2023, 7, 1–12. [Google Scholar] [CrossRef] [PubMed]
  13. Corbet, S.; Lucey, B.; Urquhart, A.; Yarovaya, L. Cryptocurrencies as a Financial Asset: A Systematic Analysis. Int. Rev. Financ. Anal. 2019, 62, 182–199. [Google Scholar] [CrossRef]
  14. Dabbagh, M.; Sookhak, M.; Safa, N.S. The Evolution of Blockchain: A Bibliometric Study. IEEE Access 2019, 7, 19212–19221. [Google Scholar] [CrossRef]
  15. Kamran, M.; Khan, H.U.; Nisar, W.; Farooq, M.; Rehman, S.-U. Blockchain and Internet of Things: A Bibliometric Study. Comput. Electr. Eng. 2020, 81, 106525. [Google Scholar] [CrossRef]
  16. Ante, L.; Steinmetz, F.; Fiedler, I. Blockchain and Energy: A Bibliometric Analysis and Review. Renew. Sustain. Energy Rev. 2021, 137, 110597. [Google Scholar] [CrossRef]
  17. Pandey, V.; Pant, M.; Snasel, V. Blockchain Technology in Food Supply Chains: Review and Bibliometric Analysis. Technol. Soc. 2022, 69, 101954. [Google Scholar] [CrossRef]
  18. Kuzior, A.; Sira, M. A Bibliometric Analysis of Blockchain Technology Research Using VOSviewer. Sustainability 2022, 14, 8206. [Google Scholar] [CrossRef]
  19. Salim, M.; Khan, P.A.; Jalil, N.A.; Johl, S.K.; Jafar, S.H.; Irfan, F.; Khan, M. Fusion of Blockchain and Sustainable Development Goals: A Bibliometric Analysis for Future Trends. KnE Soc. Sci. 2023, 8, 633–654. [Google Scholar] [CrossRef]
  20. Zou, Y.; Meng, T.; Zhang, P.; Zhang, W.; Li, H. Focus on Blockchain: A Comprehensive Survey on Academic and Application. IEEE Access 2020, 8, 187182–187201. [Google Scholar] [CrossRef]
  21. Cao, B.; Li, Y.; Zhang, L.; Zhang, L.; Mumtaz, S.; Zhou, Z.; Peng, M. When Internet of Things Meets Blockchain: Challenges in Distributed Consensus. IEEE Netw. 2019, 33, 133–139. [Google Scholar] [CrossRef]
  22. Chandan, A.; Potdar, V.; John, M. Systematic Literature Review of Blockchain Technology’s Technical Challenges: A Tertiary Study. Information 2024, 15, 475. [Google Scholar] [CrossRef]
  23. Judijanto, L.; Gamaliel, F. Analyzing the Impact of Blockchain Technology on Transaction Security with a Bibliometric Perspective. Eastasouth J. Inf. Syst. Comput. Sci. 2024, 1, 136–146. [Google Scholar] [CrossRef]
  24. Asif, R.; Hassan, S.R. Shaping the Future of Ethereum: Exploring Energy Consumption in Proof-of-Work and Proof-of-Stake Consensus. Front. Blockchain 2023, 6, 1151724. [Google Scholar] [CrossRef]
  25. Mišić, V.B.; Mighan, S.N.; Mišić, J.; Chang, X. Decentralization Is Good or Not? Defending Consensus in Ethereum 2.0. Blockchains 2024, 2, 1–19. [Google Scholar] [CrossRef]
  26. Birkle, C.; Pendlebury, D.A.; Schnell, J.; Adams, J. Web of Science as a Data Source for Research on Scientific and Scholarly Activity. Quant. Sci. Stud. 2020, 1, 363–376. [Google Scholar] [CrossRef]
  27. Fan, L.; Li, L.; Ma, Z.; Lee, S.; Yu, H.; Hemphill, L. A Bibliometric Review of Large Language Models Research from 2017 to 2023. arXiv 2023, arXiv:2304.02020. [Google Scholar] [CrossRef]
  28. Van Eck, N.; Waltman, L. Software Survey: VOSviewer, a Computer Program for Bibliometric Mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef]
  29. Aria, M.; Cuccurullo, C. Bibliometrix: An R-Tool for Comprehensive Science Mapping Analysis. J. Informetr. 2017, 11, 959–975. [Google Scholar] [CrossRef]
  30. Otto, E.; Culakova, E.; Meng, S.; Zhang, Z.; Xu, H.; Mohile, S.; Flannery, M.A. Overview of Sankey Flow Diagrams: Focusing on Symptom Trajectories in Older Adults with Advanced Cancer. J. Geriatr. Oncol. 2022, 13, 742–746. [Google Scholar] [CrossRef]
  31. Melcer, E.; Nguyen, T.-H.D.; Chen, Z.; Canossa, A.; El-Nasr, M.S.; Isbister, K. Games Research Today: Analyzing the Academic Landscape 2000–2014. Network 2015, 17, 20. [Google Scholar]
  32. Guleria, D.; Kaur, G. Bibliometric Analysis of Ecopreneurship Using VOSviewer and RStudio Bibliometrix, 1989–2019. Libr. Hi Tech 2021, 39, 1001–1024. [Google Scholar] [CrossRef]
  33. van Eck, N.J.; Waltman, L. Citation-Based Clustering of Publications Using CitNetExplorer and VOSviewer. Scientometrics 2017, 111, 1053–1070. [Google Scholar] [CrossRef] [PubMed]
  34. Qu, F.; Wang, G.; Wen, P.; Liu, X.; Zeng, X. Knowledge Mapping of Immunotherapy for Breast Cancer: A Bibliometric Analysis from 2013 to 2022. Hum. Vaccines Immunother. 2024, 20, 2335728. [Google Scholar] [CrossRef] [PubMed]
  35. Gorenflo, C.; Lee, S.; Golab, L.; Keshav, S. FastFabric: Scaling Hyperledger Fabric to 20,000 Transactions per Second. Int. J. Netw. Manag. 2020, 30, 455–463. [Google Scholar] [CrossRef]
  36. Kumar, S.; Lim, W.M.; Sivarajah, U.; Kaur, J. Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis. Inf. Syst. Front. 2023, 25, 871–896. [Google Scholar] [CrossRef]
  37. Tiwari, A.; Batra, U. Blockchain Enabled Reparations in Smart Buildings Cyber Physical System. Def. Sci. J. 2021, 71, 491–498. [Google Scholar] [CrossRef]
  38. Zulkepli, M.I.S.; Mohamad, M.T.; Azzuhri, S.R. Leveraging Blockchain-Based Smart Contract in Islamic Financial Institutions: Issue and Relevant Solution. Int. J. Islam. Econ. Financ. Res. 2023, 6, 18–28. [Google Scholar] [CrossRef]
  39. Sousa, J.; Bessani, A.; Vukolić, M. A Byzantine Fault-Tolerant Ordering Service for the Hyperledger Fabric Blockchain Platform. In Proceedings of the 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Luxembourg, 25–28 June 2018; pp. 51–58. [Google Scholar] [CrossRef]
  40. Thakkar, P.; Nathan, S. Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform. In Proceedings of the 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Milwaukee, WI, USA, 25–28 September 2018. [Google Scholar] [CrossRef]
  41. Kiayias, A.; Russell, A.; David, B.; Oliynykov, R. Ouroboros: A Provably Secure Proof-of-Stake Blockchain Protocol. Adv. Cryptol.–CRYPTO 2017, 2017, 357–388. [Google Scholar] [CrossRef]
  42. Vukolić, M. Rethinking Permissioned Blockchains. In Proceedings of the ASIA CCS ‘17: ACM Asia Conference on Computer and Communications Security, Abu Dhabi, United Arab Emirates, 2 April 2017; pp. 3–7. [Google Scholar] [CrossRef]
  43. Mišić, J.; Mišić, V.B.; Chang, X. Delegated Proof of Stake Consensus with Mobile Voters and Multiple Entry PBFT Voting. In Proceedings of the GLOBECOM 2022–2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 4–8 December 2022; pp. 6253–6258. [Google Scholar] [CrossRef]
  44. Hassan, A.; Ali, M.I.; Ahammed, R.; Khan, M.M.; Alsufyani, N.; Alsufyani, A. Secured Insurance Framework Using Blockchain and Smart Contract. Sci. Program. 2021, 2021, 6787406. [Google Scholar] [CrossRef]
  45. Gatteschi, V.; Lamberti, F.; Demartini, C.; Pranteda, C.; Santamaría, V. Blockchain and Smart Contracts for Insurance: Is the Technology Mature Enough? Future Internet 2018, 10, 20. [Google Scholar] [CrossRef]
  46. Zeadally, S.; Abdo, J.B. Blockchain: Trends and Future Opportunities. Internet Technol. Lett. 2019, 2, e130. [Google Scholar] [CrossRef]
  47. Akar, S.; Akar, E. Is It a New Tulip Mania Age?: A Comprehensive Literature Review Beyond Cryptocurrencies, Bitcoin, and Blockchain Technology. J. Inf. Technol. Res. (JITR) 2020, 13, 44–67. [Google Scholar] [CrossRef]
  48. Luo, J.; Hu, Y.; Bai, Y. Bibliometric Analysis of the Blockchain Scientific Evolution: 2014–2020. IEEE Access 2021, 9, 120227–120246. [Google Scholar] [CrossRef]
  49. Dziatkovskii, A.; Hryneuski, U.; Krylova, A.; Loy, A.C.M. Chronological Progress of Blockchain in Science, Technology, Engineering and Math (STEM): A Systematic Analysis for Emerging Future Directions. Sustainability 2022, 14, 12074. [Google Scholar] [CrossRef]
  50. Ali, M.S.; Vecchio, M.; Pincheira, M.; Dolui, K.; Antonelli, F.; Rehmani, M.H. Applications of Blockchains in the Internet of Things: A Comprehensive Survey. IEEE Commun. Surv. Tutor. 2019, 21, 1676–1717. [Google Scholar] [CrossRef]
  51. Cai, W.; Wang, Z.; Ernst, J.B.; Hong, Z.; Feng, C.; Leung, V.C.M. Decentralized Applications: The Blockchain-Empowered Software System. IEEE Access 2018, 6, 53019–53033. [Google Scholar] [CrossRef]
  52. Li, Q.; Zhou, W.; Zheng, X. Distributed Learning in Intelligent Transportation Systems: A Survey. Information 2024, 15, 550. [Google Scholar] [CrossRef]
  53. Bhutta, M.N.M.; Khwaja, A.A.; Nadeem, A.; Ahmad, H.F.; Khan, M.K.; Hanif, M.A.; Song, H. A Survey on Blockchain Technology: Evolution, Architecture and Security. IEEE Access 2021, 9, 61048–61073. [Google Scholar] [CrossRef]
  54. Bodkhe, U.; Tanwar, S.; Parekh, K.; Khanpara, P.; Tyagi, S.; Kumar, N.; Alazab, M. Blockchain for Industry 4.0: A Comprehensive Review. IEEE Access 2020, 8, 79764–79800. [Google Scholar] [CrossRef]
  55. Bhumichai, D.; Smiliotopoulos, C.; Benton, R.; Kambourakis, G.; Damopoulos, D. The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead. Information 2024, 15, 268. [Google Scholar] [CrossRef]
  56. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F. An Approach for Detecting, Quantifying, and Visualizing the Evolution of a Research Field: A Practical Application to the Fuzzy Sets Theory Field. J. Informetr. 2011, 5, 146–166. [Google Scholar] [CrossRef]
  57. Bano, S.; Sonnino, A.; Al-Bassam, M.; Azouvi, S.; McCorry, P.; Meiklejohn, S.; Danezis, G. Consensus in the Age of Blockchains. arXiv 2017, arXiv:1711.03936. [Google Scholar] [CrossRef]
  58. Xie, M.; Liu, J.; Chen, S.; Lin, M. A Survey on Blockchain Consensus Mechanism: Research Overview, Current Advances and Future Directions. Int. J. Intell. Comput. Cybern. 2023, 16, 314–340. [Google Scholar] [CrossRef]
  59. Xiao, Y.; Zhang, N.; Lou, W.; Hou, Y.T. A Survey of Distributed Consensus Protocols for Blockchain Networks. IEEE Commun. Surv. Tutor. 2020, 22, 1432–1465. [Google Scholar] [CrossRef]
  60. Liang, Y.; Lu, C.; Zhao, Y.; Sun, C. Interference-Based Consensus and Transaction Validation Mechanisms for Blockchain-Based Spectrum Management. IEEE Access 2021, 9, 90757–90766. [Google Scholar] [CrossRef]
  61. Nguyen, C.T.; Hoang, D.T.; Nguyen, D.N.; Niyato, D.; Nguyen, H.T. Dutkiewicz Proof-of-Stake Consensus Mechanisms for Future Blockchain Networks: Fundamentals, Applications and Opportunities. IEEE Access 2019, 7, 85727–85745. [Google Scholar] [CrossRef]
  62. Munir, M.A.; Habib, M.S.; Hussain, A.; Shahbaz, M.A.; Qamar, A.; Masood, T.; Sultan, M.; Mujtaba, M.A.; Imran, S.; Hasan, M.; et al. Blockchain Adoption for Sustainable Supply Chain Management: Economic, Environmental, and Social Perspectives. Front. Energy Res. 2022, 10, 899632. [Google Scholar] [CrossRef]
  63. Amanat, A.; Rizwan, M.; Maple, C.; Zikria, Y.B.; Almadhor, A.S.; Kim, S.W. Blockchain and Cloud Computing-Based Secure Electronic Healthcare Records Storage and Sharing. Front. Public Health 2022, 10, 938707. [Google Scholar] [CrossRef]
  64. Manzoor, R.; Sahay, B.S.; Singh, S.K. Blockchain Technology in Supply Chain Management: An Organizational Theoretic Overview and Research Agenda. In Annals of Operations Research; Springer: Berlin/Heidelberg, Germany, 2022; pp. 1–48. [Google Scholar] [CrossRef]
  65. Yang, F.; Zhou, W.; Wu, Q.; Long, R.; Xiong, N.N.; Zhou, M. Delegated Proof of Stake with Downgrade: A Secure and Efficient Blockchain Consensus Algorithm with Downgrade Mechanism. IEEE Access 2019, 7, 118541–118555. [Google Scholar] [CrossRef]
  66. Callon, M.; Courtial, J.P.; Laville, F. Co-Word Analysis as a Tool for Describing the Network of Interactions between Basic and Technological Research: The Case of Polymer Chemsitry. Scientometrics 1991, 22, 155–205. [Google Scholar] [CrossRef]
  67. Gilsing, V.; Nooteboom, B.; Vanhaverbeke, W.; Duysters, G.; van den Oord, A. Network Embeddedness and the Exploration of Novel Technologies: Technological Distance, Betweenness Centrality and Density. Res. Policy 2008, 37, 1717–1731. [Google Scholar] [CrossRef]
  68. Muñoz-Leiva, F.; Viedma-del-Jesús, M.I.; Sánchez-Fernández, J.; López-Herrera, A.G. An Application of Co-Word Analysis and Bibliometric Maps for Detecting the Most Highlighting Themes in the Consumer Behaviour Research from a Longitudinal Perspective. Qual. Quant. 2012, 46, 1077–1095. [Google Scholar] [CrossRef]
  69. Kizildag, M.; Dogru, T.; Zhang, T.; Mody, M.A.; Altin, M.; Ozturk, A.B.; Ozdemir, O. Blockchain: A Paradigm Shift in Business Practices. Int. J. Contemp. Hosp. Manag. 2019, 32, 953–975. [Google Scholar] [CrossRef]
  70. Zaghloul, E.; Li, T.; Mutka, M.; Ren, J. Bitcoin and Blockchain: Security and Privacy. arXiv 2019, arXiv:1904.11435v1. [Google Scholar] [CrossRef]
  71. Cai, S.; Han, T.; Wang, Y.; Zhang, H. Study of Blockchain’s Consensus Mechanism Based on Score. IET Blockchain 2021, 1, 41–55. [Google Scholar] [CrossRef]
  72. Zhou, Q.; Huang, H.; Zheng, Z.; Bian, J. Solutions to Scalability of Blockchain: A Survey. IEEE Access 2020, 8, 16440–16455. [Google Scholar] [CrossRef]
  73. Aluko, O.; Kolonin, A. Proof-of-Reputation: An Alternative Consensus Mechanism for Blockchain Systems. arXiv 2021, arXiv:2108.03542. [Google Scholar] [CrossRef]
  74. Hossain, M.R.; Nirob, F.A.; Islam, A.; Rakin, T.M.; Al-Amin, M. A Comprehensive Analysis of Blockchain Technology and Consensus Protocols Across Multilayered Framework. IEEE Access 2024, 12, 63087–63129. [Google Scholar] [CrossRef]
  75. Tsolakis, N.; Schumacher, R.; Dora, M.; Kumar, M. Artificial Intelligence and Blockchain Implementation in Supply Chains: A Pathway to Sustainability and Data Monetisation? Ann. Oper. Res. 2023, 324, 157–210. [Google Scholar] [CrossRef]
  76. Pillai, B.; Biswas, K.; Hóu, Z. Muthukkumarasamy Cross-Blockchain Technology: Integration Framework and Security Assumptions. IEEE Access 2022, 10, 41239–41259. [Google Scholar] [CrossRef]
  77. Koukaras, P.; Afentoulis, K.D.; Gkaidatzis, P.A.; Mystakidis, A.; Ioannidis, D.; Vagropoulos, S.I.; Tjortjis, C. Integrating Blockchain in Smart Grids for Enhanced Demand Response: Challenges, Strategies, and Future Directions. Energies 2024, 17, 1007. [Google Scholar] [CrossRef]
  78. Li, Y.; Chen, T. Blockchain Empowers Supply Chains: Challenges, Opportunities and Prospects. Nankai Bus. Rev. Int. 2023, 14, 230–248. [Google Scholar] [CrossRef]
  79. Wu, T.-C.; Ho, C.-T.B. Blockchain Revolutionizing in Emergency Medicine: A Scoping Review of Patient Journey through the ED. Healthcare 2023, 11, 2497. [Google Scholar] [CrossRef] [PubMed]
  80. Kumar, S.; Kar, A.K.; Ilavarasan, P.V. Applications of Text Mining in Services Management: A Systematic Literature Review. Int. J. Inf. Manag. Data Insights 2021, 1, 100008. [Google Scholar] [CrossRef]
  81. Shah, U.; Alzubaidi, M.; Mohsen, F.; Abd-Alrazaq, A.; Alam, T.; Househ, M. The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review. Sensors 2022, 22, 6975. [Google Scholar] [CrossRef]
  82. Anker, M.S.; Hadzibegovic, S.; Lena, A.; Haverkamp, W. The Difference in Referencing in Web of Science, Scopus, and Google Scholar. ESC Heart Fail. 2019, 6, 1291–1312. [Google Scholar] [CrossRef]
  83. Martín-Martín, A.; Orduna-Malea, E.; López-Cózar, E.D. Coverage of Highly-Cited Documents in Google Scholar, Web of Science, and Scopus: A Multidisciplinary Comparison. Scientometrics 2018, 116, 2175–2188. [Google Scholar] [CrossRef]
  84. Dai, W. Quantum-Computing with AI & Blockchain: Modelling, Fault Tolerance and Capacity Scheduling. Math. Comput. Model. Dyn. Syst. 2019, 25, 523–559. [Google Scholar] [CrossRef]
  85. Sgantzos, K.; Grigg, I. Artificial Intelligence Implementations on the Blockchain. Use Cases and Future Applications. Future Internet 2019, 11, 170. [Google Scholar] [CrossRef]
  86. Kaushik, K.; Kumar, A. Demystifying Quantum Blockchain for Healthcare. Secur. Priv. 2023, 6, e284. [Google Scholar] [CrossRef]
  87. Kahmann, F.; Honecker, F.; Dreyer, J.; Fischer, M.; Tönjes, R. Performance Comparison of Directed Acyclic Graph-Based Distributed Ledgers and Blockchain Platforms. Computers 2023, 12, 257. [Google Scholar] [CrossRef]
  88. Arslan, C.; Sipahioğlu, S.; Şafak, E.; Gözütok, M.; Köprülü, T. Comparative Analysis and Modern Applications of PoW, PoS, PPoS Blockchain Consensus Mechanisms and New Distributed Ledger Technologies. Adv. Sci. Technol. Eng. Syst. J. 2021, 6, 279–290. [Google Scholar] [CrossRef]
  89. Karapetyan, M.E.; Timoshenko, L.P.; Stroganov, I.A.; Pronina, I.V. The Development of Blockchain Technology in Russia: Outlook and Trends. Int. J. Econ. Bus. Adm. 2019, 7, 279–289. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Analytical framework and workflow of the study of blockchain consensus mechanisms.
Figure 1. Analytical framework and workflow of the study of blockchain consensus mechanisms.
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Figure 2. Annual scientific productions per year (the number of publications in 2024 is based on data collected up to 2 August 2024).
Figure 2. Annual scientific productions per year (the number of publications in 2024 is based on data collected up to 2 August 2024).
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Figure 3. Countries’ production over time.
Figure 3. Countries’ production over time.
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Figure 4. Affiliated production over time.
Figure 4. Affiliated production over time.
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Figure 5. Most relevant affiliations.
Figure 5. Most relevant affiliations.
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Figure 6. Most relevant authors.
Figure 6. Most relevant authors.
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Figure 7. Word cloud for most frequent keywords in blockchain and consensus research.
Figure 7. Word cloud for most frequent keywords in blockchain and consensus research.
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Figure 8. Bibliometric analysis of the citations: (a) Citations of authors. Eight clusters are shown in different colors. Marko Vukolić in the blue cluster was the most cited author (2455 citations); (b) Citations of organizations. IBM Corporation in yellow cluster was the most cited organization (2377 citations); (c) Citations of countries. The USA, represented by the green cluster, was the most cited country (9198 citations).
Figure 8. Bibliometric analysis of the citations: (a) Citations of authors. Eight clusters are shown in different colors. Marko Vukolić in the blue cluster was the most cited author (2455 citations); (b) Citations of organizations. IBM Corporation in yellow cluster was the most cited organization (2377 citations); (c) Citations of countries. The USA, represented by the green cluster, was the most cited country (9198 citations).
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Figure 9. Bibliometric analysis of co-authorship: (a) The co-authorship map of authors, indicating the authors that have cooperated in the field of blockchain and consensus; (b) The co-authorship map of organizations. The Chinese Academy of Sciences produced 30 related papers and collaborated with 13 other institutions.
Figure 9. Bibliometric analysis of co-authorship: (a) The co-authorship map of authors, indicating the authors that have cooperated in the field of blockchain and consensus; (b) The co-authorship map of organizations. The Chinese Academy of Sciences produced 30 related papers and collaborated with 13 other institutions.
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Figure 10. Co-authorship map of countries in blockchain and consensus research: (a) Network view shows clusters and collaboration links between countries, with node size representing the number of documents; (b) Overlay view displays changes over time, with circle size indicating document count and color gradient (purple to yellow) showing the average publication year from 2014 to 2024.
Figure 10. Co-authorship map of countries in blockchain and consensus research: (a) Network view shows clusters and collaboration links between countries, with node size representing the number of documents; (b) Overlay view displays changes over time, with circle size indicating document count and color gradient (purple to yellow) showing the average publication year from 2014 to 2024.
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Figure 11. Co-occurrence analysis of keywords within the blockchain research field: (a) Network cluster visualization displays the relationships and clustering of keywords based on their co-occurrence in the literature, emphasizing the main research themes and their interconnections. (b) Overlay visualization illustrates the temporal progression of research topics, with colors representing different periods of focus.
Figure 11. Co-occurrence analysis of keywords within the blockchain research field: (a) Network cluster visualization displays the relationships and clustering of keywords based on their co-occurrence in the literature, emphasizing the main research themes and their interconnections. (b) Overlay visualization illustrates the temporal progression of research topics, with colors representing different periods of focus.
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Figure 12. Thematic evolution of blockchain and consensus research: a three-period Sankey diagram.
Figure 12. Thematic evolution of blockchain and consensus research: a three-period Sankey diagram.
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Figure 13. Number of articles analyzed per period (the number of publications in 2024 is based on data collected up to 2 August 2024).
Figure 13. Number of articles analyzed per period (the number of publications in 2024 is based on data collected up to 2 August 2024).
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Figure 14. Strategic thematic map for blockchain and consensus research across three periods: (a) 2014–2019; (b) 2020–2021; (c) 2022–2024.
Figure 14. Strategic thematic map for blockchain and consensus research across three periods: (a) 2014–2019; (b) 2020–2021; (c) 2022–2024.
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Table 1. Types of popular consensus mechanism (https://crypto.com/university/consensus-mechanisms-explained and https://www.shiksha.com/online-courses/articles/consensus-mechanisms-in-blockchain, accessed on 6 September 2024).
Types of
Consensus Mechanism		Description
Proof of Work
(PoW)		Miners solve complex mathematical problems to validate and add blocks to the blockchain. The first to solve the problem is rewarded. This mechanism is used in Bitcoin and Ethereum (before Ethereum 2.0).
Proof of Stake
(PoS)		Validators stake cryptocurrency as collateral, with the chance to create new blocks based on the amount staked. It is more energy-efficient than PoW. Commonly used in Ethereum 2.0, Cardano, and Polkadot.
Delegated Proof of Stake
(DPoS)		Similar to PoS, but with selected delegates, voted by stakeholders, who are responsible for validating transactions and creating blocks. This mechanism improves scalability and transaction speed. It is used in EOS, Tron, and Lisk.
Proof of Importance
(PoI)		Block creation ability is determined by transaction quality and user reputation, helping to prevent centralization. This consensus method is used in NEM.
Proof of Capacity
(PoC)		Mining is based on the storage capacity of the miner’s hardware. Miners plot nonces and block hashes in advance. This method is used in Burstcoin, Chia, and Storj.
Proof of Elapsed Time
(PoET)		Miners are assigned random waiting times, and the first to “wake up” creates the next block. This mechanism is used in Hyperledger Sawtooth.
Proof of Activity
(PoA)		A hybrid mechanism that combines PoW and PoS. Miners use PoW to create empty blocks, while the holder with the most cryptocurrency uses PoS to fill these blocks with transactions.
Proof of Authority
(PoA)		Used in private or permissioned blockchains. It relies on the reputation of participants to validate transactions. Commonly used in VeChain.
Proof of Burn
(PoB)		The miners “burn” a portion of their cryptocurrency by sending it to an unspendable address. The higher the burn amount, the greater the chances of creating new blocks. This method is used in Slimcoin.
Byzantine Fault Tolerance
(BFT)		This mechanism ensures consensus even when some nodes in the network are faulty or malicious. It relies on cryptography to regulate communication between nodes. It is used in Hyperledger Fabric and Zilliqa.
Table 2. Major forks and updates to the Ethereum blockchain (https://ethereum.org/en/history/ and https://github.com/ethereum, accessed on 6 September 2024.).
Table 2. Major forks and updates to the Ethereum blockchain (https://ethereum.org/en/history/ and https://github.com/ethereum, accessed on 6 September 2024.).
DateFork NameSummary
30 July 2015Ethereum (Frontier)Ethereum blockchain launch.
7 September 2015Ice Age
(Frontier Thawing)		First (unplanned) fork, providing security and speed updates. Introduced the difficulty bomb to ensure a future PoS hard fork.
14 March 2016HomesteadEnabled Ether (ETH) transactions, the native cryptocurrency of Ethereum, and facilitated the deployment of smart contracts.
20 July 2016The DAOA decentralized autonomous organization (DAO) that raised $150 million in ETH but was hacked, losing $50 million. A hard fork recovered funds, creating Ethereum Classic.
2016~Ethereum ClassicThe hard fork after the DAO hack split the original chain into Ethereum Classic, while the new chain became the main Ethereum.
18 October 2016Tangerine WhistleResponse to DDoS attacks.
22 October 2016Spurious DragonResponse to DDoS attacks.
16 October 2017ByzantiumReduced mining rewards, delayed difficulty bomb, added non-state-changing contract calls.
28 February 2019ConstantinopleEnsured blockchain functionality pre-PoS, optimized gas costs, added interaction with non-existent addresses.
8 December 2019IstanbulOptimized the gas cost.
2 January 2020Muir GlacierDelayed the difficulty bomb (by increasing the block difficulty of the PoW consensus mechanism).
15 April 2021BerlinOptimized gas costs for certain EVM actions. Increased support for multiple transaction types.
5 August 2021LondonReformed transaction fees (EIP-1559), changed gas refunds and Ice Age schedule.
9 December 2021Arrow GlacierPushed back difficulty bomb.
30 June 2022Gray GlacierPushed back difficulty bomb.
6 September 2022BellatrixPrepared Beacon Chain for “The Merge” updated fork choice rules.
15 September 2022Paris (The Merge)Ethereum successfully transitioned its consensus mechanism from PoW to PoS.
12 April 2023ShanghaiEnabled staking withdrawals on the execution layer.
12 April 2023CapellaEnabled staking withdrawals and automatic account sweeping on the consensus layer (Beacon Chain).
13 March 2024Cancun-Deneb
(Dencun)		Reduced data storage costs by lowering transaction fees, and enhanced consensus by capping validator growth and improving staker control, boosting scalability and decentralization.
Table 3. Description for data collection.
Table 3. Description for data collection.
DescriptionResults
Main Information:
 Timespan2014–2024 *
 Number of sources (journals, books, etc.)1134
 Number of documents1872
 Annual growth rate39.55%
 Document average age3.7
 Average citations per document12.78
 Number of references26,366
Keywords
 Number of keywords plus289
 Number of authors’ yeywords3090
Authors:
 Number of authors5477
 Authors of single-authored documents85
Authors Collaboration:
 Number of single-authored documents90
 Average number of co-authors per document3.77
 International co-authorship percentage26.5%
* The data for 2024 were collected up until 2 August 2024.
Table 4. Annual scientific production per year.
Table 4. Annual scientific production per year.
YearNumber of Articles Published
20141
20153
201610
201768
2018228
2019378
2020333
2021310
2022307
2023206
202428 *
* The articles for 2024 were collected up until 2 August 2024.
Table 5. Most frequent keywords for each cluster.
Table 5. Most frequent keywords for each cluster.
KeywordsCluster NumberItemsLinksTotal Link StrengthOccurrence
Blockchain1 (Red)6516926721241
Internet of Things2 (Green)4399538161
Consensus3 (Blue)38129803295
Proof of Work4 (Yellow)256127584
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Ahn, J.; Yi, E.; Kim, M. Blockchain Consensus Mechanisms: A Bibliometric Analysis (2014–2024) Using VOSviewer and R Bibliometrix. Information 2024, 15, 644. https://doi.org/10.3390/info15100644

AMA Style

Ahn J, Yi E, Kim M. Blockchain Consensus Mechanisms: A Bibliometric Analysis (2014–2024) Using VOSviewer and R Bibliometrix. Information. 2024; 15(10):644. https://doi.org/10.3390/info15100644

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Ahn, Joongho, Eojin Yi, and Moonsoo Kim. 2024. "Blockchain Consensus Mechanisms: A Bibliometric Analysis (2014–2024) Using VOSviewer and R Bibliometrix" Information 15, no. 10: 644. https://doi.org/10.3390/info15100644

APA Style

Ahn, J., Yi, E., & Kim, M. (2024). Blockchain Consensus Mechanisms: A Bibliometric Analysis (2014–2024) Using VOSviewer and R Bibliometrix. Information, 15(10), 644. https://doi.org/10.3390/info15100644

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