Blockchain and Smart Cities: Co-Word Analysis and BERTopic Modeling

Highlights

What are the main findings?

• Blockchain plays a foundational role in supporting secure, interoperable infrastructure for key urban services, particularly through integration with IoT, edge computing, and smart contracts.
• Research has shifted from general blockchain exploration to sector-specific applications, including decentralized healthcare, energy trading, smart mobility, and drone coordination.

What is the implication of the main finding?

• Blockchain enables cross-sectoral innovation in smart cities by enhancing transparency, data integrity, and trust across complex urban systems.
• As both a technological and ethical infrastructure, blockchain supports the development of secure, resilient, and sustainable smart city ecosystems aligned with Industry 5.0 values.

Abstract

This paper explores the intersection of blockchain technology and smart cities to support the transition toward decentralized, secure, and sustainable urban systems. Drawing on co-word analysis and BERTopic modeling applied to the literature published between 2016 and 2025, this study maps the thematic and technological evolution of blockchain in urban environments. The co-word analysis reveals blockchain’s foundational role in enabling secure and interoperable infrastructures, particularly through its integration with IoT, edge computing, and smart contracts. These systems underpin critical urban services such as transportation, healthcare, energy trading, and waste management by enhancing data privacy, authentication, and system resilience. The application of BERTopic modeling further uncovers a shift from general technological exploration to more specialized and sector-specific applications. These include real-time mobility systems, decentralized healthcare platforms, peer-to-peer energy exchanges, and blockchain-enabled drone coordination. The results demonstrate that blockchain increasingly supports cross-sectoral innovation, enabling transparency, trust, and circular flows in urban systems. Overall, the current study identifies blockchain as both a technological backbone and an ethical infrastructure for smart cities that supports secure, adaptive, and sustainable urban development.

1. Introduction

The concept of smart cities has rapidly evolved in recent decades as urban areas worldwide face growing challenges in sustainability, infrastructure, governance, and resource management [1,2,3]. As global populations increasingly migrate toward urban centers, cities are under pressure to become more efficient, resilient, and citizen-centric [4,5]. Smart cities aim to leverage modern technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and big data, to enhance the quality of life, reduce environmental impact, and optimize services like transportation, waste management, and energy use [6]. However, these ambitions are often hampered by fragmented infrastructures, lack of interoperability, privacy risks, and data integrity issues [7].

In response to these limitations, blockchain technology has emerged as a potential backbone for building trust, security, transparency, and decentralization within smart city ecosystems [8,9,10,11]. Originally introduced as the underlying architecture for cryptocurrencies, blockchain has evolved into a versatile tool that supports data immutability, consensus mechanisms, and decentralized applications across various domains [12,13]. The core characteristics of a blockchain—distributed ledger, transparency, auditability, and cryptographic security—provide a compelling framework for addressing the vulnerabilities of conventional smart city infrastructure [14,15].

Recent literature has increasingly emphasized the synergy between blockchain and smart cities, exploring practical implementations, benefits, and the challenges that arise. For example, blockchain enables more secure data sharing in IoT-based systems, a fundamental component of smart cities [7,8]. Its integration with IoT facilitates secure communication, device authentication, and decentralized control, which are critical for enabling real-time services in transportation, waste management, and energy distribution [16,17]. In this context, edge computing and blockchain co-designs are gaining prominence to ensure faster and localized data processing with reduced latency [18]. Waste management, which represents a persistent urban issue, stands to benefit significantly from blockchain-enabled tracking and transparency. A comprehensive review of blockchain applications in smart waste systems has indicated improvements in traceability, incentive models, and operational efficiency [19]. Similarly, another study [20] developed a blockchain-based e-waste tracing system and showed how it could enhance environmental compliance and accountability. Other works, like [21], stressed blockchain’s potential to support sustainability goals across various city sectors through decentralized resource optimization and intelligent governance.

Moreover, blockchain holds immense potential in securing democratic processes within smart cities. For instance, ref. [22] proposed a blockchain-enabled e-voting application that ensures electoral integrity while supporting mobility and accessibility in smart governance. In real estate, a traditionally opaque and bureaucratic sector, blockchain smart contracts simplify transactions and lower operational costs [23]. From a services perspective, blockchain also enhances citizen engagement, service delivery, and urban planning through decentralized platforms. The integration of blockchain with sharing economy models and public services provides more transparent and trustworthy systems [6,12]. Several researchers have also explored how blockchain converges with AI, machine learning, and fog/edge computing to provide intelligent, secure infrastructures for healthcare, transportation, and other critical urban systems [7,24,25]. Notably, scholars [8,9] have emphasized how blockchain reinforces digital trust within smart cities by securing identity, transactions, and interactions among stakeholders.

Cybersecurity remains a fundamental concern, and blockchain plays a crucial role in mitigating threats by decentralizing data control and strengthening resilience against breaches [26]. Efforts to secure transportation systems, data dissemination, and even 6G-driven urban communications are being actively explored using blockchain frameworks [27,28]. Emerging innovations like quantum-inspired blockchain for edge utilities and IPFS-based data sharing further point to a robust trajectory of blockchain research in the urban context [29,30]. Despite these advancements, there are considerable challenges in blockchain adoption in smart cities. Blockchain adoption faces key challenges such as the technology’s immaturity, limited large-scale implementations, and associated uncertainty for citizens and decision-makers [9]. Other significant issues include security and privacy concerns, storage limitations, and integration with edge computing. As smart cities generate massive amounts of data and connect countless devices, systems must be secure, efficient, scalable, and cost-effective to handle these demands without compromising user privacy, security, and access needs [9,31]. In summary, blockchain has emerged as a transformative enabler for smart city development. Through its decentralization, transparency, and security capabilities, it addresses many of the technical and governance gaps in smart urban infrastructures. The growing body of interdisciplinary research confirms its promise in a wide array of smart city applications, from waste and energy to mobility, healthcare, governance, and citizen engagement.

Although research on blockchain in smart cities is growing, there is still a lack of comprehensive reviews that use advanced bibliometric and text-mining methods, such as co-word analysis and BERTopic modeling, to explore the academic landscape of this field. Practically, co-word analysis helps identify key themes by mapping frequently used keywords, showing how research has developed and what trends are emerging. Meanwhile, BERTopic modeling represents a more recent technique that enables researchers to detect new and emerging topics, offering valuable insights into future research directions. By combining these two methods, this study provides a detailed understanding of both the established themes and the latest developments in blockchain-related smart city research. It aims to address this gap by answering the following research questions:

• What are the main thematic clusters that reflect current trends and the development of blockchain in smart cities?
• What emerging research topics are likely to shape the future of blockchain applications in smart cities?

This paper employs advanced analytical tools to provide a comprehensive and evidence-based overview of the academic discourse around blockchain in smart cities. For academics, this study highlights significant advancements and identifies deficiencies that may inform further research endeavors. It enhances theoretical models and promotes the investigation of novel concepts in this domain. The research provides valuable information for practitioners and policymakers to inform the development of smart city initiatives and policies using blockchain technology. Understanding both established and emerging trends will help decision-makers align their efforts with sustainable and technology-driven urban development, ensuring long-term resilience and efficiency in smart city systems.

2. Methodology

This study aims to answer the research questions by using a combined method of co-word analysis and BERTopic modeling to review the existing literature on blockchain in smart cities. The full research process is shown in Figure 1, which presents each step taken to ensure a clear and thorough analysis. Combining both topic modeling and thematic analysis helps to gain a better understanding of complex subjects [32]. This study follows that approach by using thematic analysis to identify key ideas in the field and topic modeling to explore specific research areas in more detail. Together, these methods offer a broad and detailed view of how blockchain is being studied in the context of smart cities. The subsequent sections detail how the data were collected, how the co-word and topic modeling techniques were applied, and how the findings from each method contribute to a comprehensive thematic understanding of the literature.

2.1. Data Collection

At the beginning of this study, we applied the PRISMA methodology for systematic reviews to ensure a clear, transparent, and replicable research process [33,34,35]. We conducted a structured review using the Scopus database in March 2025. Although we recognize that research in blockchain and smart cities evolves rapidly, systematic reviews inherently provide a temporal snapshot of the literature and are widely valued for synthesizing knowledge up to a specific point in time [36].

The search strategy included specific keywords related to blockchain and smart cities and was carefully designed to capture the relevant literature in this interdisciplinary area. To ensure the comprehensiveness of our dataset, the search terms were iteratively tested and refined in consultation with domain experts and by reviewing key articles in the field to confirm their inclusion [8]. Although the search was limited to TITLE-ABS-KEY fields, this strategy is considered both effective and standard in bibliometric and topic modeling studies, as it strikes a balance between dataset relevance and analytical scalability [36].

The search query targeted terms in the title, abstract, and keywords (TITLE-ABS-KEY) fields and focused on the following combinations: (“smart cit*” and “blockchain*”) or (“smart cit*” and BCT) or (“smart cit*” and DLT) or (“smart cit*” and “distributed ledger technolog*”). The asterisk (*) is used as a wildcard character to capture different word endings—for example, “smart cit” retrieves both “smart city” and “smart cities,” while “blockchain*” includes both “blockchain” and “blockchains.”

The review focused only on peer-reviewed journal articles to ensure the quality and relevance of the sources. While we acknowledge that conference papers, technical reports, and white papers often serve as early outlets for emerging blockchain applications in smart cities, we intentionally prioritized peer-reviewed journal articles due to their rigorous review standards and more comprehensive methodological reporting [37,38].

Moreover, this study excluded other types of publications, such as books (full books or book chapters), editorial notes, and corrections (errata). In addition, articles were removed if they did not list an author, lacked an abstract, or were not written in English. After screening and removing irrelevant records, 576 articles were retained for further analysis. We then applied co-word analysis to identify the main thematic clusters and BERTopic modeling to uncover emerging research topics, and the content used for this analysis consisted of titles, abstracts, and keywords provided by the authors. This approach enables scalable analysis of large corpora and ensures that key conceptual structures and research trends are effectively captured [39]. Numerous peer-reviewed studies in the field of bibliometrics and topic modeling have demonstrated that metadata, particularly titles, abstracts, and keywords, can reliably reflect the core content and thematic direction of scientific publications [32,40].

These advanced text-mining techniques helped map the structure and evolution of blockchain research in the context of smart cities. Microsoft Excel was employed to organize, filter, categorize, and visually present the data. The results of the co-word analysis are presented in the next subsection, followed by the BERTopic modeling outcomes, allowing for a side-by-side understanding of macro-level patterns and deeper contextual insights.

2.2. Thematic Cluster Analysis

Thematic analysis is a widely used qualitative method that helps researchers identify, examine, and interpret major patterns or themes within a body of literature. It involves several steps, including the organization, analysis, interpretation, and reporting of recurring ideas found in academic sources [41]. This method is especially valuable in the literature reviews where the goal is to summarize complex topics and highlight research trends across multiple studies. Some scholars argue that thematic analysis is not just a standalone method but also a supportive tool that enhances other qualitative approaches by providing deeper analytical insight [42]. Its adaptability and accessibility make it useful across a wide range of disciplines, including smart city development, finance, and construction management. For example, a thematic analysis approach was used to explore blockchain in construction management, where key themes were identified through clustering and co-word analysis techniques over five years (2017–2023) [43]. Similarly, in the context of smart cities, a thematic review helped identify people-centered governance strategies by grouping findings into thematic categories, showing how public participation and digital technologies interact [44]. Another study applied thematic analysis to evaluate blockchain applications in banking and finance, demonstrating how this method helps classify existing knowledge and uncover research gaps [42].

In this study, we conducted thematic cluster analysis using the VOSviewer (version 1.6.20), which is a specialized software tool designed for creating and assessing bibliometric maps [45]. The analysis was based on author-supplied keywords from the selected publications. In the keyword co-occurrence network, each node represents a unique keyword, and the links between nodes indicate how often those keywords appear together in the same articles [46]. The color of each node reflects the cluster or thematic group it belongs to. Each color represents a different research theme, based on how frequently keywords are mentioned together. Keywords that are located closer together in the visual map typically have a stronger relationship and suggest that those topics are often discussed together in the literature and are therefore closely related conceptually [32]. We acknowledge that co-word analysis captures term frequency and proximity without understanding semantic meaning [47]. To address this limitation, we complemented the analysis with BERTopic modeling, which leverages transformer-based embeddings to detect deeper contextual and semantic relationships between terms [32]. This mixed-method approach allowed us to balance surface-level keyword associations with deeper thematic insight and improve the overall interpretability and robustness of the findings. This methodological linkage ensures that both breadth and depth are addressed cohesively across this study.

2.3. Topic Modeling

Topic modeling has become an essential technique in qualitative research and literature reviews, especially in identifying and analyzing patterns within large textual datasets [32]. In the context of blockchain research for smart cities, it supports a more structured and systematic understanding of academic developments. Many scholars have emphasized how combining topic modeling with machine learning can improve the efficiency and accuracy of literature reviews by uncovering hidden themes and emerging trends [48]. Recently, several studies have illustrated the usefulness of topic modeling in different research domains. For instance, Nikolenko et al. [49] proposed an advanced method that combines topic modeling with a tf-idf coherence score to improve the quality of results in exploratory reviews. Similar approaches have been applied in reviews across diverse fields, such as financial fraud detection [50], humanitarian logistics [51], sentiment analysis [52], transnational entrepreneurship [53], and medical research [54], demonstrating topic modeling’s flexibility and analytical power.

In this study, we apply BERTopic as an advanced topic modeling method that uses pre-trained BERT (Bidirectional Encoder Representations from Transformers) language models. Unlike traditional models such as LDA (Latent Dirichlet Allocation), BERTopic captures the deeper meanings and contextual relationships in text, making it particularly suitable for analyzing complex topics like blockchain integration in smart city systems [55,56]. According to Chen et al. [55], BERTopic outperforms conventional models like LDA and NMF (Non-Negative Matrix Factorization) in terms of producing semantically rich and interpretable topics. To apply this method effectively, we followed a rigorous data preparation process.

After extracting relevant literature, we performed several preprocessing tasks, including text cleaning, lemmatization, tokenization, and other natural language processing (NLP) techniques. The textual data were then embedded using the “all-MiniLM-L6-v2” model from Sentence Transformers. To improve visualization and clustering, we applied Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction, allowing the BERTopic model to generate coherent topic clusters with high interpretability [57]. To mitigate BERTopic’s sensitivity to poorly written or overly short text segments, we excluded incomplete abstracts and applied a minimum token length threshold during preprocessing. This ensured that only the semantically rich and sufficiently detailed documents contributed to the modeling, which enhances the quality and coherence of the resulting topics.

The BERTopic process begins by converting text into sentence embeddings, which are then used to form topic clusters based on shared patterns and contextual similarity. Using the c-TF-IDF algorithm (class-based Term Frequency–Inverse Document Frequency), we refined topic boundaries and increased accuracy. Each document was assigned to a specific topic, with probabilities calculated to show the degree of fit. We identified seven main topic clusters across the literature, labeled from Topic -1 to Topic 6. In BERTopic modeling, Topic -1 typically represents a residual or undefined grouping that collects documents with weak or ambiguous topic associations. In our case, this topic was thematically broad and lacked coherent internal structure, making it analytically uninformative. For this reason, it was excluded from the subsequent analysis to maintain focus on well-defined and actionable thematic clusters. This refinement also responds to concerns about overly general topics diluting interpretability and relevance for policy and scholarly implications.

To assess the reliability of the results, we conducted a coherence validation using the normalized pointwise mutual information (NPMI) metric.

Table 1. Topic coherence comparison (NPMI scores).

Topic	BERTopic (NPMI)	LDA (NPMI)
0	0.72	0.6
1	0.75	0.58
2	0.71	0.62
3	0.69	0.55
4	0.74	0.61
5	0.7	0.59
6	0.73	0.57
Average	0.72	0.59

below compares average coherence scores between BERTopic and LDA across the analyzed topics (Topics 0 to 6), excluding Topic-1.

These results suggest that BERTopic consistently produced higher topic coherence across all analyzed clusters. Furthermore, we performed a cross-validation by applying LDA to the same dataset and comparing topic outputs. While LDA captured broader categories, BERTopic offered greater granularity and contextual depth, which better aligns with the complexity of the blockchain–smart city intersection. Topic coherence was evaluated to ensure clarity and internal consistency, while the distribution of documents across topics was also analyzed to reveal dominant and emerging areas.

By integrating the results from co-word analysis and BERTopic modeling, this study distinguishes between core themes and emerging topics. This combination allows us to observe how foundational concepts connect with newer developments and offers a multidimensional view of the blockchain-in-smart-cities research landscape. In doing so, this work contributes a comprehensive thematic overview and offers detailed insights into specific research directions. These insights are valuable for scholars who aim to identify gaps and propose new research agendas and for policymakers and technology developers interested in shaping sustainable and data-driven smart cities that leverage blockchain technology.

3. Discussion of Results

In the current study, the thematic clustering analysis reveals that research at the nexus of blockchain technology and smart city development is organized around five thematic clusters. Figure 2 presents the keyword co-occurrence network, illustrates the relational density among prominent keywords, and signals the emerging research contours in this interdisciplinary space.

3.1. Keyword Co-Occurrence Analysis

3.1.1. Technological Infrastructure for Secure Smart Cities

The first cluster is primarily oriented around the technological infrastructure that enables secure, intelligent, and interoperable urban environments. The centrality of terms such as “BCT,” “Smart City,” “IoT,” and “Smart Contract” suggests a foundational layer where blockchain serves as both a secure ledger and a decentralized coordination mechanism for smart urban ecosystems [58,59]. These elements indicate that blockchain is increasingly embedded in the technological fabric of urban infrastructure to underpin diverse smart city functionalities from data authentication to service automation [60,61,62].

In this cluster, security and privacy are dominant themes, as reflected by frequently co-occurring keywords such as “Security,” “Privacy,” “Authentication,” “Access Control,” “Security and Privacy,” and “Data Integrity.” These concerns are intrinsic to the deployment of decentralized technologies within highly networked environments such as cities, where vast quantities of personal, operational, and infrastructural data are generated and exchanged [63,64]. The inclusion of “Intrusion Detection,” “Intrusion Detection System,” and “Privacy-Preserving” further underscores a shift toward resilience and trust in data management processes [60,65]. According to Rejeb et al. [9], blockchain’s immutable and transparent ledger system is being explored as a countermeasure to cyber vulnerabilities, reinforcing secure data flows across urban systems. However, the immutability of blockchain also poses limitations, such as challenges in updating or correcting erroneous data and high computational costs that may hinder scalability in resource-constrained urban infrastructures.

The interplay between edge technologies and blockchain is highlighted by the co-occurrence of terms such as “Edge Computing,” “Fog Computing,” “Cloud Computing,” and “MEC” (Multi-access Edge Computing). Sameer Jabar et al. [66] argue that this convergence paves the way for an architectural paradigm in which distributed computing resources complement blockchain-based platforms to process data locally, reduce latency, and preserve privacy. By distributing intelligence closer to the data source, these infrastructures enable real-time analytics and responsiveness essential for urban operations such as traffic control, emergency response, and smart grid optimization [67,68,69]. In addition, emerging technologies like “Deep Learning” and “SDN” (Software-Defined Networking) also appear prominently and indicate an ecosystemic approach wherein AI-enhanced analytics and programmable network architectures are integrated with blockchain systems. Such integration can enhance automation and facilitate intelligent decision-making across interconnected urban subsystems, thereby aligning with broader smart city objectives such as efficiency, adaptability, and responsiveness [11]. Nevertheless, interoperability challenges between blockchain and these emerging technologies remain a significant issue, requiring standardized frameworks and protocols.

Furthermore, the co-occurrence of “Mobile Crowdsensing,” “Open Data,” and “Smart Environment” reflects a growing research interest in participatory urbanism, where citizens act as both data producers and beneficiaries of decentralized services [70,71]. In this regard, blockchain enables trust in such participatory frameworks by validating contributions and safeguarding user data [71,72]. “Sharing Economy,” “Cryptocurrency,” and “Digitalization” frequently appear within the same cluster. These keywords illustrate blockchain’s role in facilitating novel economic models, where tokenization and decentralized exchanges are transforming urban transactions and service delivery [73,74].

Finally, institutional and governance-related terms such as “Urban Governance,” “e-Government,” and “ICT” point to a scholarly focus on the administrative applications of blockchain to foster transparency, accountability, and interoperability among urban stakeholders [75,76]. This reflects a broader reimagining of governance architectures based on blockchain technologies to support decentralized identity, verifiable records, and secure citizen–state interactions. Nonetheless, issues such as regulatory uncertainty, data privacy compliance, and citizen trust remain critical barriers that must be addressed for widespread implementation. Overall, the first cluster underscores a tightly interwoven research landscape where blockchain acts as the spine of secure, interoperable, intelligent smart cities.

3.1.2. AI-Driven Sustainability and Intelligent Urban Services

The second cluster focuses on the growing convergence of AI, sustainability, and decentralized energy and service systems in smart cities. This cluster is anchored by high-frequency terms such as “AI,” “Sustainability,” “Smart Grid,” and “Machine Learning,” which indicate a strategic focus on leveraging intelligent technologies to enhance the environmental, operational, and social performance of urban systems [77,78,79]. The co-occurrence network reveals that these innovations are increasingly intertwined with energy optimization, mobility, health, and waste management, which represent core pillars of smart urban life. This convergence offers major advantages such as increased automation, improved efficiency, and enhanced predictive capabilities across city systems.

A defining feature of this cluster is the central role of AI and machine learning in advancing urban sustainability goals [80,81,82]. As such, scholars focus on the use of data-driven methodologies like “Big Data,” “Reinforcement Learning,” and “Anomaly Detection” to enhance system efficiency and adaptability [83,84,85]. These technologies play a critical role in supporting various smart city services, ranging from energy demand forecasting and predictive maintenance to optimized mobility routing and personalized health services [86,87]. As a result, they can contribute to emissions reduction and resource conservation. However, their effectiveness is often limited by issues of data privacy, algorithmic bias, and the need for large, high-quality datasets.

Furthermore, energy systems transformation emerges as a key thematic axis in the cluster. Terms like “Smart Grid,” “Microgrid,” “Energy Management,” and “P2P Energy Trading” demonstrate a shift towards decentralized, intelligent, and participatory energy infrastructures [67,88,89]. For instance, the presence of the keywords “Prosumer,” “Smart Meter,” and “Hyperledger” shows the importance of blockchain technology in propelling new urban energy paradigms that facilitate transparent, secure, and automated energy transactions among producers and consumers [90,91,92]. Recent studies further reinforce this perspective: Zhou and Zhou [93] present a comprehensive architecture for automatic, tamper-proof energy trading using blockchain smart contracts and secure consensus protocols, offering scalable and trust-based energy marketplaces for smart cities. This use of blockchain ensures trust and traceability in energy exchanges, but challenges remain in terms of interoperability, regulatory compliance, and the high energy consumption of certain blockchain frameworks. Thus, this indicates a democratization of energy systems, which aligns with broader sustainability goals and empowers technological decentralization.

In addition, there is a parallel effort to secure and simulate critical urban infrastructures. As real-time digital replicas of physical assets, digital twins are increasingly being paired with AI and blockchain to monitor, optimize, and secure complex systems such as transportation networks, utility grids, and healthcare facilities [80,94]. These integrations enhance resilience, enabling cities to anticipate disruptions, simulate alternative scenarios, and make informed, real-time decisions. Nevertheless, integrating blockchain into such real-time systems demands robust latency handling and compatibility with legacy infrastructure.

Urban mobility and logistics are also central concerns for scholars, and this is reflected by the use of the keywords “Smart Mobility,” “Smart Transportation,” and “Transport.” AI-powered systems are being deployed to optimize traffic flow, reduce congestion, and enhance public transportation accessibility, while blockchain supports trusted data exchange between stakeholders [63,95]. In recent research on the low-altitude economy, Zhou [96] demonstrates how blockchain supports secure UAV coordination in aerial logistics networks, enabling real-time data sharing, route validation, and energy-efficient navigation in urban drone ecosystems. These findings show blockchain’s adaptability to the UAV sector and its relevance to decentralized smart city services involving autonomous aerial mobility. Despite these benefits, scalability and user adoption remain critical barriers, particularly when attempting to integrate blockchain into city-wide transport systems.

The co-existence of “Smart Health” and “Waste Management” further emphasizes a holistic vision of urban well-being that is enabled by the convergence of AI, IoT, and blockchain to ensure cleaner, safer, and healthier environments for citizens [19,97]. Another notable trend is the emergence of intelligent communities, which reinforce the transition from centralized governance models to decentralized and citizen-centric urban ecosystems [9]. In this context, AI and blockchain serve as foundational layers for participatory decision-making, transparent resource allocation, and ethical data governance, aligning with smart city ideals of inclusivity and resilience.

3.1.3. Decentralized Intelligence and Secure Mobility

The third cluster concentrates on the integration of decentralized technologies and intelligent transportation systems (ITS) to facilitate secure, efficient, and sustainable mobility in smart cities. Central to this cluster are keywords such as “DLT” (Distributed Ledger Technology), “ITS,” “Internet of Vehicles,” and “V2X” (Vehicle-to-Everything), which collectively stress a research focus on the adoption of blockchain-based architectures in next-generation urban mobility infrastructures [60,98,99].

The analysis of the network shows a growing convergence of distributed computing, edge intelligence, and vehicular communication networks in shaping the connected urban future. More specifically, blockchain serves as a foundational technology enabling decentralized trust, secure communication, and tamper-resistant data management in vehicular and transportation systems [100,101,102]. The cluster includes frequent terms such as “Consensus Mechanism,” “Consensus Algorithm,” and “Cryptography” and highlights the need for secure and scalable protocols that ensure data integrity and system reliability in dynamic and distributed mobility networks. These technologies are particularly relevant in environments characterized by high data velocity and low-latency demands, such as vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication systems [103,104].

Similarly, “Internet of Vehicles,” “VANET” (Vehicular Ad Hoc Networks), and “V2X” emerge as important keywords that mirror the evolution of mobility systems from centralized models to cooperative and self-organizing vehicular networks [98,105,106]. These systems rely on real-time data sharing and trust mechanisms, which blockchain and DLTs are increasingly positioned to provide [101,107,108]. As such, the application of blockchain in this context extends beyond secure data exchange because it supports liability attribution, traffic coordination, and service authentication, thereby enhancing both the safety and efficiency of urban transportation [109,110]. Nonetheless, the deployment of blockchain in highly dynamic mobility environments presents challenges related to scalability, energy efficiency, and integration with legacy systems.

Emerging learning paradigms, notably “Federated Learning,” suggest that AI integration is becoming increasingly privacy-aware and decentralized [28,111,112]. Muhammad et al. [110] note that federated learning enables collaborative model training across vehicles or edge nodes without centralized data collection, thereby preserving user privacy and reducing transmission overhead. When combined with blockchain-based audit trails, this approach allows for transparent and privacy-preserving analytics, which is essential for environments like smart transportation and UAV coordination [113,114].

The third cluster covers topics associated with aerial mobility and surveillance systems, which are gaining prominence in urban planning and public service delivery [115,116,117]. Drones are being deployed for traffic monitoring, emergency response, and infrastructure inspection, and their integration with blockchain supports secure data logging, autonomous coordination, and mission accountability [118,119]. The keyword “5G” alongside these terms indicates the role of ultra-low-latency and high-bandwidth communication in supporting real-time and decentralized operations across aerial and ground-based smart systems [31,120,121].

The literature also discussed security-related issues, including “IoT Security,” “Data Privacy,” “Trust Management,” and “Cryptography” [82,122,123]. This indicates that trust and privacy are not auxiliary concerns but central design imperatives. The inherent vulnerabilities of highly connected and mobile urban systems necessitate robust mechanisms for authenticating devices, managing permissions, and protecting data integrity. With their transparent and immutable nature, blockchain and DLTs provide a trusted infrastructure for ensuring that these requirements are met in a scalable and decentralized manner.

“Sustainable Smart City” and “Built Environment” are themes that reflect a growing recognition of the urban–structural implications of decentralized mobility technologies. As smart cities evolve, their physical and digital infrastructures must co-adapt to accommodate autonomous systems, distributed networks, and edge computing nodes [79,124]. Blockchain thus becomes part of a broader digital–physical ecosystem that supports technological innovation and urban sustainability, efficiency, and resilience. In summary, the third cluster illustrates a robust and multidimensional research trajectory focused on decentralized mobility, intelligent transport, and secure data exchange. Through the integration of blockchain, federated learning, and vehicular networks, this body of work envisions a smart city landscape where mobility is autonomous, data are sovereign, and infrastructure is both intelligent and trustworthy.

3.1.4. Decentralized Energy Ecosystems and Trusted Home-Scale Infrastructure

The fourth cluster revolves around the deployment of blockchain in localized, trusted energy ecosystems and smart residential environments. The recurrence of keywords such as “Smart Home,” “Electric Vehicle,” “Energy Trade,” and “Hyperledger Fabric” indicates that scholars studied how blockchain can support decentralized, privacy-preserving, and interoperable systems within the domestic and prosumer-focused layer of the smart city infrastructure [84,125,126]. At the core of this cluster lies the concept of the smart home as an energy and data hub, where residential units are no longer passive energy consumers but active participants in local and distributed energy markets [63,127,128].

The keywords “Electric Vehicle” and “Energy Trade” appear as relevant themes that illustrate an emerging model in which homes equipped with electric vehicles and solar panels interact dynamically with the grid and with other prosumers [129,130,131]. Blockchain-based platforms enable these exchanges to occur in a transparent, verifiable, and secure manner, thereby fostering greater energy efficiency and resilience at the microgrid level [67,88,132].

With respect to energy, the prominence of “Hyperledger Fabric” and “Consortium Blockchain” indicates a shift away from permissionless public blockchains toward permissioned and enterprise-grade frameworks tailored to trusted community networks or regulated energy environments [133,134,135]. These architectures provide fine-grained control over access and data sharing, making them suitable for use cases involving utilities, energy providers, and consumers in tightly governed ecosystems [29,136].

More precisely, Hyperledger Fabric is favored for its modularity, scalability, and support for confidential transactions [7,135]. Majeed et al. [7] contend that these features are essential for real-world energy markets and home automation systems. The selected articles also studied the concept of “IPFS” (InterPlanetary File System) and stressed the importance of decentralized storage solutions in managing the vast amounts of data generated within smart homes [113,126]. If paired with blockchain, IPFS can provide data immutability, traceability, and availability without relying on centralized data repositories. This is especially critical for applications that involve real-time monitoring, usage tracking, and energy forecasting, where both integrity and performance are non-negotiable [67,137].

Finally, “Privacy Protection” and “Integrity” are also important keywords that further underscore the dual focus on data sovereignty and trust [138,139]. In the context of smart homes and residential energy trading, the protection of user data from unauthorized access or misuse is a foundational requirement. Owing to its decentralized storage and identity verification, blockchain’s cryptographic primitives are increasingly being leveraged to uphold these requirements [9].

The presence of “Privacy Protection” also reflects a broader societal concern regarding surveillance and data exploitation in digitally connected living spaces, suggesting that future smart home systems must be designed with embedded privacy features by default. Collectively, the fourth cluster presents a vision of the smart city where households function as autonomous and intelligent nodes within a larger urban ecosystem. The integration of electric vehicles adds further complexity and opportunity, creating mobile energy storage units that can interact with homes and grids alike in a decentralized manner.

3.1.5. Blockchain for Health, Built Environments, and Sustainable Development

The final cluster relates to the application of blockchain technology to public health, sustainable infrastructure, and circular economy initiatives. The cluster encompasses several keywords, including “Healthcare,” “COVID-19,” “Smart Building,” and “Circular Economy”, reflecting a multidisciplinary research focus that bridges technological innovation with public welfare, environmental sustainability, and adaptive infrastructure [87,140]. Studies pertaining to this cluster articulate the evolving role of blockchain as a foundational enabler for trusted, resilient, and sustainable urban systems in times of crisis and long-term transformation [141,142,143].

For instance, the high frequency of “Healthcare” and “COVID-19” suggests a strong emphasis on the role of blockchain in managing health data and crisis response [81,86,141]. During the pandemic, blockchain emerged as a trusted infrastructure for verifying health credentials, tracking contagion, and securing sensitive medical information [144]. The technology’s decentralized and immutable characteristics enabled new paradigms of secure data sharing among patients, providers, and government entities without compromising privacy or trust. These use cases have extended beyond emergency scenarios, paving the way for blockchain-driven electronic health records, telemedicine authentication, and interoperable patient data management in smart city health ecosystems [109,145].

Another focus is on the concept of “Smart Building” that expands the cluster’s scope to encompass built environment resilience and adaptive infrastructure [141,146,147]. In the post-pandemic era, smart buildings are being reimagined as health-aware and sensor-equipped environments that are capable of monitoring occupancy, ventilation, and sanitation [119,141,146]. Blockchain supports these environments by ensuring tamper-proof data logs, secure access control, and transparent maintenance records.

These capabilities improve operational efficiency and foster occupant trust in digitally mediated building systems, contributing to broader smart city resilience goals. The presence of “Circular Economy,” “IIoT” (Industrial Internet of Things), and “Sustainable Development” highlights a secondary but deeply interconnected focus on environmental and economic sustainability [148]. Blockchain is increasingly applied to track product lifecycles, certify recycling processes, and enforce environmental compliance across industrial and urban supply chains.

Combined with IIoT sensors and smart contracts, blockchain can automate resource tracking, enable real-time reporting of emissions and waste, and support sustainable procurement policies—which are all key components of a functioning circular economy [149]. Lastly, “Sustainable Development” as a recurring keyword points to the overarching goal of aligning blockchain-enabled smart city initiatives with the United Nations Sustainable Development Goals (SDGs).

Whether in managing energy in smart buildings, improving healthcare outcomes, or supporting green industrial practices, blockchain serves as a transparent and accountable infrastructure that can bridge siloed systems and ensure verifiable progress toward sustainability targets [150,151]. To sum up, the final cluster addresses an emerging and highly integrative research direction in which blockchain supports public health resilience, sustainable infrastructure, and environmentally conscious urban policy.

3.2. Findings from BERTopic Modeling

BERTopic proves to be an effective methodology for extracting semantically rich and context-aware themes within the scholarly literature on blockchain technology and smart cities. By leveraging transformer-based embeddings, BERTopic enables the identification of nuanced relationships between terms and allows for a more refined understanding of thematic structures across the corpus. This technique captures the surface-level co-occurrence of keywords and deeper contextual alignments, offering a comprehensive view of how various topics are interconnected within the research landscape. The topical content, their interrelationships, and the relative importance of each topic are detailed in this section.

Table 2. BERTopic model output.

Topic	Top Keywords
0	“iot”×0.0427 + “smart”×0.0383 + “blockchain”×0.0371 + “smart city”×0.0314 + “city”×0.0308 + “security”×0.0279 + “data”×0.0239 + “device”×0.0186 + “network”×0.0183 + “system”×0.0172
1	“city”×0.0534 + “smart”×0.0517 + “smart city”×0.0452 + “blockchain”×0.0365 + “technology”×0.0285 + “research”×0.0175 + “digital”×0.0159 + “data”×0.0154 + “urban”×0.0146 + “sustainable”×0.0136
2	“vehicle”×0.0438 + “blockchain”×0.0329 + “data”×0.0282 + “traffic”×0.0259 + “system”×0.0259 + “smart”×0.0258 + “transportation”×0.0234 + “network”×0.0221 + “city”×0.0221 + “smart city”×0.0188
3	“energy”×0.0772 + “smart”×0.0340 + “trading”×0.0327 + “grid”×0.0308 + “blockchain”×0.0296 + “energy trading”×0.0247 + “system”×0.0219 + “smart grid”×0.0210 + “charging”×0.0209 + “power”×0.0204
4	“healthcare”×0.0692 + “medical”×0.0442 + “patient”×0.0423 + “data”×0.0415 + “health”×0.0350 + “blockchain”×0.0339 + “smart”×0.0264 + “privacy”×0.0250 + “iot”×0.0213 + “city”×0.0209
5	“waste”×0.1545 + “waste management”×0.0940 + “management”×0.0672 + “blockchain”×0.0277 + “city”×0.0266 + “smart”×0.0264 + “solid waste”×0.0231 + “system”×0.0230 + “management system”×0.0220 + “solid”×0.0220
6	“drone”×0.1575 + “iod”×0.0660 + “security”×0.0545 + “internet drone”×0.0326 + “monitoring”×0.0276 + “authentication”×0.0247 + “network”×0.0235 + “blockchain”×0.0234 + “uav”×0.0233 + “internet”×0.0227

presents the set of identified topics along with the ten most representative words for each, ranked by their probability within the topic distribution. Titled “Blockchain-Driven Security and Privacy Solutions for IoT-Enabled Smart Cities,” Topic 0 emerges as the most prominent and accounts for approximately 24.82% of the literature. This is followed by Topic 1, “Strategic and Technological Perspectives on Blockchain Adoption in Smart Cities,” which represents 19.09% of the analyzed content. Topic 2, “Blockchain-Enabled Intelligent Transportation and Mobility in Smart Cities,” contributes 13.71% and indicates a substantial focus on mobility-oriented blockchain applications.

Additional topics cover a diverse range of use cases, including blockchain-based energy management and electric mobility (Topic 3), smart healthcare systems for urban resilience (Topic 4), and waste management and circular economy practices (Topic 5). Moreover, Topic 6, which focuses on security and trust mechanisms for drone-based applications, reflects a cutting-edge area of urban innovation. The spatial distribution of these topics, as visualized in the inter-topic distance map (Figure 3), further validates the distinctiveness of the identified topics. The separation between topics confirms that each topic occupies a unique position within the research landscape, reflecting specialized discussions rather than overlapping or redundant themes. This clear delineation reinforces the robustness of the BERTopic model in capturing the evolving thematic architecture of blockchain-related smart city research.

3.2.1. Blockchain-Driven Security and Privacy Solutions for IoT-Enabled Smart Cities

Topic 0 centers on blockchain-driven security and privacy solutions for IoT-enabled smart cities, representing approximately 24.82% of the total research corpus. This topic discusses blockchain’s essential role in securing the vast networks of interconnected devices that define modern urban systems. The studies pertaining to this topical area emphasize the ability of blockchain to provide decentralized authentication, data integrity, and privacy preservation, particularly in resource-constrained environments such as edge networks.

One study by Iftikhar et al. [152] proposes a blockchain-based multifactor authentication system for edge computing environments. Using a consortium blockchain and biometric verification enhanced with fuzzy extractors, their model defends against impersonation and reflection attacks while reducing communication and computational costs, making it suitable for real-time smart city applications. Moreover, Luo et al. [153] focus on cross-domain authentication in smart parks and suggest a lightweight blockchain solution that allows each device to use a single certificate across domains, with an efficient revocation mechanism that minimizes overhead and enhances interoperability. This approach ensures secure device cooperation in complex, multi-domain urban settings.

In another contribution, Rahman et al. [154] present a privacy-preserving framework for Wireless Multimedia Sensor Networks (WMSNs) using blockchain. By replacing centralized structures with decentralized certification and communication through trusted cluster heads, the system enhances energy efficiency and detection accuracy by over 30%, making it ideal for surveillance and multimedia applications. Similarly, Mishra and Chaurasiya [155] integrate blockchain with a hybrid deep learning model (LSTM-SVM) to detect malicious transactions in smart city IoT networks. In the proposed system, blockchain serves as a trust layer to validate genuine transactions, and the AI component boosts accuracy to 97%, demonstrating a powerful combination of machine learning and decentralized security. Finally, Awotunde et al. [156] use a CNN-KPCA model supported by blockchain to detect threats and enhance privacy. Their system improves performance on benchmark datasets and reinforces blockchain’s value in securing real-time urban systems. Overall, Topic 0 highlights blockchain as a core enabler of cybersecurity in smart cities, offering scalable, trustworthy solutions for authentication, data protection, and resilient IoT integration.

3.2.2. Strategic and Technological Perspectives on Blockchain Adoption

Topic 1 reflects the strategic, institutional, and technological dimensions of blockchain integration into smart city ecosystems. The central theme of this cluster emphasizes how blockchain is a technical tool and a promising infrastructure that reshapes urban governance, planning, and service delivery. Key studies in this area investigate frameworks, adoption strategies, sectoral applications, and public value creation through blockchain within the smart city context.

For example, Faris Alketbi et al. [157] explore the technological potential and regulatory challenges of blockchain implementation in urban services and present an innovative certification framework designed to balance decentralization with regulatory oversight. Through the case of Dubai’s Blockchain Strategy, the authors demonstrate how targeted government initiatives can drive urban digital transformation, enhance citizen trust, and contribute to sustainable development goals. Sahoo et al. [158] conduct a systematic scoping review to evaluate the global research landscape on blockchain and smart cities. By analyzing 79 peer-reviewed articles determined as most relevant using the methods described above, this study identifies five thematic clusters, ranging from urban mobility and governance to resource management and digital identity.

A further study by Ullah et al. [21] assesses the role of blockchain in advancing sustainable smart cities and illustrates applications in healthcare, transportation, energy, tourism, and 5G/6G infrastructure. According to the authors, blockchain supports the intelligent use of ICTs to address environmental and operational challenges, promoting more efficient and citizen-centered urban ecosystems. Treiblmaier et al. [8] develop a comprehensive research agenda by identifying nine application domains of blockchain within smart cities, including e-voting, logistics, education, and public services. Alkhaldi et al. [159] analyzes how public sector leaders perceive blockchain’s value in smart city development in Kuwait. The findings show strong support for blockchain’s utility but stress the need for a structured framework to articulate its public value, particularly within policy and institutional settings.

Together, these studies highlight the multifaceted role of blockchain technology in the strategic design and technological execution of smart cities, advocating for robust frameworks, interdisciplinary collaboration, and governance models that support scalable and sustainable adoption.

3.2.3. Blockchain-Enabled Intelligent Transportation and Mobility

Topic 2 focuses on the integration of blockchain technology into intelligent transportation systems (ITS) in smart cities. Studies related to this topic discuss the complex challenges of urban mobility, including security, data integrity, system efficiency, and real-time decision-making. Scholars also explore how blockchain can support decentralized, secure, and energy-efficient transport infrastructures.

For example, Das et al. [160] perform a comprehensive analysis of the role of blockchain in enhancing data security, privacy, and service interoperability in ITS. The authors emphasize the use of blockchain for secure vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, autonomous vehicle coordination through smart contracts, and decentralized mobility marketplaces. While challenges such as scalability and energy consumption persist, blockchain has the potential to foster innovation and transparency in smart mobility services. Ge and Qin [161] examine the fusion of digital twin technology and ITS to form DT-ITS models. Blockchain is presented as a core enabler for secure communication and data exchange among stakeholders in these systems. In combination with technologies such as federated learning, edge–cloud collaboration, and 5G, blockchain ensures data trustworthiness, system autonomy, and improved operational transparency in future urban mobility systems.

In a related study, Gantla et al. [162] explore a real-time traffic and environmental data fusion system, integrating sensor inputs through AI-driven algorithms for traffic signal control and pollution forecasting. Blockchain is used as a security layer to guarantee data integrity and prevent tampering, enabling low-latency, decentralized decision-making. The system demonstrates significant improvements in traffic flow and air quality, showcasing the synergy between AI, edge computing, and blockchain. Parking management is another key area addressed.

In this regard, Singh et al. [163] present a blockchain-secured smart parking framework that uses LSTM networks and RSU-based blockchain to recommend optimal parking locations. The system reinforces energy efficiency and secure data communication, validated through real-time analysis. Similarly, D and Baskaran [164] combine deep learning with redactable consortium blockchain to improve privacy, authentication, and data accuracy in urban parking systems, with demonstrated gains in secure space allocation. Overall, Topic 2 indicates the ability of blockchains to act as a trust layer in intelligent urban mobility and enable decentralized control, secure data exchange, and real-time optimization in transport networks, parking systems, and environmental management across smart cities.

3.2.4. Blockchain Applications for Sustainable Energy and Electric Mobility

Topic 3 focuses on the application of blockchain technology in sustainable energy management and electric mobility, which represents a key domain for the realization of efficient, decentralized, and low-carbon smart city infrastructures. Blockchain can support energy trading, smart grid operations, prosumer markets, and energy resilience by ensuring transparency, automation, and trust in complex energy systems.

For instance, Yahaya et al. [165] propose a blockchain-based energy trading model for smart communities using novel consensus mechanisms, proof-of-energy reputation, to reduce the computational burden of traditional methods. A mutual, verifiable fairness mechanism ensures secure and transparent transactions, thereby preventing fraudulent behavior in peer-to-peer exchanges. The results of this study demonstrate significant reductions in energy costs and peak-to-average demand ratios, highlighting blockchain’s potential in optimizing grid performance.

In addition, Draz et al. [166] introduce a blockchain-enabled energy swapping protocol tailored for wireless sensor networks. The system allows sensor nodes to autonomously trade surplus energy using lightweight smart contracts and a proof-of-stake consensus mechanism. Compared to centralized systems, this decentralized approach increases node lifespan by 20% while ensuring low communication overhead and strong data security, which is critical in energy-constrained environments like smart cities and precision agriculture.

A broader architectural perspective is provided in the study by Appasani et al. [88], who examine blockchain integration within smart grid applications. The authors present a taxonomy of blockchain-enabled use cases, including electric vehicle integration, smart metering, and distributed energy resource coordination. The findings indicate that blockchain can improve the decentralization, security, and operational intelligence of modern grid systems while outlining future research needs across architecture and implementation levels.

Furthermore, Park et al. [127] propose a blockchain-based P2P energy trading platform for smart homes, introducing the concept of “energy tags”, which represent smart contracts that automate and optimize energy transactions based on cost, source quality, and user demand. Their simulation results suggest that the platform reduces energy costs, increases transaction transparency, and promotes blockchain as a solution for a sustainable and user-centered energy ecosystem.

Cui et al. [167] develop a double auction mechanism using consortium blockchain to facilitate secure and economically efficient energy trading among prosumers. The model improves market fairness and encourages participation by embedding auction logic directly into blockchain consensus protocols, thereby ensuring truthful bidding and reliable transaction execution. Therefore, Topic 3 clarifies how blockchain can be relevant in the decentralized energy transition, positioning it as a crucial enabler for smart cities seeking to enhance energy efficiency, market democratization, and sustainability.

3.2.5. Blockchain-Enabled Smart Healthcare Systems for Urban Health Resilience

Topic 4 focuses on the role of blockchain technology in smart healthcare systems and elaborates on its potential to enhance data security, patient privacy, interoperability, and overall healthcare resilience in urban environments. Related studies to the topic include the research of Ali et al. [168], who introduce a permissions-based blockchain framework integrated with hybrid deep learning to ensure scalable and secure healthcare systems. By combining blockchain with deep learning models, the framework facilitates secure storage and real-time analysis of sensitive patient data, enabling timely diagnostics and data-driven healthcare decisions.

This approach ensures only authorized access to medical records and improves both privacy and interoperability in healthcare delivery. In the same vein, Omar et al. [140] present a privacy-preserving healthcare platform for smart cities, where patient data and insurance information are managed on the Ethereum blockchain. This study addresses issues around data manipulation and fraudulent activities, proposing a system that secures both electronic health records and financial data through smart contracts and cryptographic tools, thus promoting transparency and trust among healthcare entities.

Albahli et al. [145] introduce a multi-level blockchain eHealth architecture to modernize the healthcare infrastructure in Saudi Arabia. The solution enables seamless access and sharing of health records across public and private providers while aligning with national transformation strategies. The authors highlight the disparity in data accessibility between public and private hospitals and advocate for blockchain to standardize and decentralize data access.

A further study by Aldhyani et al. [169] targets breast cancer detection within IoHT environments, integrating blockchain with federated learning to support data security, privacy, and model trustworthiness. The system encrypts data exchange among clients, verifies provenance, and enhances model transparency, which are important factors in increasing social acceptance of AI in healthcare.

Lastly, Dwivedi et al. [170] proposed a blockchain framework for IoT-based remote patient monitoring, focusing on privacy-preserving data logging and secure transaction management. By tailoring blockchain protocols to the limitations of IoT devices, this study offers a lightweight and cryptographically secure solution suitable for widespread healthcare deployment in smart cities. Overall, blockchain plays a critical role in building secure, transparent, and patient-centric healthcare ecosystems, enhancing resilience, trust, and privacy in increasingly digital urban health infrastructures.

3.2.6. Blockchain Applications in Smart Waste Management and Recycling Systems

Topic 5 uncovers the potential of blockchain for enhancing waste management and recycling systems within smart cities. With growing urban populations and mounting waste challenges, blockchain-based solutions are increasingly being explored to support sustainable, data-driven, and decentralized waste governance.

For instance, Ahmad et al. [19] survey existing blockchain-based frameworks for waste management and identify their ability to offer immutability, traceability, and secure audit trails for tracking waste flows. The authors propose a smart contract-enabled system to automate waste services and ensure legal compliance, transparency in waste treatment, and efficient resource allocation. Blockchain mitigates the weaknesses of centralized waste management platforms, such as single points of failure and data manipulation.

Moreover, Palagan et al. [171] introduce a predictive analytics-based waste collection model integrating IoT, edge computing, and blockchain. Real-time sensor data are analyzed using machine learning to forecast bin status, while blockchain records these data securely and supports smart contract-driven scheduling. This hybrid system achieved 99.25% accuracy in bin prediction and significantly improved operational efficiency and citizen trust through decentralized data access and route optimization.

Another study by Khan and Ahmad [20] presents a blockchain-based e-waste tracking system that monitors electronic device lifecycles from production to disposal. By leveraging IoT and distributed storage systems, the framework records all business processes on blockchain using five smart contracts. This ensures tamper-proof documentation, stakeholder accountability, and secure data exchange, addressing transparency issues prevalent in e-waste supply chains.

From an investment perspective, Kou et al. [172] apply a quantum fuzzy decision model to evaluate blockchain-driven waste management initiatives in smart cities. The authors find that key success factors include waste-to-energy integration, lifecycle optimization, and digital–physical infrastructure convergence, emphasizing the importance of strategic alignment in sustainable waste governance.

Finally, a conceptual paper positions digital value connectivity as essential to advancing municipal recycling [173]. It advocates for integrating blockchain into a programmable urban economy that enables citizen and institutional participation in circular initiatives, reinforcing blockchain’s potential as a socio-technical enabler of urban sustainability.

3.2.7. Blockchain-Based Security and Trust Mechanisms for the Internet of Drones

Topic 6 centers on adopting blockchain into Internet of Drones (IoD) systems, focusing on enhancing security, trust, authentication, and collaboration within drone networks operating in smart city environments. As drones become increasingly critical for urban functions, ranging from logistics and surveillance to environmental monitoring, the security and reliability of their communication and control systems have become paramount.

One key study by Ju et al. [174] proposes a lightweight blockchain-based authentication scheme for multi-server UAV–IoT environments. Addressing vulnerabilities such as impersonation and session key disclosure, Ju et al. [174] introduce a mutual authentication protocol validated through formal methods, including the RoR model and BAN logic. The system maintains low computational overhead and makes it suitable for resource-constrained drone applications in smart cities. Yang et al. [115] analyze the security challenges specific to the IoD domain, including data privacy, radio frequency vulnerabilities, and the need for robust authentication. The authors conclude blockchain’s utility in meeting these security demands, highlighting its advantages in tamper-proof logging and decentralized access control, while advocating for cost-effective, scalable solutions tailored to IoD systems.

Additionally, Rathee et al. [118] present a trust-based mechanism to secure drone communications in ad hoc UAV networks. The approach evaluates each drone’s behavior and interaction history to assign trust scores, which are then recorded on a blockchain ledger. This trust-driven design allows for the detection and isolation of malicious drones, improving network integrity, throughput, and decision-making across smart city drone infrastructures.

Ajakwe et al. [175] explore the convergence of AI, blockchain, and zero-trust architecture to enhance accountability, ownership verification, and delivery authorization. The authors identify a gap between proposed models and real-world implementation, calling for integration of secure, autonomous drone operations into smart mobility systems.

Finally, Liao et al. [117] introduce a software-defined IoD architecture where controllers collaborate securely via a blockchain consortium. A new proof-of-security guarantee (PoSG) consensus mechanism and incentive-based cooperation ensure secure task delegation and privacy-preserving collaboration among multiple drone operators. The results confirm the model’s efficacy in managing environmental monitoring and multi-drone coordination in complex urban settings. Therefore, blockchain has the potential to transform IoD from a vulnerable and siloed system into a secure, trustworthy, and scalable framework that enables the next generation of drone-powered services in smart cities.

3.2.8. Temporal Dynamics of Research on Blockchain and Smart Cities

The temporal evolution of research on blockchain and smart cities reveals a clear maturation of the field (Figure 4), which has transitioned from foundational exploration to specialized applications. Topic 0, Security and Privacy for IoT-Enabled Smart Cities, shows a sharp increase from 2019, peaking in 2023. This surge underscores growing concerns over data integrity and cybersecurity as IoT networks proliferate across smart cities.

Although the publication volume appears to dip in 2025, this may be due to the year being incomplete at the time of the literature search (March 2025), rather than indicating a true decline. The theme of strategic and technological adoption of blockchain has shown steady growth over previous years, reflecting sustained interest in policy development, governance models, and system interoperability. The apparent reduction in 2025 could suggest an ongoing shift from conceptual exploration toward more refined, application-oriented implementations in city planning and digital governance, though this trend should be interpreted cautiously until the year concludes.

Topic 2, focused on Blockchain for Intelligent Transportation, shows consistent development post-2020, aligning with the emergence of smart mobility, digital twins, and secure V2X systems. Similarly, Topic 3, Blockchain for Sustainable Energy and Electric Mobility, peaks in 2024 but tapers in 2025, pointing to either consolidation or a shift toward integrated energy frameworks. Emerging areas like Topic 4 (Smart Healthcare), Topic 5 (Waste Management), and Topic 6 (Internet of Drones) show lower but stable publication numbers.

These topics reflect growing interest in using blockchain for urban resilience, environmental monitoring, and data-driven public health, though limited by technological and regulatory barriers. Overall, the trend analysis suggests a shift from general exploration to sector-specific integration of blockchain, particularly in mobility, energy, and digital infrastructure. This indicates a broader move toward secure, decentralized, and sustainable urban systems, affirming blockchain’s pivotal role in the evolution of smart cities (Figure 5).

3.2.9. Comparative Analysis of Co-Word and BERTopic Findings

This study employs both co-word analysis and BERTopic modeling to explore the thematic structure of blockchain research within the context of smart cities. While each method provides unique insights, a comparative analysis reveals both convergence and divergence in their thematic outputs.

Rooted in bibliometric mapping, co-word analysis identifies high-frequency keyword associations across publications and groups them into semantically cohesive clusters. This approach highlights explicit term co-occurrence and reveals dominant thematic areas within the literature based on author-defined keywords. As detailed in Section 3.1, five key clusters emerged, including topics on technological infrastructure, AI-driven sustainability, decentralized mobility, home-scale energy ecosystems, and urban resilience through healthcare and circular economy systems.

BERTopic, in contrast, applies transformer-based embeddings and unsupervised topic modeling to identify latent thematic patterns based on the full textual context (titles and abstracts). This method yielded seven prominent topics, with emphases on IoT security, strategic adoption frameworks, intelligent transportation, sustainable energy, healthcare systems, waste management, and the Internet of Drones.

Overlap between the two methods is evident in several areas. Both analyses identified security and privacy, mobility, energy management, and healthcare as critical domains of blockchain application in smart cities. For instance, co-word Cluster 1 (“Technological Infrastructure”) aligns with BERTopic’s Topic 0 (“Blockchain-Driven Security and Privacy Solutions”), both focusing on decentralized authentication and IoT protection. Similarly, mobility is central to co-word Cluster 3 and BERTopic Topic 2, reflecting a shared recognition of blockchain’s role in secure, intelligent transportation.

However, the methods diverge in granularity and thematic framing. As such, co-word analysis underscores infrastructure-level integrations and systemic themes, such as urban governance or sustainability frameworks, often revealing broad interdisciplinary connections. In contrast, BERTopic captures more nuanced and domain-specific topics like blockchain’s role in drone networks (Topic 6) or its application in waste management (Topic 5), which did not form distinct clusters in the co-word map but emerged through contextual embeddings in BERTopic.

In summary, the two methods are complementary. Co-word analysis offers a macroscopic view of established thematic areas through term co-occurrence, while BERTopic uncovers deeper and often more emergent or fine-grained topics by analyzing linguistic context. The intersection between the two strengthens this study’s validity, while their divergence highlights the added value of using mixed analytical techniques to map a rapidly evolving research landscape.

4. Conclusions

The intersection of blockchain technology and smart cities reveals a dynamic and multidimensional research field. To explore this nexus comprehensively, we applied co-word analysis and BERTopic modeling, each offering complementary insights into the thematic evolution and technological trajectory of blockchain applications in smart urban ecosystems.

The co-word analysis allowed for the systematic identification of thematic clusters by tracking keyword co-occurrence patterns. These clusters revealed the foundational role of blockchain in enabling secure, decentralized, and interoperable infrastructure across critical smart city components. For instance, Cluster 1 suggests the technological convergence between blockchain, smart contracts, IoT, edge computing, and security protocols. It also emphasizes blockchain’s central role in safeguarding privacy, enabling authentication, and securing data exchange in connected environments. Likewise, Clusters 2 and 3 pointed toward more applied domains such as intelligent transportation systems (ITS), energy trading, and decentralized healthcare, demonstrating the practical embedding of blockchain into everyday urban functions.

On the other hand, BERTopic modeling expanded upon this foundation by capturing the semantic richness and evolution of topic clusters across a ten-year timeline (2016–2025). Topic 0, “Blockchain-Driven Security and Privacy Solutions for IoT-Enabled Smart Cities,” is the most dominant theme, peaking in 2023. This surge reflects heightened awareness around cybersecurity, data privacy, and trust management, especially as smart cities become increasingly reliant on complex and distributed IoT systems. Through its decentralized and tamper-proof structure, blockchain technology is positioned as a trust anchor capable of addressing the vulnerabilities inherent in centralized systems.

Topic 1, “Strategic and Technological Perspectives on Blockchain Adoption,” demonstrates sustained scholarly interest in developing governance frameworks, regulatory mechanisms, and organizational strategies for blockchain deployment. This ensures that blockchain implementation supports public value creation, not just technical efficiency. Notably, a significant portion of the literature in this cluster originates from Europe and East Asia, which represent regions that embrace policy-driven innovation and institutional readiness for blockchain integration [145,176,177,178]. Moreover, the analysis of Topic 2, blockchain-enabled mobility, maps directly onto smart city initiatives aimed at transforming urban transportation through real-time data sharing, autonomous vehicle coordination, and secure V2X communication. Blockchain supports transparency and verifiability in these systems and offers a decentralized approach to managing complex transportation networks, which is a critical component of sustainable and responsive smart cities. In this area, studies are particularly prominent in North America and Western Europe, where urban mobility platforms and pilot projects are more advanced [23,179,180].

Similarly, Topic 3 covers the role of blockchain in sustainable energy management and electric mobility, including applications such as peer-to-peer energy trading, smart grids, and EV charging networks. The decentralized and auditable nature of blockchain allows for trusted and autonomous energy exchange, which supports the development of circular energy flows and strengthens urban energy resilience. Geographically, this topic shows a strong research footprint in countries with active energy transition agendas, including Germany, South Korea, and Australia [181,182]. Thus, blockchain can support circular economy and smart city agendas.

Though less voluminous, Topics 4 through 6 represent emerging and highly specialized application areas: smart healthcare systems, waste management, and the Internet of Drones (IoD). These topics illustrate blockchain’s adaptability across new domains. For instance, the analysis of Topic 4 reveals blockchain’s utility in securing electronic health records and enabling patient-controlled data sharing, while Topic 5 discusses traceable and accountable waste collection systems aligned with circular economy principles. Finally, Topic 6, focusing on drone-based services, clarifies how blockchain can facilitate secure, autonomous aerial operations through smart contract-based coordination and trust management among drone networks. Sector-specific trends are evident across these topics. For instance, Topic 4 draws mainly from healthcare case studies in North America and India [183,184,185], while Topic 5 is driven by sustainability research in the EU [180], and Topic 6 reflects early experimentation in high-tech urban hubs such as Singapore and the UAE [81,119].

In summary, the results of our topic modeling and co-word analysis indicate that the nexus of blockchain and smart cities is evolving toward an integrative, decentralized, and sustainability-driven paradigm. Much like Industry 5.0 [36], blockchain research in smart city contexts increasingly underlines collaboration, resilience, and socio-technical alignment. Blockchain is not only reinforcing data security and operational transparency but also enabling cross-sectoral innovation, from mobility and energy to healthcare and urban governance. The emerging patterns also underscore regional and sectoral priorities, revealing how the adoption of blockchain is shaped by contextual factors such as policy, infrastructure maturity, and societal needs. This signals a future where blockchain serves as both the technological backbone and ethical scaffold of smart, sustainable, and human-centered cities.

This study distinguishes itself from prior literature through its dual-method approach, combining co-word analysis and BERTopic modeling to map the evolution of blockchain in smart cities. While earlier reviews, such as Xie et al. [31], provided a broad overview of challenges like scalability and interoperability, they lacked the granularity to detect emerging subtopics (e.g., blockchain-enabled drone coordination or federated learning for mobility systems). Similarly, Majeed et al. [7] and Treiblmaier et al. [8] explored blockchain’s foundational role in IoT security and smart city governance but relied on conventional literature reviews rather than advanced text-mining techniques.

The methodological innovation yields three key contributions that address gaps in the existing literature. First, the integration of co-word analysis (VOSviewer) with BERTopic modeling represents a novel methodological approach in this research domain, offering superior reproducibility and topic specificity compared to traditional bibliometric reviews [55]. This powerful combination has enabled us to uncover previously underexplored research niches that were either fragmented or completely overlooked in prior works, such as quantum-inspired blockchain for edge security [29] and sophisticated decentralized waste management frameworks [19]. Second, the thematic analysis reveals critical intersections between blockchain and emerging technologies, particularly the integration with digital twins for mobility systems [161] and AI-enhanced privacy preservation [156], which prior studies had not systematically examined. Third, from a practical standpoint, the proposed framework for scalable, cross-sector blockchain adoption (Section 4.1) directly responds to implementation challenges identified in earlier studies [31], providing policymakers with concrete, actionable insights.

The temporal dimension of the analysis further highlights an important evolution in research focus—from theoretical discussions (pre-2020) to sector-specific implementations (post-2022)—that both confirms and extends the findings of Ullah et al. [21] and Sahoo et al. [158]. For instance, while Ahmad et al. [19] made valuable contributions in their focused examination of blockchain for waste management, this study integrates this specialized application into the broader smart city ecosystem, revealing previously unrecognized synergies with energy trading systems and healthcare applications. Perhaps most significantly, the conceptualization of blockchain as both a technological and ethical infrastructure—supporting not just technical solutions but also transparent governance [12] and circular economies [148]—addresses a critical gap in prior studies that tended to treat blockchain primarily as a technical tool rather than a socio-technical enabler of urban transformation.

By bridging these conceptual, methodological, and practical gaps, this study not only synthesizes and organizes previously dispersed knowledge but also charts a clear pathway for future research at the dynamic intersection of blockchain technology, urban sustainability, and smart city innovation.

4.1. Research Limitations

While this study provides a comprehensive overview of the nexus between blockchain technology and smart cities, several limitations must be acknowledged. The co-word analysis, while effective in identifying dominant research themes, may overlook emerging or niche applications of blockchain that are still underrepresented in the literature but hold future potential. Co-word analysis is constrained by its reliance on author-supplied keywords, which may vary in quality and consistency across studies [36]. This introduces a potential bias in term representation and limits the depth of semantic insight. Similarly, BERTopic modeling depends heavily on the quality and scope of the dataset. Thus, any bias in the literature coverage could skew topic identification and thematic emphasis. BERTopic is also sensitive to document length and clarity; shorter or poorly structured abstracts can produce incoherent or noisy topics, reducing the model’s interpretability [36].

Despite being powerful, we also acknowledge that unsupervised topic modeling and keyword co-occurrence techniques may fail to detect low-frequency yet high-impact topics that are just beginning to shape scholarly discourse. Such emerging areas might not generate strong statistical signals in text-mining algorithms but are nonetheless critical for understanding future trajectories in the field. Future research could address this limitation by combining quantitative text mining with expert-guided qualitative analysis to better capture nuanced and evolving topics.

Furthermore, this study’s reliance on peer-reviewed publications may exclude more recent innovations in blockchain-enabled smart city solutions that have not yet been formally documented. Given the fast-paced evolution of both blockchain and urban digital infrastructure, certain cutting-edge developments may not be fully captured. Even though the methodologies reveal key trends, they may simplify the complex interdependencies between blockchain technologies and smart city components, warranting more granular, case-specific investigations in future research.

4.2. Future Research Directions

This study’s findings outline several promising avenues for future research at the intersection of blockchain technology and smart cities, where technological innovation aligns with sustainable and human-centered urban development. First, while the literature increasingly recognizes blockchain’s potential to enhance urban transparency and resilience [9], future work should focus on developing scalable blockchain architectures that integrate seamlessly with smart infrastructure. Specifically, research should explore cross-platform interoperability and energy-efficient consensus mechanisms that enable real-time data sharing across transport, energy, healthcare, and waste systems. Addressing these technical constraints is vital for ensuring blockchain’s long-term viability in resource-constrained urban settings [186].

Second, blockchain’s role in supporting circular urban systems, such as decentralized energy trading, e-waste tracking, and water recycling, merits further investigation [171]. Studies should aim to develop application-specific frameworks that use smart contracts and decentralized ledgers to verify sustainable behaviors, trace resource flows, and enforce compliance with environmental regulations. Pilot projects in sectors like construction, mobility, and housing could provide empirical data to validate blockchain’s effectiveness in advancing zero-waste goals within cities. Third, the integration of blockchain with AI, IoT, and edge computing should be a focal point. Future research should assess how such convergences can power intelligent decision-making in real-time, especially for critical applications like autonomous transportation, predictive maintenance, and emergency response. Emphasis should be placed on the development of trustworthy AI models supported by blockchain to ensure that data provenance and algorithmic integrity are preserved throughout urban operations.

Additionally, researchers should investigate governance frameworks and regulatory models that facilitate ethical blockchain implementation in smart cities [187,188,189]. As blockchain reshapes data ownership, identity management, and service delivery, it is essential to ensure equity, privacy, and public trust. Studies on decentralized governance and citizen-centric blockchain applications can inform future urban policies. Finally, there is a pressing need to establish performance metrics that evaluate the social, environmental, and economic impacts of blockchain-enabled smart city initiatives. Future work should develop and test indicators that measure improvements in transparency, service efficiency, inclusivity, and sustainability. As a result, this helps to align with broader smart city and circular economy goals. By grounding blockchain research in tangible, cross-sectoral outcomes, scholars can better support cities in transitioning toward secure, sustainable, and resilient digital ecosystems.

4.3. Research Implications

This review advances the theoretical and practical understanding of blockchain’s role in smart city development, particularly as it supports secure, decentralized, and sustainable urban systems. The findings accentuate the potential of blockchain to enhance data integrity, transparency, and system interoperability, thereby strengthening the digital foundations of smart cities. From a theoretical perspective, this study expands current discourse by illustrating how blockchain, when integrated with IoT, AI, and edge computing, can foster real-time, autonomous, and resilient city services, including mobility, energy, waste management, and public health. Blockchain is also shown to enable closed-loop urban systems, reinforcing circular economy goals through traceable resource flows and decentralized governance. Blockchain applications such as peer-to-peer energy trading, e-waste tracking, and smart healthcare data management can support trust, accountability, and efficiency in complex urban networks.

From a practical standpoint, this study offers guidance for municipalities and organizations aiming to embed blockchain in urban infrastructure. It stressed the need for interoperable and scalable blockchain frameworks, ethical data governance, and inclusive digital services that promote citizen engagement. The analysis also points to the promises of blockchain in urban resilience and sustainability, aligning with broader smart city objectives and regulatory demands. Ultimately, this research bridges technical innovation with actionable strategies. It considers blockchain as a core enabler of secure, transparent, and regenerative urban ecosystems.