Blockchain Papers

Yu Cui, Prakash J. Singh

No abstract is available for this record.

Hoong Hao Yap, Karel Kruger

No abstract is available for this record.

K. Meenakshi, Kirti Singh, Deepshikha Bhargava

No abstract is available for this record.

Christiane Isenberg

No abstract is available for this record.

Reachsak Ly, Alireza Shojaei, Xinghua Gao, Philip Agee · 5 authors

While traditional AI and data-driven facilities management approaches have improved building operational efficiency, they remain constrained by centralized organizational structures that are vulnerable to cyber attacks, limited contextual understanding, and decision-making processes that exclude key stakeholders from governance. This paper introduces a novel AI- and data-driven distributed governance framework for smart building management that integrates decentralized autonomous organizations (DAOs), digital twins, large language models (LLMs), and blockchain technology. The framework enables transparent collective decision-making through a DAO governance platform, implements data-driven management using IoT and digital twins, incorporates LLM-based virtual assistants for enhanced decision support, and utilizes blockchain for secure building automation. A full-stack decentralized application was developed to facilitate user interaction with these integrated components. The system was evaluated for cost efficiency, scalability, data security, and usability using the System Usability Scale (SUS). Expert interviews were also conducted to assess its practical benefits and implementation challenges.

Lingfeng Guo, Yichen Guo, Tianzuo Zhang, Z. Hong Zhou

This paper explores the integrated application of robotics, blockchain, and software engineering in intelligent warehousing, achieving efficient collaboration and credible management. For software engineering and robotics integration, Model-Driven Architecture (MDA) combined with ROS 2 abstracts warehouse robots' environmental perception and motion control modules into independent models, boosting code reuse by over 40% and keeping system response delay within 50ms. To solve robots' poor path optimization and slow algorithm convergence under multiple constraints, an improved genetic algorithm-based path planning method is proposed. It uses grid method for environment construction, improves traditional genetic algorithm with deletion and smoothing operators plus niche method, and comparative experiments show it outperforms others in path length, smoothness, difficulty, and running time. In blockchain-robotics integration, Hyperledger Fabric-based alliance chain is applied to multi-robot task allocation, defining scheduling priority via smart contracts and relying on PBFT. Tests with 100 nodes show task conflict rate below 0.3 % and financial-grade data security (passing NIST SP 800–210). Software engineering's cross-layer framework addresses integration pain points: bottom-layer lightweight Wasm triples contract efficiency; application-layer Coq verifies protocol non-repudiation. This supports blockchain-driven dynamic allocation with 200 transactions/second throughput and stable end-to-end delay within 200ms, greatly enhancing warehousing efficiency.

Dawood Alalisalem, Hafizur Rahman

Recently, it has been noted that the convergence of blockchain technology presents a promising paradigm for secure, privacy-preserving, and transparent healthcare systems. Moreover, Digital Twins enable real-time replication of patients, hospital operations, and medical devices, and their dependence on continuous sensitive data streams introduces the latest trust and Cybersecurity challenges. A systematic literature review aims to investigate how distributed ledger and blockchain technologies have been applied to secure healthcare digital twins from 2020 to 2025. Furthermore, the review addresses the proposed architecture of blockchain, the security objectives targeted, integration approaches within digital twins, and evaluation methods with limitations. The study follows PRISMA 2020 guidelines. Web of Sciences, IEEE Xplore, PubMed, Scopus, and ACM Digital Library were searched from January 2020 to October 2025 by using defined Boolean queries. Also, the focus of the inclusion criteria is on peer-reviewed studies that discussed blockchain for DT security in healthcare. Data extraction captured blockchain type, metadata, security mechanisms, DT domain, and evaluation methods. From the 487 identified records, only 20 successfully met the inclusion criteria. The fact behind it is that most studies only employed permissioned blockchains like Quorum and Hyperledger integrated with digital twins for monitoring patients, device lifecycle tracking, and data provenance. Some main security objectives include provenance assurance, access control, and integrity. Moreover, only some studies provide formal threat analysis or real-world deployment. Blockchain technology is reliable because it increases digital twin security through immutability, smart-contract-based governance, and decentralized trust. However, interoperability, scalability, and privacy-preserving computation remain the main barriers for clinical adoption.

Bhimanagouda L. Rayanagoudra

The digital economy is rapidly transforming the global landscape by integrating technology, entrepreneurship, and innovation across every sector. Startups have become the key drivers of this transformation, enabling new models of production, finance, and governance. By 2047, the digital economy is expected to evolve into a deeply interconnected system powered by artificial intelligence, blockchain, decentralized finance, and sustainable technologies. These advancements will reshape industries, empower small enterprises, and foster inclusive growth. This paper explores how startups will act as engines of innovation, leveraging digital tools to solve complex social and economic challenges. It highlights emerging trends such as AI-driven decision-making, edge computing, green technologies, and decentralized governance models that will redefine the global business environment. At the same time, the paper acknowledges the challenges of data privacy, cybersecurity, skill development, and environmental sustainability. Through policy analysis and strategic recommendations, the study emphasizes the importance of strong digital infrastructure, ethical data practices, and inclusive innovation ecosystems to ensure balanced growth. By 2047, success in the digital economy will depend not only on technological advancement but also on human creativity, collaboration, and sustainable practices.

Z D Zhang, Jiaming Gu, Rong Li, Wenting Lian · 6 authors

No abstract is available for this record.

Akkaraju Sailesh Chandra, Lakshmi Iyer, Helen Josephine V.L, Nisha Shankar

This research investigates the integration of machine learning algorithms within blockchain-based Decentralized Autonomous Organizations (DAOs) to enhance operational efficiency, resource allocation, decision-making, and governance. While DAOs provide a transparent and trustless mechanism for digital collaboration, they face challenges related to scalability, bias, data privacy, and coordination. We propose a novel framework that leverages supervises learning models for predictive analytics, reinforcement learning for autonomous decision-making, and unsupervised learning for anomaly detection in DAO voting and resource usage patterns. The study also addresses security and privacy risks by incorporating federated learning and homomorphic encryption. Our proposed model demonstrates improved throughput, decision accuracy, and fairness, as evidenced by performance benchmarks against traditional DAO implementations. The findings suggest that machine learning can significantly optimize DAO architecture and contribute to a more scalable, democratic, and intelligent decentralized ecosystem.

Aravindh Sekar, Deb Tech, Cherie Noteboom

The convergence of Artificial Intelligence (AI) and Blockchain Technology (BCT) is transforming supply-chain ecosystems by enhancing transparency, intelligence, and automation. However, existing research lacks a unified theory explaining how these technologies jointly create resilience across organizational levels. This paper extends the Strategic–Decentralized Resilience Theory (SDRT), originally developed to guide effec-tive blockchain implementation, by integrating Agentic AI capabilities to form the SDRT–Agentic AI framework. The framework conceptualizes how predictive, adaptive, and agentic (autonomous) AI capabilities reinforce SDRT’s three pillars: Strategic, Or-ganizational, and Decentralized Resilience. The framework draws on three AI modali-ties—predictive AI for strategic foresight and agility, adaptive AI for organizational learning and flexibility, and agentic AI for self-governed, trustless coordination within blockchain ecosystems. Together, these mechanisms explain how intelligent and de-centralized systems co-evolve to generate dynamic, multi-level resilience. This con-ceptual paper develops a comprehensive model and propositions describing interac-tions between AI capabilities and blockchain-based organizational structures. It con-tributes to information systems and supply-chain research by unifying two fragmented domains, AI and blockchain, under a resilience-oriented mid-range theory. Practically, the framework provides managers with a roadmap to align AI investments with de-centralized governance mechanisms, enabling proactive decision-making, adaptability, and sustainable competitiveness in increasingly autonomous digital environments.

Lakshmi Sirisha Veturi, Rahamatunnisa Shaik, Mukesh Chinta

The rapid growth of Industry 4.0 has transformed traditional manufacturing systems into connected cyber-physical environments. These systems continuously produce large amounts of operational and sensor data, which are vital for predictive maintenance and informed decision-making. However, the reliability, integrity, and security of this data remain significant challenges. This paper introduces a blockchain-based industrial monitoring framework that integrates Artificial Intelligence (AI) for predictive analytics with blockchain for decentralized and tamper-proof data management. The framework enhances transparency, traceability, and secure collaboration among stake-holders by maintaining verified records of equipment health and maintenance activities. Smart contracts also automate fault alerts, compliance checks, and maintenance scheduling, removing the need for centralized authorities. Experimental results on industrial datasets demonstrate better data integrity, lower risk of cyber threats, and improved predictive accuracy. The proposed hybrid architecture offers a scalable, auditable, and secure foundation for next-generation industrial systems.

Alexander Samuels

Background Global supply chains are increasingly challenged by disruptions, environmental pressures, and evolving market demands, necessitating a strong digital transformation. This study explores how the integration of Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) is revolutionizing supply chain management (SCM) by improving operational efficiency, transparency, resilience, and sustainability. Methods Adhering to the PRISMA framework, a systematic review of literature published between 2010 and 2024 was undertaken. Comprehensive searches were conducted in Scopus database. The collected literature was rigorously screened and analyzed using Atlas-ti software to identify recurring themes and assess the synergistic impact of AI, Blockchain, and IoT on supply chain operations. Results The review reveals that digital transformation significantly improves SCM through improved demand forecasting, optimized inventory management, and real-time decision-making capabilities. AI provides predictive insights that mitigate risks and streamline processes, Blockchain offers secure, transparent, and immutable records that improve trust and traceability, and IoT enables real-time monitoring and connectivity across the supply chain network. Despite these benefits, challenges remain, including cybersecurity vulnerabilities, interoperability with legacy systems, and the need for workforce upskilling. Conclusion The integration of AI, Blockchain, and IoT into SCM presents a compelling pathway toward creating more resilient and sustainable supply chains. The paper offers a comprehensive analysis of the benefits and challenges associated with these digital technologies and provides strategic recommendations for practitioners and policymakers to encourage a balanced, technology-driven, and sustainable supply chain ecosystem. JEL codes O33, M11, M15

Xuling Ye, Xingyu Tao, Jack C.P. Cheng, Markus König

No abstract is available for this record.

Tanya Zhelezniak, Ilan Alon

Abstract The metaverse is transforming many industries, customer behavior, and companies’ strategies. However, there is a lack of research and understanding of how to adjust existing approaches and solutions. Moreover, based on numerous studies, there is a strong connection between the metaverse and virtual technologies in general and non-fungible tokens (NFTs). The authors’ research aims to address this gap by investigating the reasons for this connection. The authors suggest that NFTs complement metaverse adoption and might be a key component for its business and marketing model developments. In addition, the authors provide specific guidance for NFT implementations in different industries. The authors’ contribution extends the knowledge of this nascent field and might be valuable for scholars and practitioners.

Ravi Chandra Gurung, Santosh Dhungana

The deployment of cyber-physical systems (CPS) in smart manufacturing has advanced industrial processes via live data analytics, automation, and intelligent decision-making. However, these interrelated systems can no longer avoid critical issues surrounding data trust, integrity, and security across heterogeneous devices and networks. The issue is to facilitate a secure, transparent, and efficient method of performing consensus in a distributed CPS environment while keeping performance and resilience in the face of cyber-attacks. This paper outlines a Cognitive Blockchain Consensus Algorithm (CBCA) for dealing with these concerns, that integrates the decentralized ledger property of blockchain with cognitive computing principles. CBCA will apply a adaptive learning model to determine consensus parameters based on real-time information in order to minimize computation overhead to reach consensus, and latency, while being able to detect threats and respond to anomalous behavior. The cognitive layer observes behavior across the network continually and makes autonomous changes to the consensus mechanism on behalf of CPS, optimizing the trust vs security tradeoff. Results from experimental simulations conducted using a smart manufacturing testbed show CBCA increasing throughput transactions by 23%, reducing consensus delay by 17%, and malicious node detection accuracy by 96% when compared to traditional Proof of Work and Proof of Stake methods.

Vadym Derkach

As global pressure increases for sustainable and transparent supply chains, logistics organisations are exploring ways to strengthen environmental, social and governance (ESG) performance. This article examines how artificial intelligence (AI) and distributed ledger technologies (DLT) contribute to ESG integration in logistics. The study applies a qualitative desk research approach based on secondary data from 2017–2025, including sustainability reports, port authority publications, and the industry press. The comparative case analysis covers four Baltic logistics actors: the Port of Klaipėda (Lithuania), Vlantana (Lithuania), the Freeport of Riga/ Baltic Container Terminal (Latvia), and HHLA TK Estonia (Estonia). The findings show that Klaipėda’s LNG, OPS, and hydrogen projects enhance environmental outcomes; the Vlantana Norge case exposes social and governance compliance risks; and Riga and Tallinn demonstrate governance-oriented digitalisation through 5G networks and blockchain documentation. AI primarily supports efficiency and risk detection, while DLT secures the transparency and auditability of ESG data. Together, they function as complementary enablers of ESG reporting, though broader adoption requires regulatory alignment, interoperability, and investment in the digital infrastructure.

Khira Belgada, Laila El Abbadi

In the context of Industry 4.0, industrial firms are encountering new challenges related to data management, flow traceability, security and process transparency. Blockchain, as a distributed ledger technology, offers innovative solutions to meet these challenges. This study proposes a systematic literature review (SLR) on the recent contributions of blockchain in industrial environments. A total of 20 scientific articles, published over the last ten years, were analyzed to better understand how this technology is being integrated into production processes and supply chains. The analysis identified four major areas in which blockchain is being mobilized: traceability of production processes, transparency of supply chains, integration into digital industrial systems, and its role in decision support. The results show that blockchain enables reliable, real-time monitoring of industrial operations, particularly when coupled with technologies such as IoT, smart contracts or event-driven databases. It also promotes better coordination between players, reinforces trust, and facilitates audits in complex or multi-actor environments. However, despite its potential, several limitations remain. Barriers related to scalability, implementation costs, system interoperability and the integration of manual tasks still limit its widespread adoption. Furthermore, in many cases, blockchain is treated as a secondary technology, reducing the depth of analysis available. This review offers a structured vision of the contributions and limitations of blockchain in industry while identifying future research prospects, particularly around hybrid models and concrete implementation cases.

Perboli Guido, Simionato Nadia, Vandoni Chiara

This paper investigates how Web3 technologies, such as blockchain, NFTs, and the metaverse, can drive Business Model Innovation (BMI) by enabling new forms of value creation, delivery, and capture. While the strategic potential of Web3 has been widely discussed, there remains a lack of operational tools to guide its implementation in real-world business contexts. To address this gap, we introduce the Web3 Value Exploitation De sign Model (Web3 VEDM), a step-by-step framework grounded in the GUEST methodology. The model is designed to support engineering managers in assessing Web3 readiness, aligning stakeholders, and developing decentralized business models. The framework is empirically validated through a real-world case study in the agri-food sector, offering actionable insights into how organizations can leverage Web3 to transition from centralized to decentralized, participatory ecosystems. The study contributes both theoretically and practically by bridging the gap between conceptual exploration and structured application of Web3 in business transformation.

Ammar Hummieda, Karim Moawad, Khaled Salah, Mohammed Omar · 5 authors

Purpose Cement manufacturing is a vital yet emission-intensive industry that faces challenges in certifying sustainable production practices, driven by the need for transparency, accountability and regulatory compliance. This paper provides a blockchain-based certification framework to enhance traceability and sustainability in cement production. Design/methodology/approach Leveraging Ethereum smart contracts (SCs) and blockchain technology, our solution ensures decentralized, immutable tracking of emissions data, production processes and compliance through secure interactions among regulators, manufacturers and auditors. The framework facilitates deployment, registration, reporting, auditing and certification. Detailed insights into system architecture, algorithms, SC implementation and validation are provided. Security analysis evaluates access controls, data privacy and vulnerability mitigation, while cost analysis highlights the framework's economic feasibility by examining gas costs for key functions. Findings The blockchain-based framework successfully produced a scalable solution with a modular design to allow for flexible deployment by different regulatory bodies. Transparency was achieved through blockchain events announcement, and accuracy was ensured through periodic auditing rounds. Security analysis results show no serious vulnerabilities. The developed framework proves a cost-effective solution for certifying sustainable production practices in the cement industry, with potential applications across other heavy manufacturing sectors. Research limitations/implications Cement manufacturing is a vital yet emission-intensive industry that faces significant challenges in certifying sustainable production practices, driven by the need for transparency, accountability and regulatory compliance. Practical implications We believe that this paper provides the following practical implications: Innovative Framework: A blockchain-based certification system promoting adherence to sustainable processes in cement manufacturing. SC Implementation: Detailed insights into the system architecture, implementation and validation of algorithms and SCs. Comprehensive Analysis: A thorough security analysis of SC coding and a cost analysis highlighting the economic feasibility of our proposed framework. Social implications The proposed methodology will help all stakeholder involved in the cement production and supply chain have a better understanding and a robust tool to observe and learn about operations, tasks and other activities made through sustainable cement production Originality/value The study contributes a novel blockchain-based method to certify sustainable production in energy-intensive industries, especially cement, offering a valuable tool that ensures transparency and immutability.

Nripanka Das

As global energy systems pivot toward net-zero, Carbon Capture, Utilization and Storage (CCUS) has emerged as a foundational tool to mitigate industrial emissions (Abu Zahra et al. 2007; Boot-Handford et al. 2014; IEA 2020). However, the effectiveness, financeability and scalability of CCUS projects are severely hindered by traditional Monitoring, Reporting and Verification (MRV) workflows (IEAGHG 2017; U.S. DOE 2018–2023; Verra 2023). These legacy processes are typified by siloed data collection, infrequent audits, manual record reconciliation and a lack of trust between stakeholders. This results in substantial verification delays, compliance risks and a sluggish, opaque carbon market where the link between physical decarbonization and financial value is weak (ISO 2018; Dutta et al. 2021; Hartmann et al. 2023). Although real-time IoT sensing, AI-powered analytics and blockchain-based recordkeeping have each been explored independently (Garcia Freites and Jones 2021; Wang et al. 2019; Hartmann et al. 2023), no prior framework has fused these into a seamless, robust and self-governing digital pipeline for CCUS MRV and carbon-credit management at industrial scale (Hütten et al. 2022). This paper introduces a novel architecture that: Deploys dense, high-fidelity IoT sensor networks and edge-compute nodes across the full CCUS value chain (Abu Zahra et al. 2007; IEAGHG 2017; Dutta et al. 2021);Leverages advanced, multi-tiered AI/ML for operational intelligence, predictive reliability and financial optimization (Breiman 2001; Hochreiter and Schmidhuber 1997; Chen et al. 2019; Bender et al. 2021);Employs a hybrid off-chain/on-chain data model for performance, privacy and regulatory-grade auditability (Garcia Freites and Jones 2021; Boneh and Shoup 2020; NIST 2024);Harnesses Layer-2 blockchain with decentralized oracles and smart contracts to automate MRV validation, progressive credit minting, escrow and transparent marketplace integration (Buterin et al. 2014; zkSync 2024; Bai et al. 2021; KlimaDAO 2024).

Ningshuang Zeng, Xuling Ye, Shiqi Chen, Yan Liu · 5 authors

Although existing models and theories have explained systemic behaviors such as demand amplification and disruption propagation, practical challenges in Modular Construction Supply Chains (MCSC) remain unresolved due to production heterogeneity, geographic dispersion, and conflicting stakeholder interests. In addition, the lack of digital infrastructure and process-level data integration continues to hinder the development of automation and intelligent decision-making. To address these issues, this study develops an MCSC coordination system informed by industrial input. The system features a novel dual-engine architecture that integrates blockchain-enabled smart contracts and Robotic Process Automation (RPA). It also incorporates a practice-oriented approach to MCSC Supply Batch (MSB)-based management, using industrial insights to define the MSB as the fundamental coordination unit in process execution. The automatic triggering mechanism enabled by MSBs and dual-engine enables task-to-task transitions while maintaining traceability and operational clarity across supply chain nodes. A real-world case study validates the effectiveness of the proposed system in enhancing traceability, automation, and stakeholder collaboration within MCSC environments.

Kavita Kanabar, Rajesh Dey, Rupali Mahajan, Pratibha Vijay Jadhav · 6 authors

No abstract is available for this record.

Dun Li, Dezhi Han, Noël Crespi, Roberto Minerva · 8 authors

Digital twin (DT) technology integrates Internet of Things (IoT), communication networks, and sensor systems through high-fidelity modeling and multi-dimensional simulation, enabling dynamic mapping and real-time optimization of physical objects. However, DT development still faces several challenges, including cross-platform interoperability limitations, excessive latency in real-time scenarios, security vulnerabilities in distributed deployments, and the complexity of accurately modeling multi-modal systems. Blockchain (BC) enhances the security and functional scope of DTs across diverse applications. This survey begins by introducing the core principles of BC and DT, and then investigates the rationale and benefits behind their integration. From a data-centric perspective, we explore how Blockchain-empowered Digital Twins (BCDTs) enhance data storage, secure exchange, privacy protection, and system interoperability. The survey further explores the architecture of BCDT systems, covering network topology, functional modules, platform design, and representative prototypes, offering insights into real-world applications. In addition, we survey how BCDT supports the convergence of key Industry 4.0 technologies, including the Internet of Things, vehicle networks, unmanned aerial systems, artificial intelligence, federated learning, 5G mobile networks, and software-defined networking. Industrial-grade quality BCDT-supported applications are highlighted, providing a solid foundation for further research. Finally, we analyze the challenges faced by BCDT and offer some optimistic suggestions for further research in the field of BCDT.