Odunayo Akindotei, Igba Emmanuel, Babatunde Olusola Awotiwon, Adah Otakwu
Blockchain technology has garnered significant attention for its potential to revolutionize critical systems by enhancing transparency, efficiency, and data security. This review examines the integration of blockchain in three essential domains: Agile Project Management, Decentralized Finance (DeFi), and Cold Chain Management. By leveraging decentralized ledgers and smart contracts, blockchain provides a robust framework for real-time tracking, data integrity, and automated compliance, addressing long-standing challenges across these sectors. In Agile Project Management, blockchain fosters seamless collaboration and transparent decision-making, minimizing bottlenecks and improving accountability. In DeFi, blockchain strengthens security for digital transactions and identity verification while offering financial autonomy and mitigating fraud risks. Within Cold Chain Management, blockchain ensures traceability, reduces data tampering risks, and enhances visibility throughout supply chain processes, safeguarding temperature-sensitive goods. This paper evaluates existing blockchain-based applications and frameworks, identifies current limitations, and discusses future opportunities for optimizing critical systems through blockchain. The findings highlight blockchain's transformative role in driving operational efficiency, security, and data transparency across diverse applications, providing a roadmap for industries to harness its full potential in critical environments.
This chapter outlines diverse AI tools for philanthropic operations, substantiating the discussion with other technological breakthroughs such as blockchain and Decentralized Autonomous Organizations (DAOs) for improved governance. A comprehensive overview of how AI is revolutionizing fundraising, outreach, engagement, and Human Resources (HR) is provided, presenting real-world examples of AI tools in enhancing such internal and external operations. The chapter emphasizes AI’s ability to enhance organizational growth and innovation through automation and efficiency. Furthermore, it explores how blockchain and DAOs can revolutionize philanthropic governance by improving efficiency, grantmaking strategies, and promoting accountability and transparency. It then touches on the joint role of AI and philanthropy in advancing the Sustainable Development Goals (SDGs), addressing global challenges with enhanced innovation. Through case studies and tool examples, this chapter underscores the significant impact of AI, DAOs, and blockchain in reshaping operational landscapes and advancing social good initiatives with transparency, efficiency, and innovation.
Smart contract-based applications are executed in a blockchain environment, and they cannot directly access data from external systems, which is required for the service provision of these applications. Instead, smart contracts use agents known as blockchain oracles to collect and provide data feeds to the contracts. The functionality and compatibility with smart contract applications need to be considered when selecting the best-fit oracle platform. As the number of oracle alternatives and their features increases, the decision-making process becomes increasingly complex. Selecting the wrong or sub-optimal oracle is costly and may lead to severe security risks. This article provides a decision support model for the oracle selection problem. The model supports smart contract decision-makers in selecting a secure, cost-effective, and feasible oracle platform for their applications. We interviewed oracle co-founders and smart contracts experts to refine and validate the decision model. Two real-world smart contract application case studies were used to evaluate the model. Our model prioritises and suggests more than one possible oracle platform based on the developer’s required criteria, security assessment and cost analysis. Moreover, this guided decision model serves to reveal issues that may go unnoticed if done haphazardly, reduce decision-making efforts and provide a cost-effective solution.
Richard Hobeck, Christopher Klinkmüller, H. M. N. Dilum Bandara, Ingo Weber · 5 authors
Abstract Blockchain technology is known for its transparency properties due to its publicly available, immutable data. Yet, as data availability does not inherently ensure transparency, further analytical methods may be required for human interpretation of data traces. Process mining has emerged as a popular toolbox for understanding processes and how they are executed in practice. The paper studies process mining as a method to enhance the transparency of blockchain data. To this end, two popular Ethereum applications were analyzed using process mining: the prediction and betting marketplace Augur and the network marketing platform Forsage . Observations from the process-mining analyses are used to discuss if process mining can serve as a method to establish transparency of a blockchain. For both applications, new insights are generated for usage scenarios such as application redesign, security analysis, user behavior analysis, and revealing blind spots in Augur’s and Forsage’s documentation. The paper concludes that there is evidence that process mining can serve as a method to enhance transparency in blockchains at the cost of technical setup and knowledge acquisition.
Ruchi Arora, Meera Kapoor, Nidhi Singh, Muhammad Zafar Yaqub
Abstract This study critically evaluates the extant research on green cryptocurrency (GC). It incorporates the systematic literature review (SLR) approach of research executed through the analysis and compilation of 54 relevant studies. The focus is on identifying and interpreting the thematic foci and existing gaps about GC to inform potential areas of future research. The SLR findings aggregated the research around key themes: interconnectedness, portfolio diversification, environmental impact, green blockchain, behavioral impact, and regulatory policy. Additionally, the study develops the research profile of the selected studies in terms of data analysis methodology, research findings, variables investigated, and critical knowledge gaps in the literature corpus. The study proffers an integrated framework and actionable inferences for supporting the merits of novel research and regulatory project applications in GC. It is crucial for understanding and advancing research in the GC domain, which currently has scant academic literature and efforts toward sustainable investment.
In the pursuit of sustainable supply chains, the importance of data interoperability and fusion has become increasingly evident. Distributed Ledger Technologies (DLT) have emerged as a transformative solution, enabling enhanced data sharing, transparency, and accountability among diverse stakeholders. This review explores the role of DLT in improving data interoperability within sustainable supply chains, addressing key challenges and opportunities that arise from its implementation. The first section provides an overview of sustainable supply chains, highlighting the necessity for effective data interoperability to achieve operational efficiency and meet sustainability goals. The challenges related to data fragmentation, disparate formats, and security concerns are discussed, emphasizing the need for a cohesive approach to data management. Next, the review delves into the core principles of DLT, including decentralization, immutability, and consensus mechanisms. It outlines how these principles facilitate the development of standardized data formats and promote secure, transparent data sharing among supply chain participants. The integration of DLT with legacy systems and its capacity to enhance cross-border data exchange are also examined, showcasing how DLT can bridge existing gaps in data interoperability. Moreover, the benefits of DLT in sustainable supply chains are explored, including enhanced traceability, increased efficiency, better compliance with sustainability standards, and the establishment of trust among stakeholders. Real-world case studies from sectors such as food, textiles, and energy illustrate successful implementations of DLT and the resultant improvements in sustainability outcomes. The review also discusses future directions and innovations in the application of DLT. The potential integration of artificial intelligence (AI) for predictive analytics and decision-making, as well as the incorporation of Internet of Things (IoT) devices for real-time data capture, are highlighted as pivotal developments that can further enhance data interoperability. This review underscores the critical role of DLT in fostering data interoperability and fusion within sustainable supply chains. It calls for collaborative efforts among stakeholders to harness the full potential of DLT, paving the way for more resilient, efficient, and sustainable supply chain systems in the future. Through this exploration, the review aims to contribute to the ongoing discourse on the intersection of technology and sustainability in supply chain management. Keywords: Ledger Technologies, Data Interoperability, Supply Chains, Review.
Tarek Zaarour, Ahmed Khalid, Preeja Pradeep, Ahmed H. Zahran
Knowledge graphs have proven vital for efficient data management, enhanced search capabilities, and improved decision-making in various information technology domains. However, constructing reliable knowledge graphs in decentralized ecosystems, with distributed autonomous actors, poses significant challenges related to asynchronous transmission, out-of-order knowledge-sharing, device heterogeneity, and trust issues. These challenges are also present in resource orchestration within multi-cloud edge ecosystems where multiple stakeholders must collaborate and share information to enable next-gen smart applications. In this paper, we propose a novel system design that utilizes Distributed Ledger Technology to build knowledge graphs. This approach ensures consistent and trustworthy knowledge sharing among orchestrators in a cloud-edge continuum. Our solution accommodates diverse requirements of both cloud and edge servers, allowing clients to construct complete historic graphs or build filtered sub-graphs. We deploy our solution in a multi-cloud edge environment and construct knowledge graphs representing the system state, including clusters, servers, microservices, and various resources. We validate the feasibility and performance of our solution through a real-world deployment and experiments in a smart shopping use case. Results demonstrate that the proposed solution achieves the claimed benefits with minimal or acceptable delays in comparison to traditional event streaming services.
Monks, CA, USA, Shafeeq Ur Rahaman, P. Sudheer, Samsung, CA, USA · 5 authors
The rapidly evolving landscape of cryptocurrency markets presents unique challenges and opportunities. The significant daily variations in cryptocurrency exchange rates lead to substantial risks associated with investments in crypto assets. This study aims to forecast the prices of cryptocurrencies using advanced machine learning models. Among seven models that were tested for their prediction and validation efficiency, Neutral Networks performed the best with minimum error. Thus, Long Short-Term Memory (LSTM) neural networks were used for predicting future trends. LSTM model is well-suited for analyzing complex dependencies in financial data. Starting with historical data collection, data preprocessing, feature engineering, normalization and integrative binning, a comprehensive Exploratory Data Analysis (EDA) was conducted on 50 cryptocurrencies. Top performers were identified based on criteria such as trading volume, market capitalization, and price trends. The LSTM model was implemented using Python to predict 90-day price movements data to check intricate patterns and relationships. Model performance was validated by performance metrics such as MAE and RMSE. The findings align with the Adaptive Market Hypothesis (AMH) which suggests that cryptocurrency markets exhibit dynamic efficiency influenced by evolving market conditions and investor behavior. The study shows the potential of machine learning models in financial economics and their role in enhancing risk management strategies and investment decision-making processes.
The increasing complexity of data management systems, coupled with the evolving nature of cybersecurity threats, necessitates innovative approaches to ensure data integrity, confidentiality, and availability. This paper explores recent studies on advanced data management strategies and their intersection with cybersecurity practices. Key insights are drawn from the latest research on topics such as distributed ledger technologies, artificial intelligence-driven threat detection, and privacy-preserving data management frameworks. The analysis highlights how these emerging technologies are reshaping the landscape of data management while addressing cybersecurity challenges. Additionally, this paper examines the role of regulation and policy in fostering secure data ecosystems. The findings offer a comprehensive overview of current trends, challenges, and opportunities in the field, with recommendations for future research directions.
Ideas about blockchain-based applications are soaring, while the realization of existing enterprise architecture landscapes is more cumbersome. This is also valid for applications based on track & trace blockchains. The main impediment to implementing smart contracts with a subsequent financial flow from one blockchain peer to another is trust. Trust issues are prevalent in many business areas. One of the most effective countermeasures is a demonstration of the power of blockchain information systems. By showing the actors of a supply chain how their nodes in a common blockchain are interacting, suspiciousness is traded for knowledge. For this reason, an example of a realistic blockchain application is discussed in detail. The main components are a widely used SAP Business Technology Platform and Hyperledger Firefly. Since multiple layers are part of such an instance, the focus will be on the Hyperledger side. This study presents a notable advancement in the realm of blockchain-enabled track & trace solutions through the use of a novel system architecture for its end-to-end implementation. Its architecture achieves crucial integrations between web3 technologies and established enterprise systems. It offers new functionalities, such as AI integration, while simultaneously acknowledging the inherent challenges and charting potential trajectories for future research. Received: 9 May 2024 | Revised: 21 June 2024 | Accepted: 29 July 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in SAP at https://help.sap.com/docs/SAP_S4HANA_ON-PREMISE/671fe27096664c2c82ac6d2cabb38dea/74c6b169bfb74953b07a66aa248cd9aa.html?locale=en-US and https://help.sap.com/docs/SAP_S4HANA_ON-PREMISE/671fe27096664c2c82ac6d2cabb38dea/02b0ed279b5b430f9f48ad41c7e37f4f.html?locale=en-US. Author Contribution Statement Markus Rabe: Conceptualization, Validation, Writing - original draft, Writing - review & editing, Supervision. Henrik Rainer Körsgen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration.
This paper explores the factors that facilitate and impede blockchain technology adoption (BTA) within business organizations. Analysing 112 scholarly articles via thematic and bibliometric methods reveals a complex adoption landscape, highlighted by the proposition of an innovative conceptual framework that augments the conventional technology-organization-environment (TOE) framework. This augmented framework integrates enabling factors, such as enhanced security mechanisms via smart contracts, and barriers, including scalability challenges and regulatory limitations. The research offers significant insights into cultivating a conducive ecosystem for BTA within organizations and provides practical implications.
This paper presents a comprehensive analysis of the historical progression, current trends, and prospects of Big Data. It explores the technological advancements that have established Big Data as a critical element of contemporary analytics, its extensive impact across various sectors, and the ethical challenges it poses. Beginning with the early recognition of Big Data's potential in the 2000s, the paper traces the development of foundational technologies such as Hadoop and the subsequent diversification of tools and methods. It delves into the integration of advanced analytics and machine learning, the rise of cloud-based Big Data services, and the transformative effects on sectors including healthcare, finance, agriculture, and education. The study also examines ethical considerations such as privacy, bias, transparency, and regulatory compliance, emphasizing the need for robust governance frameworks. It investigates the potential of emerging technologies like AI, IoT, and quantum computing to enhance Big Data capabilities further. It highlights future directions, including decentralized data ecosystems, advanced analytical techniques, and enhanced data privacy measures. By providing a panoramic view of Big Data's development, this paper aims to showcase its potential to revolutionize decision-making processes, improve operational efficiency, and drive innovation across industries; it underscores the importance of balancing technological innovation with ethical responsibility to ensure positive societal advancement and global progress. To add a novelty to the discussion, an AI agent Big D was created to provide a relevant analysis of trends in Big Data. The agent uses a multimodal ChatGPT-4o Large Language Model (LLM) from OpenAI and provides its review based on uploaded files and LLM knowledge.
Blockchain technology (BC) and big data analytics capability (BDAC) are two crucial emerging technologies that have attracted significant attention from businesses and academia. However, their combined effect on business model innovation (BMI), along with the moderating role of environmental uncertainty and the mediating influence of corporate entrepreneurship, remains underexplored. To fill this gap, the present study investigates the combined effects of BDAC and blockchain adoption on BMI and explores the mediating role of corporate entrepreneurship as well as the moderating effect of environmental uncertainty. Drawing on the dynamic capability view (DCV) and the related literature, this study investigates these relationships using a conceptual framework hypothesising that (1) BDAC and blockchain adoption affect BMI through corporate entrepreneurship and (2) environmental uncertainty moderates these relationships. Consistent with the main theoretical arguments, our results, based on a sample of 284 employees working in Australian firms, indicate direct and indirect impacts of both BDAC and blockchain adoption on BMI. Corporate entrepreneurship was found to play a partial mediating role in the relationship between the two technologies, while BMI and environmental uncertainty were found to be significant moderators. These findings have significant theoretical and practical implications for companies striving to innovate their BMI. The results suggest that the synergistic effects of BDAC and blockchain technologies together create entrepreneurial activities and strategies to generate value, thus enabling BMI. Furthermore, the mediating role of corporate entrepreneurship and the moderating effect of environmental uncertainty have important theoretical implications for innovative BMI and management. As such, this study highlights the potential of BDAC and blockchain technologies to drive sustainable business practices, offering insights into how these technologies can contribute to economic, social, and environmental sustainability through innovative business models.
Gigi Yong, Sherene Tyng Xin Saw, Jhen Nee Tang, Teng Li · 6 authors
In the domain of Industrial Internet of Things (IIoT), the demand for robust and secure methods for goods tracking and management has become growingly critical. Conventional methods face significant challenges, including authentication, computational overhead, cyber security, and data integrity. To address these issues, this paper proposes a block-chain based system for goods management and tracking with enhanced authentication mechanism by leveraging the decentralized nature of block-chain technology and integrating Elliptic Curve Digital Signature Algorithm (ECDSA) with Elliptic Curve Cryptography-Zero Knowledge Proof (ECC-ZKP). The proposed solution aims to ensure the authenticity and the integrity for all the transaction while providing high level privacy-preserving verification without revealing information. The research in this paper demonstrates that the proposed block-chain-based system significantly enhances security performance, key management and operational efficiency, addressing the existing challenges in IIoT goods tracking and management, providing a resilient framework for more secure industrial operations in managing goods.
Supply chain management (SCM) plays an important role in organizations by creating efficiency and cost advantages in operating supply chain activities across industries. It will also be important to know that traditional SCM systems have various inherent problems, including not having enough information about the system, complicated tracking of products, and the fact that fraudulent activities easily compromise most systems. The modern solution for SCM seems to be based on blockchain technology that provides an operational walls-built ledger, significantly increasing transparency and traceability rates within the supply chain processes. Consequently, this paper considers the role of SCM in incorporating blockchain technology to address potential issues such as scalability and conformity to legal frameworks. Examples from Walmart's food supply chain management and Maersk and IBM's TradeLens give a concrete realization of blockchain's benefits in supply chain clarity and functioning. Future trends of blockchain for SCM, particularly multichain and integration with other emerging technologies, reveal that the field is set to expand toward offering increased resolution and reliability to the supply chain.
Blockchain technology has emerged as a promising solution to address persistent challenges like lack of transparency, inefficient tracking, and counterfeiting issues in pharmaceutical supply chains. However, there are still limitations in integrating blockchain with existing legacy systems, lack of common standards, scalability concerns due to its decentralized architecture, and lack of regulatory clarity that need to be addressed before successful implementation in the pharmaceutical industry is feasible. This review explores the potential benefits and current use cases of blockchain in enabling drug traceability, establishing authenticity and integrity of data, facilitating smart contracts for seamless financial transactions, and optimizing overall supply chain operations. The current blockchain-based solutions employed by various stakeholders like drug manufacturers, wholesalers, distributors, retailers, hospitals, and regulators across the different stages of the pharmaceutical value chain are reviewed in this article. Recent research efforts to mitigate the key challenges are discussed, including technical improvements in blockchain architecture for increased security and privacy, the use of permissioned blockchains, innovative consensus protocols to improve scalability, and growing regulatory guidance. The future outlook highlights the tremendous opportunities for blockchain to enable end-to-end visibility, accountability, automation, real-time monitoring and combating counterfeiting in pharmaceutical supply chains through collaborative efforts among stakeholders.
Supply chain data management faces challenges in traceability, transparency, and trust. These issues stem from data silos and communication barriers. This research introduces DID-Chain, a framework leveraging blockchain technology, Decentralized Identifiers, and the InterPlanetary File System. DIDChain improves supply chain data management. To address privacy concerns, DIDChain employs a hybrid blockchain architecture that combines public blockchain transparency with the control of private systems. Our hybrid approach preserves the authenticity and reliability of supply chain events. It also respects the data privacy requirements of the participants in the supply chain. Central to DIDChain is the cheqd infrastructure. The cheqd infrastructure enables digital tracing of asset events, such as an asset moving from the milk-producing dairy farm to the cheese manufacturer. In this research, assets are raw materials and products. The cheqd infrastructure ensures the traceability and reliability of assets in the management of supply chain data. Our contribution to blockchain-enabled supply chain systems demonstrates the robustness of DIDChain. Integrating blockchain technology through DIDChain offers a solution to data silos and communication barriers. With DIDChain, we propose a framework to transform the supply chain infrastructure across industries.
Data offers companies the potential for a future competitive advantage as a strategic resource. Companies own a wide variety of data, from customer data, transaction data to supply chain data. To date, most companies exlusively rely on their own data. However, through efficient data sharing, companies can extend their databases and improve the quality of the data. In doing so, they jointly enhance the quality of data-driven decision-making, derive better business ideas, and improve their internal processes. To share their data, companies can decide between two options: Sharing data on established centralized databases on digital platforms or exploring decentralized distributed ledger technologies like blockchain. These two differ not only in their architecture (centralized/decentralized), but also in data privacy, access control, data security, and governance. This dissertation contains seven scientific articles that emphasize the strategic importance of data sharing. In particular, scientific and practical decision-makers can learn about the benefits and risks of data sharing and can base their willingness to share data on a more comprehensive knowledge foundation.
Valerio Mandarino, Giuseppe Pappalardo, Emiliano Tramontana
Blockchain technology offers unique features, such as transparency, the immutability of data, and the capacity to establish trust without a central authority. Such characteristics can be leveraged to support the collaboration among several different software systems operating within the healthcare ecosystem, while ensuring data integrity and make electronic health records (EHRs) more easily accessible. To provide a solution based on blockchain technology, this paper has evaluated the main issues that arise when large amounts of data are expected, i.e., mainly cost and performance. A balanced approach that maximizes the benefits and mitigates the constraints of the blockchain has been designed. The proposed decentralized application (dApp) architecture employs a hybrid storage strategy that involves storing medical records locally, on users’ devices, while utilizing blockchain to manage an index of these data. The dApp clients facilitate interactions among participants, leveraging a smart contract to enable patients to set authorization policies, thereby ensuring that only designated healthcare providers and authorized entities have access to specific medical records. The blockchain data-immutability property is used to validate data stored externally. This solution significantly reduces the costs related to the utilization of the blockchain, while retaining its advantages, and improves performance, since the majority of data are available off-chain.
Blockchain and artificial intelligence have now become the emerging technologies of our time. However, both strategies induce a change in the market but differ in terms of how creative and complex they are. While block chain is decentralized; distributed ledger technology that the one stores information in multiple locations with no centralized monitoring system, on the other hand, Artificial Intelligence (AI) emulates human problem-solving ability as well as decision-making abilities using software, data or even robots. These two technologies combined together to bring about new opportunities such as productivity gain from both advantages of blockchain and AI security and transparency. Businesses can transform significantly through integration of AI with blockchain technology for data protection, clear data trails and general efficiency. Cyber security is one of the most important concepts linked to these two technologies. The possibility to combine AI’s capabilities with blockchain’s reliable and decentralized nature can be used to manage resources as well as decision making at educational institutes or within healthcare agencies; it may also be employed by society to influence agriculture outputs, planning urban dwellings among others. Artificial intelligence (AI) can help identify risks at an early stage whereas information security and integrity might vanish.
Abdullah J. Abualhamayl, Mohanad A. Almalki, Firas Al-Doghman, Abdulmajeed A. Alyoubi · 5 authors
Abstract In the rapidly evolving digital era, the growing trend of conducting real estate e-business transactions through online platforms has led to escalated challenges in ensuring transactional security and trust. These challenges underscore the importance of balancing transparency with data privacy and enhancing accountability in this field. As an extension of our previously published work (Abualhamayl AJ, Almalki MA, Al-Doghman F, Alyoubi AA, Hussain FK (2023) Towards fractional NFTs for joint ownership and provenance in real estate. In: 2023 IEEE international conference on e-business engineering (ICEBE), p. 143–8. 10.1109/ICEBE59045.2023.00022.), this paper introduces the Global Real Estate Platform (GREP), a novel hybrid blockchain system that utilizes real estate provenance to establish a secure and trustworthy environment for real estate e-business, specifically focusing on two key challenges: ensuring data authenticity and effectively managing access rights. Integral to GREP's design is the involvement of government entities, which is essential for maintaining the required balance between transparency, privacy, and high levels of accountability. This proposed framework is explained conceptually and demonstrated practically, offering an innovative perspective on the integration of hybrid blockchain technology in the real estate system. Furthermore, our research encompasses a detailed implementation, using various tools, and an in-depth examination of three use cases. This combined analysis effectively demonstrates GREP's efficacy in addressing the targeted challenges in the field. While acknowledging the system's limitations, including challenges in user adoption and performance variability under different network conditions, our findings open new avenues for further exploration, such as landlords' payment histories and utility bills, and using blockchain as a secondary user identifier. These features collectively highlight the transformative potential of blockchain technology in real estate e-business.
The rising prominence of blockchain technology has spurred researchers to delve deeper into its applications through the lens of organizational and management theories. Our thematic analysis of blockchain literature reveals examination across five key domains: economic, strategic, operational, behavioral, and implementation considerations, each viewed through various theoretical perspectives. The multifaceted nature of blockchain phenomena necessitates diverse types of theory and research questions. Thus, the unique characteristics within each domain stimulate the formulation of inquiries that address a system of research questions about what, how, why, when, where, what will be, and how to do. Existing literature predominantly emphasizes explanatory and predictive theories, notably expanding research topics such as blockchain adoption behavior. However, this singular focus limits the exploration of diverse reasoning approaches suited to blockchain's complexity. Our study illustrates how different types of theory contribute to understanding each of the identified blockchain application areas. Based on our findings, we propose pertinent questions that highlight the need for varied theoretical frameworks and address specific issues within each application domain. Furthermore, we recommend appropriate theories to address these questions, fostering a more multi-perspective investigation across the breadth of blockchain-related research topics and areas. By embracing diverse theoretical perspectives, we aim to create a more balanced research landscape, facilitating deeper insights into blockchain phenomena.
A. Sasikumar, Logesh Ravi, Malathi Devarajan, V. Subramaniyaswamy · 6 authors
Industry 4.0 requires digital twins (DTs), a potential technology in the transition. By offering current operational statistics visualizations of physical assets, assisting in decision-making, and mitigating possible hazards in electronic manufacturing environments, DTs are essential to the improvement of distributed consumer electronics. DTs must also work with blockchain technology in dispersed consumer electronics systems to anticipate data privacy and access control. Unfortunately, the single failure of DT collaboration is caused by centralized attacks and data interoperability, authentication, scalability, and connectivity issues. This paper provides the secure architecture for the DTs’ collaboration in consumer electronic devices. Next, we have created an access control mechanism based on a consensus model that combines blockchain, digital twin, and real-time monitoring of consumer devices. The proposed framework uses blockchain technology to empower digital twin-based consumer electronic devices. Specifically, we created a collaborative blockchain architecture based on manufacturer, retailer, and device users, emphasizing a distributed chain code model and real-time access control. The experimental results show that the proposed system integration performs better than the baseline digital twin architecture.