PurposeAt the present time, Industry 4.0 has proven its effectiveness and significance in automation and data exchange within industries across different sectors worldwide. In the current literature, there is still a lack of research on adopting Industry 4.0 in the manufacturing setting in developing economies. The main purpose of the present study is to explore the fundamental pillars and framework for ease of adoption of Industry 4.0 in manufacturing environments, along with highlighting the benefits and challenges.Design/methodology/approachIn this study, a systematic literature review has been conducted through protocol, search, appraisal, synthesis, analysis, report (PSALSAR) model. In the literature, the articles are included within time span of 2008–2022, consisting keywords like Industry 4.0, blockchain, machine learning, artificial intelligence, Internet of Things, 3D printing, big data analytics, etc. Based on available literature, conceptual implementation framework of Industry 4.0 is proposed.FindingsThis study explored the key ingredients that play an essential role to bridge the gap and construct a strong relationship among physical and cyber world. The results reveals that the emerging technologies such as IoT, blockchain, artificial intelligence, augmented reality, 3D printing, big-data analytics, cloud-computing join hands to accomplish success in Industry 4.0 by reducing human interference for effective and efficient systems. In addition, the study also explored the possible benefits of emerging technologies with challenges faced by manufacturing setting during adaptation of Industry 4.0.Originality/valueAs per the authors' best knowledge, no research articles are found in literature which explore various emerging technologies in Industry 4.0 with its implementation framework in the manufacturing setting in developing economies. The main focus of the present study is to discover the literature review in defined area and find the research gap among current scenario and future trend for execution of Industry 4.0 in manufacturing environment.

Fundamental pillars for industry 4.0 development: implementation framework and challenges in manufacturing environment

In recent years, the proliferation of big data and analytics technologies has revolutionized various sectors, including public health. This review presents a comprehensive review of how leveraging big data and analytics has enhanced public health decision-making on a global scale. The review encompasses diverse applications, methodologies, challenges, and opportunities within this burgeoning field. Big data analytics in public health encompasses the collection, processing, and analysis of vast datasets from heterogeneous sources, including electronic health records, social media, wearable devices, and environmental sensors. These data sources offer valuable insights into disease patterns, risk factors, healthcare utilization, and population health trends. By applying advanced analytical techniques such as machine learning, predictive modeling, and data visualization, public health officials can extract actionable intelligence to inform decision-making processes. Several case studies highlight the efficacy of big data analytics in various public health domains. For instance, predictive modeling techniques have been utilized to forecast disease outbreaks, enabling timely resource allocation and intervention planning. Social media mining has facilitated real-time surveillance of public sentiment and health-related behaviors, aiding in targeted health promotion campaigns. Additionally, electronic health record analysis has facilitated personalized medicine initiatives and improved patient outcomes. Despite the significant potential of big data analytics in public health, several challenges exist. These include data privacy concerns, data quality issues, interoperability barriers, and the digital divide. Furthermore, ethical considerations regarding consent, transparency, and equity must be carefully addressed to mitigate potential biases and ensure responsible data usage. Looking ahead, the future of leveraging big data and analytics for public health decision-making appears promising. Advancements in data integration, interoperability standards, and artificial intelligence hold immense potential for improving the accuracy, timeliness, and relevance of public health interventions. Collaborative efforts between governments, academia, industry, and civil society are essential to harness the full potential of big data analytics in safeguarding population health and promoting well-being on a global scale. 

Leveraging big data and analytics for enhanced public health decision-making: A global review

Artificial Intelligence (AI) algorithms can be employed to enhance the security of the blockchain networks in the era of industry 4.0. Smart contracts, powered by blockchain, can be developed by using the AI capabilities. These contracts can execute themselves based on predefined conditions, automating various processes and reducing the need for intermediaries. Blockchain provides a transparent and immutable ledger for supply chain transactions. AI can be integrated to analyze this data, providing insights into the entire supply chain. This helps in tracking and tracing products, ensuring authenticity, and optimizing the supply chain based on data-driven decisions. AI algorithms can be employed to analyze data from the manufacturing process recorded on the blockchain. This allows for predictive maintenance and quality control, helping to identify and address issues before they result in defects or downtime. AI and blockchain can work together to secure the massive amount of data generated by IoT devices in an Industry 4.0 setting. Blockchain ensures the integrity and immutability of IoT data, while AI can analyze this data for insights and optimization. AI and blockchain can facilitate the creation of decentralized energy grids where AI algorithms optimize energy distribution and blockchain ensures transparent and secure energy trading through smart contracts. AI and blockchain can support decentralized and collaborative manufacturing networks where different entities can securely and efficiently collaborate on production processes. Blockchain enables individuals or organizations to securely monetize their data. AI can help analyze and determine the value of data, ensuring fair compensation in tokenized ecosystems. In this paper, applications of AI-Powered Blockchain Technology in Industry 4.0 is reviewed and discussed and future research works are also suggested. As a result, efficiency, transparency, and security across various industrial processes can be enhanced by analyzing the recent achievements in AI-Powered Blockchain Technology in Industry 4.0. 

AI-Powered Blockchain Technology in Industry 4.0, A Review

This paper presents an implementation which uses decentralized technologies such as blockchain and IPFS on the issuance and management of the academic certificates. The focus is on improving the actual process, as well as increasing integrity and trustworthiness of ownership of those certificates. The prototype we have developed is called BCert and its purpose is to add the certificates to blockchain network and to offer the possibility to verify them and make accessible to the potential stakeholders. It will also be used to verify that it is possible to upload a minimum of 32 certificates at one time and to evaluate speed of upload. BCert will aim to make the deployment and verification of those certificates quick, fast, secure, and efficient. It uses IPFS (Interplanetary File System) to have private logs and to increase efficiency. Additionally, based on the Technology Transfer concept, we predicted a model related to the launch of the technology to the market. The study was conducted online with 421 responses chosen randomly from several public and private universities in Albania and in Western Balkans.

Towards an IPFS-Blockchain based Authentication/Management System of Academic Certification in Western Balkans

For the past decade, there has been a significant increase in customer usage of public transport applications in smart cities. These applications rely on various services, such as communication and computation, provided by additional nodes within the smart city environment. However, these services are delivered by a diverse range of cloud computing-based servers that are widely spread and heterogeneous, leading to cybersecurity becoming a crucial challenge among these servers. Numerous machine-learning approaches have been proposed in the literature to address the cybersecurity challenges in heterogeneous transport applications within smart cities. However, the centralized security and scheduling strategies suggested so far have yet to produce optimal results for transport applications. This work aims to present a secure decentralized infrastructure for transporting data in fog cloud networks. This paper introduces Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB) for Transport Infrastructure. MORFLB aims to minimize processing and transfer delays while maximizing long-term rewards by identifying known and unknown attacks on remote sensing data in-vehicle applications. MORFLB incorporates multi-agent policies, proof-of-work hashing validation, and decentralized deep neural network training to achieve minimal processing and transfer delays. It comprises vehicle applications, decentralized fog, and cloud nodes based on blockchain reinforcement federated learning, which improves rewards through trial and error. The study formulates a combinatorial problem that minimizes and maximizes various factors for vehicle applications. The experimental results demonstrate that MORFLB effectively reduces processing and transfer delays while maximizing rewards compared to existing studies. It provides a promising solution to address the cybersecurity challenges in intelligent transport applications within smart cities. In conclusion, this paper presents MORFLB, a combination of different schemes that ensure the execution of transport data under their constraints and achieve optimal results with the suggested decentralized infrastructure based on blockchain technology. 

Multi-objectives reinforcement federated learning blockchain enabled Internet of things and Fog-Cloud infrastructure for transport data

Compared to centralized and decentralized models, distributed models have the potential to dramatically expand the scalability of existing IoT and Industry 4.0 solutions while maintaining participant organizations’ security and privacy. This is partly because participating firms are not required to rely on or trust other services or third parties to manage the data they gather and transfer, out of concern that these parties could misuse the data or, in the worst-case scenario, share it with mass surveillance programs. However, until blockchain technology (BCT) demonstrates its viability as a means of developing security solutions in decentralized, collaborative, and trustless environments, the vast majority of these use cases will struggle to meet the requirements for integrity, immutability, traceability, and notarization. By utilizing BCT, it is possible to eliminate intermediaries, enabling individuals and devices to manage their data independently of third parties and most significantly to achieve a high level of traceability with information flow harmony. This technology enables transaction, transparency, and traceability by enabling for the interchange of historical data. The fundamentals of blockchain are examined in this research paper, along with an investigation of its operation and a discussion of some of its most fundamental aspects and concepts. A concise overview of smart contracts enables us to completely reimagine how network members create and automate transactions. Finally, several IoT and Industry 4.0 application possibilities that leverage blockchain are investigated, as is the blockchain’s future trajectory. 

/pdf/adoption-of-blockchain-technology-for-privacy-and-security-37s8muf0.pdf

Adoption of Blockchain Technology for Privacy and Security in the Context of Industry 4.0

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Adoption of Blockchain Technology for Privacy and Security in the Context of Industry 4.0