Blockchain Papers

Deqian Fu, Shunbo Hu, Lintao Zhang, Shuqing He · 5 authors

No abstract is available for this record.

Smaïl Benzidia, Naouel Makaoui, Nachiappan Subramanian

No abstract is available for this record.

Svoronos Leivadaros, George Kornaros, Marcello Coppola

Today, manufacturing industry is increasingly embracing new technologies such as the Internet of Things (IoT), big data analytics, cloud computing and cybersecurity to cope with system complexity, increase information visibility, improve production performance, and gain competitive advantages in the global market. These advances are rapidly enabling a new generation of smart manufacturing, i.e., a cyber-physical system tightly integrating manufacturing enterprises in the physical world with virtual enterprises in cyberspace. To a great extent, realizing the full potential of cyber-physical systems depends on the development of new methodologies on the Internet of Manufacturing Things (IoMT) for data-enabled engineering innovations. This article presents a real implementation of IOTA Tangle architecture for data transactions extended with HMAC signing through using STM32 (F7 CPU) IoT devices. The evaluation results show promising with 32 light nodes to exceed 28 transactions per second by using 4 full nodes, thus making IOTA-based distributed ledger an effective solution for IoT-based manufacturing environments with zero-value (data) transactions.

Dario Assante, Clemente Capasso, Ottorino Veneri, Manuel Castro · 6 authors

Technologies 4.0 have had disruptive innovation in various sectors, including the energy one. Smart electricity grids, leveraging smart metering and data analysis systems, can provide a level of monitoring, control and optimization unimaginable until a decade ago. Network management mechanisms based on demand-response models may soon be a reality, thanks to smart contracts, blockchain, artificial intelligence and machine-to-machine technologies. These changes are leading to a radical change in the skills required by the labor market to operate in the energy sector, as well as the birth of new professional figures with both electricity and IT skills. This paper aims to illustrate the new opportunities and challenges that the Internet of Energy will bring. It also wants to present the results of the IoE-EQ project, which designed new professional qualifications, VET courses and open educational resources to support the training of staff involved in the digital transition of the energy sector.

Zeinab Shahbazi, Yung-Cheol Byun

The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from sensors are unstructured, massive, and real-time. Various big data techniques are applied to further process of the data. The hybrid prediction model used in this system uses the Random Forest classification technique to remove the sensor data outliers and donate fault detection through the manufacturing system. The proposed system was evaluated for automotive manufacturing in South Korea. The technique applied in this system is used to secure and improve the data trust to avoid real data changes with fake data and system transactions. The results section provides the effectiveness of the proposed system compared to other approaches. Moreover, the hybrid prediction model provides an acceptable fault prediction than other inputs. The expected process from the proposed method is to enhance decision-making and reduce the faults through the manufacturing process.

Dongmin Lee, Sang Hyun Lee, Neda Masoud, Mayuram S. Krishnan · 5 authors

No abstract is available for this record.

İbrahim Yitmen, Sepehr Alizadehsalehi

With the creation of massive data in projects, the digitization/computerization of various stages and processes of built environments are emerging to have a broad influence on how the architecture, engineering, and construction (AEC) projects are planned, built, and managed. With the evolution of Big Data and model-based engineering, the next logical step is to introduce the concept of Digital Twin, extending model-based paradigms along the complete lifecycle. Digitalization and computerization of data and information flow have a broad influence on the system and process of managing the lifecycle of projects. The relation among the DT and smart cities has been recognized as the novel and ultimate technological apparatus for smartening cities. Blockchain empowers the security of the DT system through cryptographic hashing algorithms. This process is possible by storing DT data on distributed ledgers that cannot be changed easily and cannot be controlled by any central authority. DT encompasses sensors, measurement technologies, internet of things, simulation, and modeling and ML.

Anam Bhatti, Haider Ali Malik, Ahtisham Zahid Kamal, Alamzeb Aamir · 6 authors

Purpose In the field of business, digital transformation is the integration of digital technology into all areas of business, from generating to deliver value to customers. This concept is essential for sustainable growth of a company and its overall economy. Based on this fact, this authentic and informative research is conducted whose major aim is to examine the importance of digital transformation within a business through big data, the Internet of things and blockchain-based capabilities for overall strategic performance within the telecom sector in China. Design/methodology/approach For that aim, data quality and technology competence are considered as independent variables, strategic performance as dependent variable and big data analytics capabilities, Internet of things capabilities and blockchain capabilities routinization acted as mediators within this paper. In its data collection mechanism, an online survey was conducted in which questionnaires are randomly distributed to the telecom sector's professionals in which only 343 of them gave their valid outcomes. After collecting primary data, confirmatory factor analysis (CFA) and structural equation modeling (SEM)–based statistical outcomes have been generated. Findings Results indicate that there is a significant relationship between data quality and strategic performance and between technological competence and strategic performance. Also, the big data analytics and Internet of Things capabilities acted as significant mediating role between both independent and dependent variables. But blockchain capabilities routinization is that variable that acts as an insignificant mediator between independent and dependent variables' relationship. Originality/value Overall, this study is an informative and attractive source for the Chinese government, its telecom industry, administrative body and related ones to understand the importance of such IT capabilities' implications within their operating activities for their strategic performance management. Also, related field scholars can utilize its reliable data in their research analysis. Its major limitations are (1) lack of qualitative/ mixed method of research and (2) lack of comparative analysis that may impact the acceptability factor of this paper, and this weakness can be overcome by upcoming scholars in their research.

Lanlin Li, Yinglei Teng, F. Richard Yu, Mei Song · 5 authors

Nowadays, blockchain has become a promising tamper-evident and tamper-resistant distributed ledger technology that achieves the security and privacy through the cryptography, consensus mechanism and chained data structure. In this paper, we propose a general blockchain-based smart manufacturing system (BSM) that utilizes the decentralization, immutability, auditability of blockchain to achieve flexible manufacturing that responds to on-demand services in time. For management, manufacturing services are divided into tasks and for unified scheduling, these tasks are queued along the logical flow in the transaction pool. Considering the contradiction between large scale manufacturing and limited transactional throughput, a joint task scheduling over blockchains and supply-demand configuration design is proposed to obtain the maximum customers' net profit while balancing the timeliness as well as the production and blockchain payoff. Moreover, a maximum weight matching based Alternating Optimization framework (MWMAO) is proposed as the solution. Simulation results show that the proposed framework has superiority on the profitability.

Gordon Lemme, Kilian Armin Nölscher, Erhao Bei, Christian Hermeling · 5 authors

In this paper, we use the blockchain technology to design a prototype to secure process data from a 3D-printer. Datastreams are gathered from various sources such as OPC UA servers and autonomous retrofit sensor nodes. This is followed by pre-processing for data reduction, storage in a data model, and the generation of a unique hash value over it. The hash values are stored in a blockchain using appropriate consensus methods, taking into account their temporal origin and production identification number. This also includes the context-related influence of sensor signals on the production process Restrictive access regulations using smart contracts make a partially or fully automated machine tool calibration possible. In this context, we show to realize a process partial or full automation through smart contracts. Physical machine tools and virtual simulations are integrated into the blockchain network to document the stability and performance.

Balan Sundarakani, Aneesh Ajaykumar, Angappa Gunasekaran

No abstract is available for this record.

Sabah Suhail, Rasheed Hussain, Raja Jurdak, Alma Oracevic · 7 authors

Industrial processes rely on sensory data for decision-making processes, risk assessment, and performance evaluation. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the dissemination of trustworthy data. For the physical data to be trustworthy, it needs to be cross validated through multiple sensor sources with overlapping fields of view. Cross-validated data can then be stored on the blockchain, to maintain its integrity and trustworthiness. Once trustworthy data is recorded on the blockchain, product lifecycle events can be fed into data-driven systems for process monitoring, diagnostics, and optimized control. In this regard, digital twins (DTs) can be leveraged to draw intelligent conclusions from data by identifying the faults and recommending precautionary measures ahead of critical events. Empowering DTs with blockchain in industrial use cases targets key challenges of disparate data repositories, untrustworthy data dissemination, and the need for predictive maintenance. In this survey, while highlighting the key benefits of using blockchain-based DTs, we present a comprehensive review of the state-of-the-art research results for blockchain-based DTs. Based on the current research trends, we discuss a trustworthy blockchain-based DTs framework. We also highlight the role of artificial intelligence in blockchain-based DTs. Furthermore, we discuss the current and future research and deployment challenges of blockchain-supported DTs that require further investigation.

Michael Kuperberg, Matthias Geipel

In construction, BIM (Building Information Modeling) promises to increase quality of data and to provide a shared, uniform view to all parties. While BIM tools and exchange formats exist, the distribution and safeguarding of data is an ongoing challenge. Distributed Ledger Technology and Blockchains offer a possible solution to this task, and they promise quality attributes such as tamper resistance, traceability/auditability and safe digitalization of assets and intellectual property. However, the practical application and adoption of Distributed Ledger Technology in the built environment requires a good understanding of tool maturity, performance and standardization. Also, user-oriented integration of BIM tools with the blockchain backend needs attention. The contribution of this paper is an overview over both industrial and academic progress at the intersection of BIM and blockchains/DLT.

Anton Hasselgren, Jens-Andreas Hanssen Rensaa, Katina Kralevska, Danilo Gligoroski · 5 authors

<sec> <title>BACKGROUND</title> Health care systems are currently undergoing a digital transformation that has been primarily triggered by emerging technologies, such as artificial intelligence, the Internet of Things, 5G, blockchain, and the digital representation of patients using (mobile) sensor devices. One of the results of this transformation is the gradual virtualization of care. Irrespective of the care environment, trust between caregivers and patients is essential for achieving favorable health outcomes. Given the many breaches of information security and patient safety, today’s health information system portfolios do not suffice as infrastructure for establishing and maintaining trust in virtual care environments. </sec> <sec> <title>OBJECTIVE</title> This study aims to establish a theoretical foundation for a complex health care system intervention that aims to exploit a cryptographically secured infrastructure for establishing and maintaining trust in virtualized care environments and, based on this theoretical foundation, present a proof of concept that fulfills the necessary requirements. </sec> <sec> <title>METHODS</title> This work applies the following framework for the design and evaluation of complex intervention research within health care: a review of the literature and expert consultation for technology forecasting. A proof of concept was developed by following the principles of design science and requirements engineering. </sec> <sec> <title>RESULTS</title> This study determined and defined the crucial functional and nonfunctional requirements and principles for enhancing trust between caregivers and patients within a virtualized health care environment. The cornerstone of our architecture is an approach that uses blockchain technology. The proposed decentralized system offers an innovative governance structure for a novel trust model. The presented theoretical design principles are supported by a concrete implementation of an Ethereum-based platform called VerifyMed. </sec> <sec> <title>CONCLUSIONS</title> A service for enhancing trust in a virtualized health care environment that is built on a public blockchain has a high fit for purpose in Healthcare 4.0. </sec>

Pooi-Mun Wong, Shreya R. K. Sinha, Chee‐Kong Chui

Blockchain has been applied to quality control in manufacturing, but the problems of false defect detections and lack of data transparency remain. This paper proposes a framework, Blockchain Quality Controller (BCQC), to overcome these limitations while fortifying data security. BCQC utilizes blockchain and Internet-of-Things to form a peer-to-peer supervision network. This paper also proposes a consensus algorithm, Quality Defect Tolerance (QDT), to adopt blockchain for during-production quality control. Simulation results show that BCQC enhances data security and improves defect detections. Although the time taken for the quality control process increases with the number of nodes in blockchain, the application of QDT allows multiple inspections on a workpiece to be consolidated at a faster pace, effectively speeding up the entire quality control process. The BCQC and QDT can improve the quality of parts produced for mass personalization manufacturing.

Wei Li, Ping Duan, Jingzhi Su

No abstract is available for this record.

Zeinab Shahbazi, Yung-Cheol Byun

Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management. To overcome the problems mentioned above, we applied the integrated methods of blockchain and machine learning to secure system transactions and handle a dataset to overcome the fake dataset. To manage and analyze the collected dataset, big data techniques were used. The blockchain system was implemented in the private Hyperledger Fabric platform. Similarly, the fault diagnosis prediction aspect was evaluated based on the hybrid prediction technique. The system's quality control was evaluated based on non-linear machine learning techniques, which modeled that complex environment and found the true positive rate of the system's quality control approach.

Gunasekaran Manogaran, Shahid Mumtaz, Constandinos X. Mavromoustakis, Evangelos Pallis · 5 authors

Mobile Edge Computing (MEC) paradigm is designed to meet the user requirements by providing cloud services at the edge of the user network. Blockchain technology with the Edge Computing (EC) paradigm is reliable in delivering the edge services depending on user requirements and improving the distributed management of resources at ease. In this article, blockchain-assisted data offloading for Availability Maximization (BDO-AM) is introduced. This proposed approach is presented to thwart the non-probabilistic (NP) hardness problem of data availability due to prolonging backlogs. This approach classifies the different instances of data availability and delivery for the edge-connected end-user services/applications. The classification is preceded by using Naïve Bayes' classification to identify the offloading instances to prevent unnecessary backlogs. The probability of data transmission, delivery, and offloading are independently analyzed for their likelihood, and the appropriate available time instances are allocated in a distributed manner. This validation helps to maximize data delivery by reducing the data drops and service delays.

Erick Fernando, Meyliana Meyliana, Harco Leslie Hendric Spits Warnars, Edi Abdurachman

This study builds the implementation of the traceability process by conducting simulation tests using business process simulations with the implementation of blockchain technology to track drugs. This research focus involved stakeholders, including the pharmaceutical industry, pharmaceutical wholesalers (distributors/wholesalers), health services (drug stores, hospitals), consumers. Simulation methods are used to describe the distribution and traceability of drugs. Finally, the research contribution in incorporating blockchain technology to supply chain management could potentially help in drug traceability. This study provides an overview of blockchain technology capabilities to find out which stakeholders and assets are transacted on the blockchain system. A decentralized Autonomous Organization is an approach to organizing data on the blockchain that defines all stakeholders identities associated with different addresses. This process can organize each address's transactions on a special blockchain platform in this study using multichain. Furthermore, transactions that have occurred cannot be updated or deleted. This simulation also illustrates some of the blockchain characteristics that must exist, among others, transparent, distributed, immutable, and peer to peer transactions. This contribution gives supply chain management, in particular on drug distribution, stronger control over distribution.

Reza Vatankhah Barenji

No abstract is available for this record.

Deepak Mathivathanan, K. Mathiyazhagan, Nripendra P. Rana, Sangeeta Khorana · 5 authors

Blockchain is an emerging technology with a wide array of potential applications. This technology, which underpins cryptocurrency, provides an immutable, decentralised, and transparent distributed database of digital assets for use by firms in supply chains. However, not all firms are appropriately suited to adopt blockchain in the existing supply chain primarily due to their lack of knowledge on the benefits of this technology. Using Total Interpretive Structural Modelling (TISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC), this paper identifies the adoption barriers, examines the interrelationships between them to the adoption of blockchain technology, which has the potential to revolutionise supply chains. The TISM technique supports developing a contextual relationship-based structural model to identify the influential barriers. MICMAC classifies the barriers in blockchain adoption based on their strength and dependence. The results of this research indicate that the lack of business awareness and familiarity with blockchain technology on what it can deliver for future supply chains, are the most influential barriers that impede blockchain adoption. These barriers hinder and impact businesses decision to establish a blockchain-enabled supply chain and that other barriers act as secondary and linked variables in the adoption process.

Sam Adhikari

View Video Presentation: https://doi.org/10.2514/6.2021-0662.vid The term Blockchain Technology (BT) refers to information technology (IT) where data is stored in numerous blocks using multiple distributed servers. Through digital automation the data is refreshed periodically at a frequency set by data architects and network designers. The servers are sometimes on the public internet, but in the aerospace world it is more within private intranet of authorized users to maintain privacy and security. A Blockchain network registers the components within aerospace echo system in a digital ledger accompanied by relevant data. It is virtually immutable. The data residing within BT cannot be changed without the awareness of the collaborative parties. Even if a hacker manages to penetrate one block, the overall integrity of the data would remain intact since the data is distributed among thousands of blocks containing cryptographically protected digital ledgers. Deep reinforced learning (DRL) is a machine learning (ML) process based on reinforced learning (RL) and neural networks (NN). DRL training algorithms adjusts its actions dynamically motivated by a system of reward and punishment. The DRL algorithm learns by interacting with its dynamic environment. The DRL software agents receive rewards for correct actions and penalties for performing incorrectly. These agents learn without human intervention by targeting maximizing the reward and minimizing the penalty. Robotic process automation (RPA) is a business process automation technology based on software agents. It can trigger automated agent-based actions. When RPA and DRL are combined, system operates software agents that can learn in dynamic environment automatically without human interventions. They act as autonomous agents that learns continuously and operates cyber infrastructure autonomously. In this paper we show the implementation process and the benefits of integrating deep reinforced learning and robotic process automation in Blockchain digital transformation for autonomous cybersecurity.

Kwok-Bun Yue, Mark Guerra, Howard Wagner, Joses Sandeep Thamarai Selvan · 11 authors

View Video Presentation: https://doi.org/10.2514/6.2021-0093.vid A single Authoritative Source of Truth (AST) of digitalized models is core to Model-Based Systems Engineering (MBSE), which enhances better understanding of the design changes, communicates design intents, analyzes design impacts, and enables automation and optimization. Important technical challenges of supporting MBSE projects include possible consortia of large numbers of organization participants, complex governance, rigorous compliance requirements, nuanced privacy rules, performance requirements, and integration with existing Systems Engineering Environments that are hybrid, complex, legacy, and potentially siloed. The ability of Blockchain Technology (BCT) for managing immutable, distributed ledgers of versatile transactions using smart contracts incited tremendous interests and activities in recent years. Although BCT can provide the foundation of an effective AST solution, very little work has been done on applying BCT on MBSE. Based on a pilot blockchain prototyping experiment on an aerospace MBSE project using Hyperledger’s Fabric, this paper examines the opportunities and challenges of applying BCT on MBSE. It summarizes the design and implementation of a prototype for exploring the opportunities and partially addressing some challenges, and provides pointers for future directions for industry and researchers.

Zeinab Shahbazi, Yung-Cheol Byun

The modern industry, production, and manufacturing core is developing based on smart manufacturing (SM) systems and digitalization. Smart manufacturing’s practical and meaningful design follows data, information, and operational technology through the blockchain, edge computing, and machine learning to develop and facilitate the smart manufacturing system. This process’s proposed smart manufacturing system considers the integration of blockchain, edge computing, and machine learning approaches. Edge computing makes the computational workload balanced and similarly provides a timely response for the devices. Blockchain technology utilizes the data transmission and the manufacturing system’s transactions, and the machine learning approach provides advanced data analysis for a huge manufacturing dataset. Regarding smart manufacturing systems’ computational environments, the model solves the problems using a swarm intelligence-based approach. The experimental results present the edge computing mechanism and similarly improve the processing time of a large number of tasks in the manufacturing system.