Secure Asset Tracking in Manufacturing through Employing IOTA Distributed Ledger Technology

Leivadaros, Svoronos; Kornaros, George; Coppola, Marcello

doi:10.1109/ccgrid51090.2021.00091

Cited by 2 publications

(3 citation statements)

References 33 publications

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“…Block data [66], [67] Consensus mechanism [68], [69], [70], [71], [72], [73], [74], [75], [67], [76] Blockchain structure [77], [78], [79], [80], [81], [53], [82] Sharding [83], [84], [85] Layer 2…”

Section: Scalability Solutionsmentioning

confidence: 99%

“…of the concepts presents the implementation of the proposed architecture using STM32 IoT devices. The scalability evaluation of the implemented system suggests improved scalability and that the proposed architecture is feasible in SMS [82]. However, all of the above concepts do not provide any specific details on how a general-purpose DAG blockchain structure would be implemented in connection with the manufacturing system.…”

Section: Blockchain Structurementioning

confidence: 99%

“…Furthermore, the capabilities of IIoT devices in manufacturing systems are improving. Evaluation of the concepts in the future should include additional devices with specialized hardware designed for blockchain-supported systems [82]. In the case of optimization of the consensus mechanism selection concept [75], the author proposes that in the future new consensus mechanism should be developed and that it should be adapted for different properties of manufacturing systems.…”

Section: Future Directions Open Issuesmentioning

confidence: 99%

See 2 more Smart Citations

Scalability Solutions in Blockchain-Supported Manufacturing: A Survey

Rožman¹,

Corn²,

Škulj³

et al. 2022

sv-jme

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Researchers in the field of smart manufacturing have recognized the benefits of blockchain technology, which solves the trust problem in the open network without relying on any trusted third party. Blockchain technology enables interaction between otherwise competing manufacturing entities to satisfy increasing customer demands in a trustful way. However, existing blockchain networks are facing limitations, which are defined by the trade-off between scalability, decentralization, and security. The scalability of the blockchain network is defined as the ability of the network to support an increasing load of transactions and it is lower compared to the non-blockchain systems. In order to omit the effects of the limitations, scalability solutions are being presented. This research reviews the literature in the field of blockchain-supported manufacturing concerning scalability solutions. The selected literature has been reviewed and classified according to the type of scalability solution. For each type of scalability solution, the main features of the concepts and connection between blockchain technology and manufacturing system are highlighted and discussed. The main findings of the study are that Layer 1 scalability solutions are better represented in the literature and are predominating in the case of general smart manufacturing systems, whereas Layer 2 scalability solutions are better represented in the case of specific smart manufacturing systems. Based on insights obtained from the presented analysis, future directions and open issues regarding the scalability limitations and solutions in blockchain-supported manufacturing are presented.

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Section: Scalability Solutionsmentioning

confidence: 99%

Section: Blockchain Structurementioning

confidence: 99%

Section: Future Directions Open Issuesmentioning

confidence: 99%

See 1 more Smart Citation

Scalability Solutions in Blockchain-Supported Manufacturing: A Survey

Rožman¹,

Corn²,

Škulj³

et al. 2022

sv-jme

View full text Add to dashboard Cite

show abstract

Roles of IoT, Big Data Analytics, and Cyber-Physical Systems in a Sustainable Manufacturing

Ikumapayi,

Laseinde,

Akinlabi

2024

E3S Web Conf.

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The convergence of big data analytics, cyber-physical systems (CPS), and the Internet of Things (IoT) is causing a fascinating revolution in the manufacturing sector. The convergence known as "Industry 4.0" is expected to bring about a symphony of sustainability, adaptability, and efficiency where machines move in time with real-time data. The maestro at this point is big data analytics, which translates these tunes to improve the whole thing from inventory control to machine maintenance. By permitting machines to separately adjust settings, act to fluctuating circumstances, and cooperate with one another, CPS bridge the gap amid the digital and physical worlds. This materializes into an astonishing ballet of intelligent automation. But regardless of this fascinating music, there are trials waiting in the shadows. Robust cybersecurity procedures are essential to address worries about data security and privacy, and continuous research and development is compulsory to manage the multifaceted integration and standardization of several technologies. Cautious planning and oversight are also compulsory due to ethical worries about algorithmic prejudice and job movement. The manufacturing industry is full of potential for invention and sustainability, even in the face of these dark clouds. Let us not be overwhelmed by the hindrances in the way of this uprising, but relatively use them to our benefit to generate a harmonious prearrangement of responsible solutions. Industry 4.0 will be protected to dance not just with data but also with beliefs, safety, and sustainability by means of continuing research, transparent communication, and investments in labor force development. This will pave the way for a upcoming in which humans and robots work collectively for an improved tomorrow. The main concepts of Industry 4.0 are outlined in this paper, along with its prospects, complications, and condition for an all-encompassing strategy.

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Secure Asset Tracking in Manufacturing through Employing IOTA Distributed Ledger Technology

Cited by 2 publications

References 33 publications

Scalability Solutions in Blockchain-Supported Manufacturing: A Survey

Scalability Solutions in Blockchain-Supported Manufacturing: A Survey

Roles of IoT, Big Data Analytics, and Cyber-Physical Systems in a Sustainable Manufacturing

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