Blockchain-Based Decentralized Digital Manufacturing and Supply for COVID-19 Medical Devices and Supplies

AlKhader, Wala; Sleptchenko, Andrei; Jayaraman, Raja; Yaqoob, Ibrar; Omar, Mohammed

doi:10.1109/access.2021.3118085

Cited by 27 publications

(30 citation statements)

References 33 publications

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“…Numerous businesses, like Google, Microsoft, and Nokia, among others, actively research it since it may produce substantial outcomes for various classification and regression issues and datasets [54]. In addition, the PHR is a patient-centered system that is run by patients in a confidential environment [55][56][57]. It ensures the data which is stored in the database is secure and properly controlled by the patient and accessed by the others as per the consent of the owner.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

“…Self-executing smart contracts are used to apply consent management policies. However, if adequate methods are not created to manage vast amounts of healthcare data, blockchain technology becomes expensive [55,56]. The pointers and linkers can play an important role in reducing the data amount so that blockchain can be used to its full potential in many healthcare applications.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

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WITHDRAWN: Integration of Blockchain Technology with Deep Learning Approaches: Vision and Future Opportunities

Afaq

Manocha

2022

Preprint

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Recently, Deep Learning and blockchain technology has gained successful attention in recent research due to their high potential for generating accurate decision. The operational transparency, traceability, dependability, security, and trustworthy data provenance characteristics are offered by the vast majority of current deep learning approaches that are developed on centralized servers. Therefore, using centralized data to train Deep Learning models makes them vulnerable to the single point of failure issue. In this article, the benefits of combining Blockchain and Deep Learning algorithms are explored. Moreover, the state-of-the-art literature is reviewed to find out the most possible way to integrate these two technologies for an optimal solution. Based on the literature survey this article is divided by a thematic taxonomy that includes seven different parameters such as Deep Learning techniques, protocols, domains, types of blockchain, use of deep learning and blockchain, and datasets. We have outlined the advantages and disadvantages of the most advanced blockchain-based deep learning frameworks to give insightful discussions. At last, the most important challenges that need further research are also highlighted with future recommendations.

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Section: Deep Learning (Dl)mentioning

confidence: 99%

Section: Deep Learning (Dl)mentioning

confidence: 99%

WITHDRAWN: Integration of Blockchain Technology with Deep Learning Approaches: Vision and Future Opportunities

Afaq

Manocha

2022

Preprint

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“…They use IPFS for storing and sharing design files, IoT device records, and other product specifications in a decentralised manner. Decentralised digital manufacturing of medical devices and their supply can be achieved using a blockchain-based solution presented in [91]. The authors describe the system's architecture and algorithms in detail and their implementation and testing.…”

Section: Classification Of the Retrieved Literaturementioning

confidence: 99%

A Systematic Literature Review of Blockchain-Enabled Supply Chain Traceability Implementations

et al. 2022

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In recent years, traceability systems have been developed as practical tools for improving supply chain (SC) transparency and visibility, especially in health and safety-sensitive sectors like food and pharmaceuticals. Blockchain-related SC traceability research has received significant attention during the last several years, and arguably blockchain is currently the most promising technology for providing traceability-related services in SC networks. This paper provides a systematic literature review of the various technical implementation aspects of blockchain-enabled SC traceability systems. We apply different drivers for classifying the selected literature, such as (a) the various domains of the available blockchain-enabled SC traceability systems and relevant methodologies applied; (b) the implementation maturity of these traceability systems along with technical implementation details; and (c) the sustainability perspective (economic, environmental, social) prevalent to these implementations. We provide key takeaways regarding the open issues and challenges of current blockchain traceability implementations and fruitful future research areas. Despite the significant volume and plethora of blockchain-enabled SC traceability systems, academia has so far focused on unstructured experimentation of blockchain-associated SC traceability solutions, and there is a clear need for developing and testing real-life traceability solutions, especially taking into account feasibility and cost-related SC aspects.

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“…As shown in Fig. 1 , the adoption of a blockchain instead of a traditional operating system in the supply chain is expected to increase institution-based trust (e.g., Qian & Papadonikolaki, 2020 ; Tezel et al, 2021 ) and cognition-based trust (e.g., Alkhader et al, 2021 ; Kayikci et al, 2020 ) in a system user or an organization that operates the technology. An increase in institutional trust is the outcome of a pre-agreed governance protocol and payment mechanism which will be self-executed when operating the blockchain (Qian & Papadonikolaki, 2020 ).…”

Section: Blockchain and Trust In Supply Chainsmentioning

confidence: 99%

Blockchain as the “trust-building machine” for supply chain management

2022

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This paper aims to investigate the impact of blockchain application on trust levels in supply chains. Through the systematic review of the relevant literature, three dimensions of trust, i.e., the trustor–trustee perspective, forms of trust, and time orientation, are investigated. Our findings show that, first, there are three pairs of trustors and trustees involved in blockchain implementation: (a) the user and the blockchain, (b) two supply chain partners, and (c) the consumer/public and a supply chain unit. Second, the two forms of trust, namely cognition-based and institution-based trust, are likely to be enhanced by blockchain execution, while affect-based trust may not be directly impacted by the technology. Third, the presence of blockchain technology would facilitate swift trust-building between unknown supply chain partners under specific circumstances. Moreover, we also find contradicting assertions among scholars on the implications of blockchain for trust in supply chains. While some studies pointed out that blockchain will enable a trustless trusted scheme, others expected the reinforcement of interorganizational trust. To test these assertions, we develop the blockchain-entrusted supply chain models to present the three-step process of how trust is developed through the blockchain and diffused to supply chain partners and external stakeholders.

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Blockchain-Based Decentralized Digital Manufacturing and Supply for COVID-19 Medical Devices and Supplies

Cited by 27 publications

References 33 publications

WITHDRAWN: Integration of Blockchain Technology with Deep Learning Approaches: Vision and Future Opportunities

WITHDRAWN: Integration of Blockchain Technology with Deep Learning Approaches: Vision and Future Opportunities

A Systematic Literature Review of Blockchain-Enabled Supply Chain Traceability Implementations

Blockchain as the “trust-building machine” for supply chain management

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