Identifying the potential of edge computing in factories through mixed reality

Zietsch, Jakob; Büth, Lennart; Juraschek, Max; Weinert, Nils; Thiede, Sebastian; Herrmann, Christoph

doi:10.1016/j.procir.2019.03.259

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…Also, some research can complement ours, like [22], which utilizes edge computing to improve manufacturing systems by proposing a systematic solution for Industrial IoT (IIoT) data-driven solutions; it's focused on three domains: manufacturing, data analytics, and edge computing. Moreover, MR is mentioned as the key for the interface design phase to raise awareness and enhance accessibility.…”

Section: Related Workmentioning

confidence: 99%

“…The conclusion drawn from the study of the previous work [22,[24][25][26][27] is that they have focused only on one or two of the main three pillars of the work considered in this paper (IoE/IoT, AR, fog/cloud computing), ignoring the third one with its benefits to improve the overall performance of the applications. In [23,28], models for MR applications were built in real-time.…”

Section: Problem Formulation and Plan Of Solutionmentioning

confidence: 99%

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Toward a mixed reality domain model for time-Sensitive applications using IoE infrastructure and edge computing (MRIoEF)

2022

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Mixed reality (MR) is one of the technologies with many challenges in the design and implementation phases, especially the problems associated with time-sensitive applications. The main objective of this paper is to introduce a conceptual model for MR application that gives MR application a new layer of interactivity by using Internet of things/Internet of everything models, which provide an improved quality of experience for end-users. The model supports the cloud and fog compute layers to give more functionalities that need more processing resources and reduce the latency problems for time-sensitive applications. Validation of the proposed model is performed via demonstrating a prototype of the model applied to a real-time case study and discussing how to enable standard technologies of the various components in the model. Moreover, it shows the applicability of the model, the ease of defining the roles, and the coherence of data or processes found in the most common applications.

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Section: Related Workmentioning

confidence: 99%

Section: Problem Formulation and Plan Of Solutionmentioning

confidence: 99%

Toward a mixed reality domain model for time-Sensitive applications using IoE infrastructure and edge computing (MRIoEF)

2022

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“…Linking EHRs with claims data from health insurance providers may be preferred because patients may receive care at multiple sites, many of which may not share EHRs. Opportunities to embed RCTs in EHRs include using real-time clinical and laboratory data to identify patients with sepsis and ARD, identifying of subgroups that may benefit from the intervention using machine learning and artificial intelligence algorithms, randomizing within the EHR, and using live decision support (e.g., using edge computing to reduce latency) to provide treatment options to providers at the bedside ( 43 – 46 ). Finally, these approaches will have to be incorporated across multiple EHRs for multicenter RCTs.…”

Section: Recommendationsmentioning

confidence: 99%

A Research Agenda for Precision Medicine in Sepsis and Acute Respiratory Distress Syndrome: An Official American Thoracic Society Research Statement

Shah¹,

Meyer²,

Angus³

et al. 2021

Am J Respir Crit Care Med

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Background: Precision medicine focuses on the identification of therapeutic strategies that are effective for a group of patients based on similar unifying characteristics. The recent success of precision medicine in non–critical care settings has resulted from the confluence of large clinical and biospecimen repositories, innovative bioinformatics, and novel trial designs. Similar advances for precision medicine in sepsis and in the acute respiratory distress syndrome (ARDS) are possible but will require further investigation and significant investment in infrastructure. Methods: This project was funded by the American Thoracic Society Board of Directors. A multidisciplinary and diverse working group reviewed the available literature, established a conceptual framework, and iteratively developed recommendations for the Precision Medicine Research Agenda for Sepsis and ARDS. Results: The following six priority recommendations were developed by the working group: 1 ) the creation of large richly phenotyped and harmonized knowledge networks of clinical, imaging, and multianalyte molecular data for sepsis and ARDS; 2 ) the implementation of novel trial designs, including adaptive designs, and embedding trial procedures in the electronic health record; 3 ) continued innovation in the data science and engineering methods required to identify heterogeneity of treatment effect; 4 ) further development of the tools necessary for the real-time application of precision medicine approaches; 5 ) work to ensure that precision medicine strategies are applicable and available to a broad range of patients varying across differing racial, ethnic, socioeconomic, and demographic groups; and 6 ) the securement and maintenance of adequate and sustainable funding for precision medicine efforts. Conclusions: Precision medicine approaches that incorporate variability in genomic, biologic, and environmental factors may provide a path forward for better individualizing the delivery of therapies and improving care for patients with sepsis and ARDS.

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“…It enables decentral implementations by providing local processing and communication capabilities that allow new types of data-driven solutions. Due to the novelty of the technology, there is still a lack of industry-ready applications that profit from these new capabilities [20].…”

Section: Relevance In Manufacturingmentioning

confidence: 99%

“…In the following, we attempt to summarize and highlight the most significant challenges for EC in manufacturing based on [20,22,23]: Safe and reliable operation is key and failure can lead to fatal consequences Changes and additions of infrastructure are cost-intensive compared to the expenses for hardware Historic technology investment leads to a hesitance against adaption (Never change a running system) Lack of industry-ready applications and platforms Dependency on proprietary systems or protocols (Legacy machinery requires a physical connection to be integrated into the existing infrastructure) Regulatory and quality constraints Complexity distributed over multiple departments It is of no surprise that factory owners hesitate to adopt EC within their factory when comparing the significant challenges against potential benefits. To overcome this hesitancy and gain a technological advantage over the competition, an approach is required that enables a structured and holistic analysis of data processing in manufacturing, specifically aiming at evaluating the potential of EC.…”

Section: Relevance In Manufacturingmentioning

confidence: 99%

Enabling smart manufacturing through a systematic planning framework for edge computing

Zietsch

Vogt

Lee

et al. 2020

CIRP Journal of Manufacturing Science and Technology

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Identifying the potential of edge computing in factories through mixed reality

Cited by 7 publications

References 13 publications

Toward a mixed reality domain model for time-Sensitive applications using IoE infrastructure and edge computing (MRIoEF)

Toward a mixed reality domain model for time-Sensitive applications using IoE infrastructure and edge computing (MRIoEF)

A Research Agenda for Precision Medicine in Sepsis and Acute Respiratory Distress Syndrome: An Official American Thoracic Society Research Statement

Enabling smart manufacturing through a systematic planning framework for edge computing

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