A Novel Three-Layer IoT Architecture for Shared, Private, Scalable, and Real-time Machine Learning from Ubiquitous Cyber-Physical Systems

Parto, Mahmoud; Saldaña, Christopher

doi:10.1016/j.promfg.2020.05.135

Cited by 14 publications

(9 citation statements)

References 15 publications

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“…In order to improve the IoT scalability and decrease the computing cost of running machine learning algorithms of IoT data, Parto et al developed a three-layer IoT architecture which is again divided into edge, fog and cloud layers [15]. Data preprocessing in lower layers increases the computational performance.…”

Section: Internet Of Thingsmentioning

confidence: 99%

“…Data preprocessing in lower layers increases the computational performance. Moreover, they recommend federated learning to train ML models locally but sharing them with other sites [15]. Combining cloud computing with edge and fog computing to leverage the analysis possibilities brought by IoT data has been investigated by others as it is a more efficient way of analyzing and storing large volumes of data [16].…”

Section: Internet Of Thingsmentioning

confidence: 99%

“…As the exponential growth of IoT devices is continuing, the data collection and transfer becomes increasingly routine, largely due to IoT architectures [13,14]. IoT architecture can contain three components-edge computing, fog computing and cloud computing [15]-where cloud technologies adopt these three layers to share the data between devices and perform big data analytics [16,17]. The edge layer, notably, contains the data sources such as machine tools, speeding up data analytic processed by moving computational power towards the network edge [15].…”

Section: Introductionmentioning

confidence: 99%

“…IoT architecture can contain three components-edge computing, fog computing and cloud computing [15]-where cloud technologies adopt these three layers to share the data between devices and perform big data analytics [16,17]. The edge layer, notably, contains the data sources such as machine tools, speeding up data analytic processed by moving computational power towards the network edge [15]. Moreover, fog computing involves communication with the cloud and performing computationally heavy tasks close to data source to assure real-time analysis capabilities [15,18].…”

Section: Introductionmentioning

confidence: 99%

“…The edge layer, notably, contains the data sources such as machine tools, speeding up data analytic processed by moving computational power towards the network edge [15]. Moreover, fog computing involves communication with the cloud and performing computationally heavy tasks close to data source to assure real-time analysis capabilities [15,18].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

Lepasepp

Hurst

2021

Future Internet

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Ever since the emergence of Industry 4.0 as the synonymous term for the fourth industrial revolution, its applications have been widely discussed and used in many business scenarios. This concept is derived from the advantages of internet and technology, and it describes the efficient synchronicity of humans and computers in smart factories. By leveraging big data analysis, machine learning and robotics, the end-to-end supply chain is optimized in many ways. However, these implementations are more challenging in heavily regulated fields, such as medical device manufacturing, as incorporating new technologies into factories is restricted by the regulations in place. Moreover, the production of medical devices requires an elaborate quality analysis process to assure the best possible outcome to the patient. Therefore, this article reflects on the benefits (features) and limitations (obstacles), in addition to the various smart manufacturing trends that could be implemented within the medical device manufacturing field by conducting a systematic literature review of 104 articles sourced from four digital libraries. Out of the 7 main themes and 270 unique applied technologies, 317 features and 117 unique obstacles were identified. Furthermore, the main findings include an overview of ways in which manufacturing could be improved and optimized within a regulated setting, such as medical device manufacturing.

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Section: Internet Of Thingsmentioning

confidence: 99%

Section: Internet Of Thingsmentioning

confidence: 99%