A fog computing industrial cyber-physical system for embedded low-latency machine learning Industry 4.0 applications

O’Donovan, Peter; Gallagher, Colm V.; Bruton, Ken; O’Sullivan, Dominic

doi:10.1016/j.mfglet.2018.01.005

Cited by 119 publications

(47 citation statements)

References 12 publications

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“…In Reference [32], an industrial CPS was described that employs fog computing and facilitates the delivering of real-time embedded ML applications through cyber-physical industrial interactions, while attaining security and privacy. More importantly, the cloud platform stored production-ready ML models in industrial operations, encoded as predictive modelling markup language (PMML) for various applications, which were divaricated and executed by the locally deployed fog nodes.…”

Section: Cloud/fog/edge Architecturesmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

219

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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Section: Cloud/fog/edge Architecturesmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

219

View full text Add to dashboard Cite

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“…. No architecture framework: The huge amount of data and knowledge from the IoT world is the most promising field to drive cross-domain AI research and enable machine learning to create highly developed AI services [9]. However, existing IoT platforms require the intelligence to improve resource management and support cross-domain interactions.…”

Section: C1mentioning

confidence: 99%

EiF: Toward an Elastic IoT Fog Framework for AI Services

Gall

et al. 2019

IEEE Commun. Mag.

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The first-generation of the Internet-of-Things (IoT) was developed and deployed all over the world by connecting devices with common functionalities that were not sufficiently efficient or reliable for use in dynamic situations that require adaptive solutions. However, these fundamental IoT functions and services mainly targeted stable environments; there is consequently a strong need for the next generation of IoT services to be smarter, faster, and more reliable. We believe that the hyper-connected IoT ecosystem on Fog platforms with contextual AI technologies is a promising solution. In this work, we introduce the Elastic-IoT-Fog (EiF), a flexible Fog computing framework that runs on IoT gateways with adaptive AI services fostered on the Cloud. Our approach can be viewed as an integration of three emerging technologies, namely IoT, Fog, and AI. Generally, EiF virtualizes an IoT service layer platform for fog nodes and provides functions to manage and orchestrate various fog nodes; upon service virtualization and orchestration, AI services are fostered within both the federated Cloud and distributed edge side and are deployed on fog nodes. We demonstrate the feasibility of EiF via the example of intelligent traffic flow monitoring and management.

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“…An example of Fog computing application in cyber-manufacturing for process monitoring and prognosis can be found in Wu et al (2017). In the research work of O' Donovan et al (2018,) authors present a simplified Fog computing CPS for embedded low-latency machine learning Industry 4.0 applications. The initial findings highlight and confirm the Fog's potential to deliver consistent and reliable cyber-physical interactions in real-time engineering scenarios, while Cloud computing can support such scenarios and be tolerant to occasional failures.…”

Section: Related Workmentioning

confidence: 99%

A novel fluid architecture for cyber-physical production systems

Beregi

Pedone

Mezgár

2019

International Journal of Computer Integrated Manufacturing

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Cloud computing has revolutionised the conceptualisation of IT environments in most fields of the economy. Nevertheless, it still requires adequate pre-requisites both in the business strategy and the computational architecture to be adopted successfully and profitably. This is especially true in the context of manufacturing where production services are very sensitive with respect to their distribution and localisation within the boundaries of the computing constellation. The paper aims at clarifying the concept and the application of Fluid Computing in Cyber-Physical Productions Systems, connecting the idea of computational sedimentation to the novel Fluid Manufacturing Architecture (FMA). The FMA encompasses the computing layers of cloud, fog, edge and mist, and extends them with a new one called Dew. The Dew represents the entry point where manufacturing legacy devices are converted into network-able, embedded components for the distributed production scenarios of Industry 4.0. The application of the FMA in a pilot factory has been investigated. The preliminary results of an agent-based simulation case-study for collaborative manufacturing services within the FMA are finally presented. ARTICLE HISTORY

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A fog computing industrial cyber-physical system for embedded low-latency machine learning Industry 4.0 applications

Cited by 119 publications

References 12 publications

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

EiF: Toward an Elastic IoT Fog Framework for AI Services

A novel fluid architecture for cyber-physical production systems

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