Cloud-assisted industrial cyber-physical systems: An insight

Yue, Xuejun; Cai, Hu; Yan, Hehua; Zou, Caifeng; Zhou, Keliang

doi:10.1016/j.micpro.2015.08.013

Cited by 132 publications

(58 citation statements)

References 55 publications

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“…Cloud-based architectures are the most widely used in Industry 4.0 applications, due to their flexibility and efficiency,allowing both horizontal and vertical integration [11,24]. However, it might not always meet the stringent requirements posed by Industry 4.0 or IIoT applications.…”

Section: Related Workmentioning

confidence: 99%

“…Besides applying novel and advanced communication technologies, it is crucial to deploy an architecture that adequately supports these requirements. Fog computing [9,10] allows to perform early analytics and so reduce the latency for delay sensitive applications, while cloud computing [11,12] enables to store, process, and manage large amounts of data (Big Data). Thus, an architecture based on the combination of both fog and cloud computing paradigms (see Fig.…”

Section: Introductionmentioning

confidence: 99%

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Fog to cloud and network coded based architecture: Minimizing data download time for smart mobility

Peralta

Garrido

Bilbao

et al. 2020

Simulation Modelling Practice and Theory

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Industry 4.0 applications foster new business opportunities, but they also pose new and challenging requirements, such as low latency communications and highly reliable systems. They would likely exploit novel wireless technologies (5G), but it would also become crucial using architectures that appropriately support them. In this sense, the combination of fog and cloud computing represents a potential solution, since it can dynamically allocate the workload depending on the specific needs of each application. In this paper, our main goal is to provide a highly reliable and dynamic architecture, which minimizes the time that an end node or user, spends in downloading the required data. In order to achieve this, we have developed an optimal distribution algorithm that decides the amount of information that should be stored at, or retrieved from, each node, to minimize the overall data download time. Our scheme is based on various parameters and it exploits Network Coding (NC) as a tool for data distribution, as a key enabler of the proposed solution. We compare the performance of the proposed scheme with other alternative solutions, and the results show that there is a clear gain in terms of the download time.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fog to cloud and network coded based architecture: Minimizing data download time for smart mobility

Peralta

Garrido

Bilbao

et al. 2020

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

show abstract

“…The almost infinite number of networked sources of sensor data, the databases residing on servers in the cloud, the growing availability of massive data flows, the development of smart data analytics approaches, the emergence of run time adapting system controls, and the other technologies shown in Figure 1, are the main enablers of the radical paradigm shift that is already observable in the academic research [11,12]. This shift encourages us to look at smart CPSs as the transformative systems of the near future.…”

Section: An Evolutionary View On Cyber-physical Systemsmentioning

confidence: 99%

A Computational Framework for Procedural Abduction Done by Smart Cyber-Physical Systems

Horváth

2018

Designs

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To be able to provide appropriate services in social and human application contexts, smart cyber-physical systems (S-CPSs) need ampliative reasoning and decision-making (ARDM) mechanisms. As one option, procedural abduction (PA) is suggested for self-managing S-CPSs. PA is a knowledge-based computation and learning mechanism. The objective of this article is to provide a comprehensive description of the computational framework proposed for PA. Towards this end, first the essence of smart cyber-physical systems is discussed. Then, the main recent research results related to computational abduction and ampliative reasoning are discussed. PA facilitates beliefs-driven contemplation of the momentary performance of S-CPSs, including a ‘best option’-based setting of the servicing objective and realization of any demanded adaptation. The computational framework of PA includes eight clusters of computational activities: (i) run-time extraction of signals and data by sensing, (ii) recognition of events, (iii) inferring about existing situations, (iv) building awareness of the state and circumstances of operation, (v) devising alternative performance enhancement strategies, (vi) deciding on the best system adaptation, (vii) devising and scheduling the implied interventions, and (viii) actuating effectors and controls. Several cognitive algorithms and computational actions are used to implement PA in a compositional manner. PA necessitates not only a synergic interoperation of the algorithms, but also an objective-dependent fusion of the pre-programmed and the run time acquired chunks of knowledge. A fully fledged implementation of PA is underway, which will make verification and validation possible in the context of various smart CPSs.

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“…The integration of computing, communication, and control technologies has led to developments in intelligent sensors and a great level of integration between the software and the physical world [3]. The term Cyber Physical Systems (CPS) was devised by Lee [4] motivated by the relevance of the interactions between computation and physical worlds, encouraging real-time information extraction, transmission and analysis for intelligent monitoring, decision-making and control [5]. In this context, CPS, and more specifically the Internet of Things (IoT), are opening a new dimension for innovation, higher levels of integration in enterprise networks, and consequently challenging their business strategies.…”

Section: Cyber Physical Systemsmentioning

confidence: 99%

A Decision-Support Tool to Deal with the Strategies Alignment Process in Collaborative Networks

Andrés

Mula

Rosas

et al. 2016

Technological Innovation for Cyber-Physical Systems

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The alignment of strategies among the enterprises that belong to collaborative networks is of increasing importance due to the influence on the networks operation success in the long term. This paper proposes a Decision-Support Tool for Strategies Alignment (DST-SA) to support SMEs in the selection of strategies that allow higher levels of alignment amongst all the strategies formulated by each partner. The DST-SA includes a mathematical model, a simulation software, and a programmed application, to address the strategies alignment process from a collaborative perspective. The result of the DST-SA is the identification of the strategies that are aligned and the proper time to activate these strategies with the main aim of obtaining higher levels of network performance.

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Cloud-assisted industrial cyber-physical systems: An insight

Cited by 132 publications

References 55 publications

Fog to cloud and network coded based architecture: Minimizing data download time for smart mobility

Fog to cloud and network coded based architecture: Minimizing data download time for smart mobility

A Computational Framework for Procedural Abduction Done by Smart Cyber-Physical Systems

A Decision-Support Tool to Deal with the Strategies Alignment Process in Collaborative Networks

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