Fog-IBDIS: Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems

Wang, Junliang; Zheng, Peng; Lv, Youlong; Bao, Jingsong; Zhang, Jie

doi:10.1016/j.eng.2018.12.013

Cited by 48 publications

(13 citation statements)

References 31 publications

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“…Whether to execute computation migration is decided by the end-users computation migration decision module. Simulation results show that the optimal computation migration strategy can significantly reduce the task execution delay compared to the strategy of local execution of computation tasks [6,7]. However, in this scheme, the communication delay between the end-user and the MEC server is too large because the end-user needs to make the migration decision based on the feedback from the MEC server [8].…”

Section: Related Jobsmentioning

confidence: 99%

An Optimization Method for Integrating Educational Information Resources Based on Edge Computing

Hua

2022

Journal of Sensors

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This paper adopts an edge computing approach to conduct in-depth research and analysis on the optimization of educational information resource integration and constructs an integrated teaching resource design model concerning the integrated teaching model, human-centered mobile learning resource design, and the interdisciplinary concept of physics subject teaching method. The learning field constituted by the model is divided into two parts, the explicit field and the potential field and then designs a five-stage teaching resource based on the model. A five-stage teaching resource development path was designed based on the model. Based on the cloud service center model, we propose a hierarchical mechanism for sharing educational information resources and analyze how each hierarchical entity constructs and shares resources and the rights and responsibilities of each hierarchical entity; we explain the meaning and functions of the personalized educational resource integrated development environment provided by the cloud service center for users. The dynamic evaluation model of the value of educational information resources is summarized and proposed for the resource exchange behavior in the sharing of educational information resources, and the significance of the calculation method of resource value, parameter values, and resource value difference for resource sharing is introduced. Firstly, the mechanism for coconstruction of educational information resources at different levels is proposed, and the construction tasks of subjects at different levels in the process of coconstruction and sharing of resources are elaborated. The sharing mechanism of educational information resources covering the regularization system, evaluation mechanism, incentive mechanism, problem handling mechanism, and supporting service mechanism is proposed, and a dynamic evaluation model of the value of educational information resources is designed to improve the enthusiasm of the coconstruction and sharing subjects.

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Section: Related Jobsmentioning

confidence: 99%

An Optimization Method for Integrating Educational Information Resources Based on Edge Computing

Hua

2022

Journal of Sensors

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“…For future work, we will explore the multiuser multitask model and further evaluate the effectiveness and applicableness of research scheme in the real-world applications, especially for real industrial applications. 30,31…”

Section: Discussionmentioning

confidence: 99%

“…Finally, the numerical results demonstrate that our proposed scheme gains significant performance advantages on the completion time, energy consumption, convergence speed, and accuracy. For future work, we will explore the multiuser multitask model and further evaluate the effectiveness and applicableness of research scheme in the real‐world applications, especially for real industrial applications 30,31 …”

Section: Discussionmentioning

confidence: 99%

Latency‐minimum offloading decision and resource allocation for fog‐enabled Internet of Things networks

Wang

Chen

2020

Trans Emerging Tel Tech

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The rapid growth of the number of sensing devices enables computation offloading to be a promising solution to alleviate the burden of core network communication and provide low delay services, especially for those computation-intensive and delay-sensitive tasks. For meeting the processing requirements of these tasks sufficiently, a latency-minimum offloading decision and resource allocation scheme for fog-enabled Internet of Things (IoT) networks is developed in this article. Specifically, we formulate a joint optimization problem of the offloading decision, the local computation capability, and the computing resource allocation of fog node to minimize the task completion time with energy constraint, in which practically considers M/M/1 waiting queues in the wireless channel and fog node. To solve this mixed integer nonlinear programming problem with low complexity, we first calculate the optimal values of local computation capability and computing resource allocation of fog node by decomposing the original optimization problem into two independent subproblems. Subsequently, we propose a hybrid genetic simulated annealing-based latency-minimum offloading decision algorithm to optimize the offloading decision. Finally, the numerical results verify that our proposed scheme achieves significant advantages compared to other alternative schemes in terms of completion time and energy consumption, and they also confirm the advantage of convergence speed and quality.

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“…A fog industrial big data integration and sharing (fog-IBDIS) platform is proposed in [ 21 ] in order to protect raw data privacy and enable data integration and sharing. They present a case study that illustrates the implementation of fog-IBDIS, which ensures raw data security by deploying the analysis tasks executed by the data generators.…”

Section: Related Workmentioning

confidence: 99%

Software Architecture of a Fog Computing Node for Industrial Internet of Things

Ungurean

Gaitan

2021

Sensors

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In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

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Fog-IBDIS: Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems

Cited by 48 publications

References 31 publications

An Optimization Method for Integrating Educational Information Resources Based on Edge Computing

An Optimization Method for Integrating Educational Information Resources Based on Edge Computing

Latency‐minimum offloading decision and resource allocation for fog‐enabled Internet of Things networks

Software Architecture of a Fog Computing Node for Industrial Internet of Things

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