Cloud Control for IIoT in a Cloud-Edge Environment

IF 1.9 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS
Ce Yan, Yuanqing Xia, Hongjiu Yang, Yufeng Zhan
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引用次数: 0

Abstract

The industrial Internet of Things (IIoT) is a new industrial idea that combines the latest information and communication technologies with the industrial economy. In this paper, a cloud control structure is designed for IIoT in cloud-edge environment with three modes of 5G. For 5G based IIoT, the time sensitive network (TSN) service is introduced in transmission network. A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration. For a transmission control protocol (TCP) model with nonlinear disturbance, time delay and uncertainties, a robust adaptive fuzzy sliding mode controller (AFSMC) is given with control rule parameters. IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows. IIoT workflow scheduling is a non-deterministic polynomial (NP)-hard problem in cloud-edge environment. An adaptive and non-local-convergent particle swarm optimization (ANCPSO) is designed with nonlinear inertia weight to avoid falling into local optimum, which can reduce the makespan and cost dramatically. Simulation and experiments demonstrate that ANCPSO has better performances than other classical algorithms.
在云边缘环境中实现 IIoT 的云控制
工业物联网(IIoT)是将最新信息通信技术与工业经济相结合的一种新型工业理念。本文针对 5G 的三种模式,设计了云边环境下 IIoT 的云控制结构。针对基于 5G 的 IIoT,在传输网络中引入了时间敏感网络(TSN)服务。设计了一个 5G 逻辑 TSN 桥接器,用于在 5G 框架上传输 TSN 流,以实现端到端配置。针对具有非线性扰动、时间延迟和不确定性的传输控制协议(TCP)模型,给出了具有控制规则参数的鲁棒自适应模糊滑模控制器(AFSMC)。物联网工作流由一系列子任务组成,这些子任务通过传感器数据集和任务流之间的依赖关系联系在一起。在云边缘环境中,物联网工作流调度是一个非确定性多项式(NP)难题。为了避免陷入局部最优,设计了一种具有非线性惯性权重的自适应和非局部收敛粒子群优化(ANCPSO),它可以显著降低工期和成本。仿真和实验证明,ANCPSO 比其他经典算法具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Systems Engineering and Electronics
Journal of Systems Engineering and Electronics 工程技术-工程:电子与电气
CiteScore
4.10
自引率
14.30%
发文量
131
审稿时长
7.5 months
期刊介绍: Information not localized
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