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工业物联网（industrial internet of things,IIoT）场景利用大量通信及工业设备的交互协同实现各类工业自动化应用,其下的信道具有多频段、强反射、密集散射、移动遮挡等特点。准确建立可表征上述特点的路径损耗模型存在巨大挑战,它是构建该场景下可靠无线通信链路的关键。本文基于射线追踪结果,利用统计方法对IIoT场景下复杂信道的路径损耗模型进行建模与研究. 首先,提出适用于多频段的路径损耗模型并构建与频率线性相关的隔断衰减因子;其次,引入距离、频率以及传播条件相关的金属机器路径损耗修正项,用于描述大型金属机器遮挡或密集金属散射体带来的额外路径损耗;最后,探索工业机器人运动对路径损耗的影响。本文所提出模型形式简洁,与射线追踪结果吻合度高,能够很好地描述IIoT场景下信道的多种特点对信道路径损耗的影响。本文研究结果可为IIoT场景下的链路预算和热点布设提供重要信息。 相似文献
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工业物联网的出现使工业数据安全备受关注,数据采集的安全直接关系到了工业互联网的数据安全;数据采集系统中,数据采集(南向)和云端通信(北向)协议及数据采集系统运行环境是数采终端最主要的安全攻击目标;在对工业物联数采安全终端主流北向MQTT协议和南向OPC UA协议的安全性及数采系统可信运行环境进行分析后,在国产处理器E2000D安全可信运行环境上基于OpenSSL库设计并实现了支持北向MQTT和南向OPC UA协议的工业数采安全终端;通过试验测试表明,该工业物联数采安全终端在安全认证、访问控制、数据完整性和数据机密性方面都有较高的安全性能. 相似文献
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针对智慧云仓货物信息量大、易出现账物不符等库存管理问题, 迫切需要将无人机(unmanned aerial vehicle, UAV)和工业物联网(industrial Internet of things, IIoT)集成起来, 为仓储精细化管理提供解决方案。首先, 分析盘库作业数据采集与信息交互运行机制, 以危险避障和数据采集为约束函数, 考虑了UAV在加速、减速、匀速、转角等飞行条件下的能耗差异, 并以能耗最低和时间最短为目标函数构造UAV盘库作业数学模型; 然后, 设计了差分迁移-分段变异生物地理学优化(differential migration-piecewise mutation-biogeography-based optimization, DPBBO)算法对上述模型进行优化解算; 最后, 进行了仿真实验验证。结果表明: DPBBO算法对解决该盘库作业问题的效果较优, 可以提升库存抽检任务的时效性和库存管理的准确性。 相似文献
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随着工业4.0时代的到来,数字化技术在工业电气制造中的应用越来越广泛。文中系统地阐述了数字化技术的分类与发展历程,解析了可编程逻辑控制器（Programmable Logic Controller,PLC）、数据采集与监控（Supervisory Control And Data Acquisition,SCADA）系统的应用,探讨了工业物联网（Industrial Internet of Things,IIoT）、大数据分析及数字孪生技术在电气制造中的应用。最后,通过案例分析和实际效果评估,分析了数字化技术在工业电气制造领域的意义与价值。 相似文献
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Ying Duan Wenfeng Li Xiuwen Fu Yun Luo Lin Yang 《IEEE/CAA Journal of Automatica Sinica》2018,5(1):74-82
As communication technology and smart manufacturing have developed, the industrial internet of things (IIoT) has gained considerable attention from academia and industry. Wireless sensor networks (WSNs) have many advantages with broad applications in many areas including environmental monitoring, which makes it a very important part of IIoT. However, energy depletion and hardware malfunctions can lead to node failures in WSNs. The industrial environment can also impact the wireless channel transmission, leading to network reliability problems, even with tightly coupled control and data planes in traditional networks, which obviously also enhances network management cost and complexity. In this paper, we introduce a new software defined network (SDN), and modify this network to propose a framework called the improved software defined wireless sensor network (improved SD-WSN). This proposed framework can address the following issues. 1) For a large scale heterogeneous network, it solves the problem of network management and smooth merging of a WSN into IIoT. 2) The network coverage problem is solved which improves the network reliability. 3) The framework addresses node failure due to various problems, particularly related to energy consumption. Therefore, it is necessary to improve the reliability of wireless sensor networks, by developing certain schemes to reduce energy consumption and the delay time of network nodes under IIoT conditions. Experiments have shown that the improved approach significantly reduces the energy consumption of nodes and the delay time, thus improving the reliability of WSN. 相似文献
6.
Connectivity and data exchange are key features of Industry 4.0. In this paradigm, (Industrial) Internet of Things ((I)IoT) devices are a vital component facilitating the collection and transmission of environmental data from the physical system to the central station for processing and analysis (digital twin [DT]). However, although (I)IoT devices play a critical role in this process, they are not inherently equipped to run strong encryption mechanisms to secure the data they transmit over wired or wireless channels. This research aims to explore the potential of DTs in securing Industry 4.0 applications and the security mechanism employed to ensure confidentiality, integrity, and authentication of data communicated between (I)IoT and DT through a systematic literature review (SLR). This SLR, based on the analysis of 67 papers published between 2018 and 2023, underscores the evolving significance of DT technology, particularly within the ambit of Industry 4.0. The findings illuminate the pervasive influence of DT technology across multiple industrial sectors. The result SLR revealed that DT is growing and being widely adopted as a security tool particularly in Industry 4.0 using enabling technology like machine learning, data analytics, blockchain, and 5G networks to provide security solutions such as intrusion detection, vulnerability assessment, cyber range, and threat intelligence. 相似文献
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针对工业物联网(IIoT)设备资源有限和边缘服务器资源动态变化导致的任务协同计算效率低等问题,该文提出一种工业物联网中数字孪生(DT)辅助任务卸载算法。首先,该算法构建了云-边-端3层数字孪生辅助任务卸载框架,在所创建的数字孪生层中生成近似最佳的任务卸载策略。其次,在任务计算时间和能量的约束下,从时延的角度研究了计算卸载过程中用户关联和任务划分的联合优化问题,建立了最小化任务卸载时间和服务失败惩罚的优化模型。最后,提出一种基于深度多智能体参数化Q网络(DMAPQN)的用户关联和任务划分算法,通过每个智能体不断地探索和学习,以获取近似最佳的用户关联和任务划分策略,并将该策略下发至物理实体网络中执行。仿真结果表明,所提任务卸载算法有效降低了任务协同计算时间,同时为每个计算任务提供近似最佳的卸载策略。 相似文献
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The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks. The proposed anomaly-based IDS is divided into three phases: pre-processing, feature selection, and classification. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection phase, the candidates’ feature vectors are computed using two feature reduction techniques, minimum redundancy maximum relevance and neighborhood components analysis. For the final step, the modeling phase, the following classifiers are used to perform the classification: support vector machine, decision tree, k-nearest neighbors, and linear discriminant analysis. The proposed work uses a new data-driven IIoT data set called X-IIoTID. The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%, a sensitivity rate of 99.59%, a specificity rate of 99.58%, and a low false positive rate of 0.4%. 相似文献
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工业设备接入网络实现生产自动化的过程中数据量级快速增长,而边缘层设备资源有限,无法完成全部任务请求。针对边缘层设备合理高效处理端设备任务请求的问题,提出了一种基于多跳计算卸载方法的物联网边缘网关（Internet of Things Edge Gateway,IoTEG）框架。该框架要求数据优先在网关侧处理以降低时延和保护隐私。首先,该框架根据端设备任务流特点将其分为时敏和非时敏两类。其次,设计了任务轮转调度处理机制,对任务流按时延要求高低进行处理。最后,设计了基于实时网络资源、实时本地资源和任务类型的最优联合计算卸载策略。实验结果表明,IoTEG框架能有效提高任务卸载的成功率,并能够高效处理不同类型的任务。 相似文献
10.
With the rapid development of the Internet of things (IoT) and mobile communication technology, the amount of data related to industrial Internet of things (IIoT) applications has shown a trend of explosive growth, and hence edge-cloud collaborative environment becomes one of the most popular paradigms to place the IIoT applications data. However, edge servers are often heterogeneous and capacity limited while having lower access delay, so there is a contradiction between capacity and latency while using edge storage. Additionally, when IIoT applications deployed crossing edge regions, the impact of data replication and data privacy should not be ignored. These factors often pose challenges to proposing an effective data placement strategy to take full advantage of edge storage. To address these challenges, an effective data placement strategy for IIoT applications is designed in this article. We first analyze the data access time and data placement cost in an edge-cloud collaborative environment, with the consideration of data replication and data privacy. Then, we design a data placement strategy based on