IIoT环境下基于聚类的工作流多雾协同调度算法

为解决在IIoT(industrial internet of things)环境下,现有的调度算法调度工作流中通信频繁、数据传输量大的任务所带来的完工时间上升、成本增加等影响的问题,提出一种基于聚类的工作流多雾协同调度算法。通过二分K均值算法对工作流中的任务进行聚类,基于聚类结果,在多个雾服务器之间使用改进的免疫粒子群优化算法进行任务调度。实验结果表明,该算法相比其它一些传统的调度算法在完工时间、成本、负载均衡方面都有一定提升。

Data Secure Storage Mechanism for IIoT Based on Blockchain

With the development of Industry 4.0 and big data technology,the Industrial Internet of Things(IIoT)is hampered by inherent issues such as privacy,security,and fault tolerance,which pose certain challenges to the rapid development of IIoT.Blockchain technology has immutability,decentralization,and autonomy,which can greatly improve the inherent defects of the IIoT.In the traditional blockchain,data is stored in a Merkle tree.As data continues to grow,the scale of proofs used to validate it grows,threatening the efficiency,security,and reliability of blockchain-based IIoT.Accordingly,this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of data.To solve this problem,a new Vector Commitment(VC)structure,Partition Vector Commitment(PVC),is proposed by improving the traditional VC structure.Secondly,this paper uses PVC instead of the Merkle tree to store big data generated by IIoT.PVC can improve the efficiency of traditional VC in the process of commitment and opening.Finally,this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of experiments.This mechanism can greatly reduce communication loss and maximize the rational use of storage space,which is of great significance for maintaining the security and stability of blockchain-based IIoT.

Cloud control for IIoT in a cloud-edge environment

The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment 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 algo-rithms.

面向工业物联网的策略隐藏属性基加密方案

针对工业物联网中的生产和监管数据易泄露与数据获取中断的问题,提出支持策略隐藏的属性基加密方案。将属性信息分为属性名和属性值进行方案构造,属性值在构造过程中并未暴露并且上传到云服务器的访问策略仅由属性名构成,工业物联网设备在获取生产信息时不会完整的访问策略,防止了不法分子对设备信息盗取进而导致访问策略及生产信息的泄露。同时,授权多个半可信云服务器,当某个半可信云服务器不能正常工作时可快速根据服务器密钥更新用户私钥,使得其他半可信云服务器接替其工作,保证物联网设备读取数据的连续性。此外,引入属性认证,在撤销过程中无需重更新密文,适用于产生大量数据的物联网环境的撤销。经过安全性分析和性能分析,该方案能抵抗选择明文攻击,且系统初始化、用户密钥生成、加密、解密都具有较高的运行效率。

基于区块链的边缘计算IIoT架构研究

在智能制造系统中,工业物联网通过先进的管理技术将制造设备互连,实现了信息的实时传输、设备的范在化感知和数据的快速分析处理。但是由于制造设备的异构性、物联网网关(Io T网关)数据分析能力的有限性、制造设备的存储力低下、设备和数据的低安全性等缺陷严重阻碍了智能制造的发展。BEIIoT架构从制造企业的实际生产过程与应用角度出发,将区块链技术与边缘计算相结合,通过对服务器进行P2P组网以实现对设备去中心化管理;通过对边缘设备进行服务化封装,增强设备的安全性与实时分析能力,降低设备的异构性;使用DAG双链式数据存储结构,提高数据的冗余度与安全性,实现生产线数据的异步并发备份存储。BEIIoT架构为智能制造的实施提供了体系支持。

Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

Rapid advancements of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)pose serious security issues by revealing secret data.Therefore,security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real time.Practically,AI techniques can be utilized to design image steganographic techniques in IIoT.In addition,encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized access.In order to accomplish secure data transmission in IIoT environment,this study presents novel encryption with image steganography based data hiding technique(EISDHT)for IIoT environment.The proposed EIS-DHT technique involves a new quantum black widow optimization(QBWO)to competently choose the pixel values for hiding secrete data in the cover image.In addition,the multi-level discrete wavelet transform(DWT)based transformation process takes place.Besides,the secret image is divided into three R,G,and B bands which are then individually encrypted using Blowfish,Twofish,and Lorenz Hyperchaotic System.At last,the stego image gets generated by placing the encrypted images into the optimum pixel locations of the cover image.In order to validate the enhanced data hiding performance of the EIS-DHT technique,a set of simulation analyses take place and the results are inspected interms of different measures.The experimental outcomes stated the supremacy of the EIS-DHT technique over the other existing techniques and ensure maximum security.

基于IIoT的西门子S7-1200 PLC线上实训教学平台设计

该文主要论述了基于工业物联网技术,应用于西门子S7-1200 PLC实训课程教学的线上实训平台的开发。平台包含由PLC、工业互联网网关等组成的硬件平台和由多个PLC典型教学实训项目监控界面组成的软件平台。平台能够实现西门子S7-1200 PLC的线上远程操作和多个PLC教学实训项目的线上实训。平台成功应用于PLC相关课程线上教学中,对提高学生的西门子S7-1200 PLC控制系统设计和调试的技能水平及综合应用的能力有显著作用。

A multi-point collaborative DDoS defense mechanism for IIoT environment

Nowadays,a large number of intelligent devices involved in the Industrial Internet of Things(IIoT)environment are posing unprecedented cybersecurity challenges.Due to the limited budget for security protection,the IIoT devices are vulnerable and easily compromised to launch Distributed Denial-of-Service(DDoS)attacks,resulting in disastrous results.Unfortunately,considering the particularity of the IIoT environment,most of the defense solutions in traditional networks cannot be directly applied to IIoT with acceptable security performance.Therefore,in this work,we propose a multi-point collaborative defense mechanism against DDoS attacks for IIoT.Specifically,for the single point DDoS defense,we design an edge-centric mechanism termed EdgeDefense for the detection,identification,classification,and mitigation of DDoS attacks and the generation of defense information.For the practical multi-point scenario,we propose a collaborative defense model against DDoS attacks to securely share the defense information across the network through the blockchain.Besides,a fast defense information sharing mechanism is designed to reduce the delay of defense information sharing and provide a responsive cybersecurity guarantee.The simulation results indicate that the identification and classification performance of the two machine learning models designed for EdgeDefense are better than those of the state-of-the-art baseline models,and therefore EdgeDefense can defend against DDoS attacks effectively.The results also verify that the proposed fast sharing mechanism can reduce the propagation delay of the defense information blocks effectively,thereby improving the responsiveness of the multi-point collaborative DDoS defense.

A multi-resource scheduling scheme of Kubernetes for IIoT

With the rapid development of data applications in the scene of Industrial Internet of Things(IIoT),how to schedule resources in IIoT environment has become an urgent problem to be solved.Due to benefit of its strong scalability and compatibility,Kubernetes has been applied to resource scheduling in IIoT scenarios.However,the limited types of resources,the default scheduling scoring strategy,and the lack of delay control module limit its resource scheduling performance.To address these problems,this paper proposes a multi-resource scheduling(MRS)scheme of Kubernetes for IIoT.The MRS scheme dynamically balances resource utilization by taking both requirements of tasks and the current system state into consideration.Furthermore,the experiments demonstrate the effectiveness of the MRS scheme in terms of delay control and resource utilization.

Optimization Scheme of Trusted Task Offloading in IIoT Scenario Based on DQN

With the development of the Industrial Internet of Things(IIoT),end devices(EDs)are equipped with more functions to capture information.Therefore,a large amount of data is generated at the edge of the network and needs to be processed.However,no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing(MEC)devices,the data is short of security and may be changed during transmission.In view of this challenge,this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security.Blockchain technology is adopted to ensure data consistency.Meanwhile,to reduce the impact of low throughput of blockchain on task offloading performance,we design the processes of consensus and offloading as a Markov decision process(MDP)by defining states,actions,and rewards.Deep reinforcement learning(DRL)algorithm is introduced to dynamically select offloading actions.To accelerate the optimization,we design a novel reward function for the DRL algorithm according to the scale and computational complexity of the task.Experiments demonstrate that compared with methods without optimization,our mechanism performs better when it comes to the number of task offloading and throughput of blockchain.

WebSocket协议在IIoT领域的应用优势

作为网络层协议,WebSocket对比Ajax有诸多优势:支持服务器端向客户端推送功能,接入API更加容易,实时性更好。客户端与服务器建立保持型连接,可以持续传送消息。WebSocket协议数据包的首部信息量和通信量更少,减少硬件资源消耗,减轻网络负担。Web版安东系统采用WebSocket协议,实现客户端与服务器的实时数据交互,满足自动化产线数据透明的管理需求。

基于IIoT思维的智能工厂架构及实践

依据目前新兴工业物联网、大数据、AR增强现实、ML机器学习等技术的发展,为满足客户需求与挑战,由浅入深地阐述了智能工厂的架构、功能、特点以及实践应用场景。重点强调智能工厂是由3个维度智能化构成的,而且是针对不同的人群而言的,操作维度智能化是基础,满足了专业人员的智能化需求;运营维度智能化是核心,满足了管理人员的智能化需求;决策维度智能化是提升和增强,满足了决策人员的智能化需求。智能工厂的建设,消除了信息孤岛,使数据互联互通,业务敏捷协同,决策实时科学。

无线IIoT:迈向下一代工业互联

制造业、电力传输和公用事业(燃气、电、水)等工业部门的运营和环境条件总是对互连构成挑战。当存在清晰明确的网络需求时,通常使用有线连接,从而确保关键任务系统的可靠性。无线更多用于提供临时点对点连接,尤其是在有线基础设施缺乏或不易实现的工业环境中。从历史上看,两种网络类型互连在一起并不常见,但互联网改变了这一切。

工业物联网IIoT在制造中的要求、框架与价值

一、对工业物联网IIoT的要求由于通常要求IIoT能自治或自主的运用,并能完成同层内点对点的分布式控制,因此,必须对IIoT提出更严格的、远高于HIoT和M2M的要求。这些要求是:自主控制(自治控制);同层内点对点的控制;工业强度的可靠性;工业级的信息安全;IIoT的解决方案应能让原有的设备和网络(不论其使用何种协议)与新添的设备和网络一样。

Edge Cloud Selection in Mobile Edge Computing(MEC)-Aided Applications for Industrial Internet of Things(IIoT)Services

In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency.There are two types of costs for this kind of IoT network:a communication cost and a computing cost.For service efficiency,the communication cost of data transmission should be minimized,and the computing cost in the edge cloud should be also minimized.Therefore,in this paper,the communication cost for data transmission is defined as the delay factor,and the computing cost in the edge cloud is defined as the waiting time of the computing intensity.The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs.That is,a device chooses a routing path to the selected edge cloud based on the costs.The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load.The performance of the proposed method is validated through extensive computer simulation.When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states,the proposed method reduced both the average delay and the service pause counts to about 25%of the existing method.

Cloud computing-enabled IIOT system for neurosurgical simulation using augmented reality data acces

In recent years,statistics have indicated that the number of patients with malignant brain tumors has increased sharply.However,most surgeons still perform surgical training using the traditional autopsy and prosthesis model,which encounters many problems,such as insufficient corpse resources,low efficiency,and high cost.With the advent of the 5G era,a wide range of Industrial Internet of Things(IIOT)applications have been developed.Virtual Reality(VR)and Augmented Reality(AR)technologies that emerged with 5G are developing rapidly for intelligent medical training.To address the challenges encountered during neurosurgery training,and combining with cloud computing,in this paper,a highly immersive AR-based brain tumor neurosurgery remote collaborative virtual surgery training system is developed,in which a VR simulator is embedded.The system enables real-time remote surgery training interaction through 5G transmission.Six experts and 18 novices were invited to participate in the experiment to verify the system.Subsequently,the two simulators were evaluated using face and construction validation methods.The results obtained by training the novices 50 times were further analyzed using the Learning Curve-Cumulative Sum(LC-CUSUM)evaluation method to validate the effectiveness of the two simulators.The results of the face and content validation demonstrated that the AR simulator in the system was superior to the VR simulator in terms of vision and scene authenticity,and had a better effect on the improvement of surgical skills.Moreover,the surgical training scheme proposed in this paper is effective,and the remote collaborative training effect of the system is ideal.

Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach

Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)applications.For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for CE.Firstly,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction problem.Then,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse adaptation.The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.

涂装车间IIoT边缘容器化的实现探索

传统SCADA应用正在从纯粹OT平台,升级成结合最新工业物联网平台技术、高度集成MES系统功能的新系统。分析了SCADA及工业物联网平台面临的挑战,提出了容器化的工业物联网边缘设计思路。根据工业物联网边缘应用要解决的问题,设计了双环路模型结构,并分析了在边缘应用中采用微服务架构及容器技术能带来的优势。通过对温湿度控制智能应用的微服务架构及组件容器化部署的说明,展示了双环路模型的应用。

Robust Attack Detection Approach for IIoT Using Ensemble Classifier

Generally,the risks associated with malicious threats are increasing for the Internet of Things(IoT)and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices.Thus,anomaly-based intrusion detection models for IoT networks are vital.Distinct detection methodologies need to be developed for the Industrial Internet of Things(IIoT)network as threat detection is a significant expectation of stakeholders.Machine learning approaches are considered to be evolving techniques that learn with experience,and such approaches have resulted in superior performance in various applications,such as pattern recognition,outlier analysis,and speech recognition.Traditional techniques and tools are not adequate to secure IIoT networks due to the use of various protocols in industrial systems and restricted possibilities of upgradation.In this paper,the objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT network.In the first phase,SVM and Naïve Bayes,are integrated using an ensemble blending technique.K-fold cross-validation is performed while training the data with different training and testing ratios to obtain optimized training and test sets.Ensemble blending uses a random forest technique to predict class labels.An Artificial Neural Network(ANN)classifier that uses the Adam optimizer to achieve better accuracy is also used for prediction.In the second phase,both the ANN and random forest results are fed to the model’s classification unit,and the highest accuracy value is considered the final result.The proposed model is tested on standard IoT attack datasets,such as WUSTL_IIOT-2018,N_BaIoT,and Bot_IoT.The highest accuracy obtained is 99%.A comparative analysis of the proposed model using state-of-the-art ensemble techniques is performed to demonstrate the superiority of the results.The results also demonstrate that the proposed model outperforms traditional techniques and thus improves the reliability of an IIoT network.

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)applications.In order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed spectrum.However,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection.Specifically,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing path.This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain.Thus,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability Probability.Moreover,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique.This protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation model.Also,it periodically updates and stores the routing table for optimal route decision-making.Moreover,in order to achieve higher throughput and lower delay,a new routing metric is presented.To evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a benchmark.The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achieves high routing performance in finding a robust route,selecting the high channel stability,and reducing the probability of PU interference for continued communication.