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.

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.

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.

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.

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.

An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

Predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions.An integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge.This study presents an IIoT-based sensor node for industrial motors.The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms.The initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple sensors.The proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main brokers.The broker’s bridging allows for data backup in case of connection loss.The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial environments.The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults.The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.

Wrapper Based Linear Discriminant Analysis(LDA)for Intrusion Detection in IIoT

The internet has become a part of every human life.Also,various devices that are connected through the internet are increasing.Nowadays,the Industrial Internet of things(IIoT)is an evolutionary technology interconnecting various industries in digital platforms to facilitate their development.Moreover,IIoT is being used in various industrial fields such as logistics,manufacturing,metals and mining,gas and oil,transportation,aviation,and energy utilities.It is mandatory that various industrial fields require highly reliable security and preventive measures against cyber-attacks.Intrusion detection is defined as the detection in the network of security threats targeting privacy information and sensitive data.Intrusion Detection Systems(IDS)have taken an important role in providing security in the field of computer networks.Prevention of intrusion is completely based on the detection functions of the IDS.When an IIoT network expands,it generates a huge volume of data that needs an IDS to detect intrusions and prevent network attacks.Many research works have been done for preventing network attacks.Every day,the challenges and risks associated with intrusion prevention are increasing while their solutions are not properly defined.In this regard,this paper proposes a training process and a wrapper-based feature selection With Direct Linear Discriminant Analysis LDA(WDLDA).The implemented WDLDA results in a rate of detection accuracy(DRA)of 97%and a false positive rate(FPR)of 11%using the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)dataset.

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。

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.

A Novel Approach for Network Vulnerability Analysis in IIoT

Industrial Internet of Things(IIoT)offers efficient communication among business partners and customers.With an enlargement of IoT tools connected through the internet,the ability of web traffic gets increased.Due to the raise in the size of network traffic,discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues.A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification(MPDQDJREBC)is introduced for accurate attack detection wi th minimum time consumption in IIoT.The proposed MPDQDJREBC technique includes feature selection and categorization.First,the network traffic features are collected from the dataset.Then applying the Maximum Posterior Dichotomous Quadratic Discriminant analysis to find the significant features for accurate classification and minimize the time consumption.After the significant features selection,classification is performed using the Jaccardized Rocchio Emphasis Boost technique.Jaccardized Rocchio Emphasis Boost Classification technique combines the weak learner result into strong output.Jaccardized Rocchio classification technique is considered as the weak learners to identify the normal and attack.Thus,proposed MPDQDJREBC technique gives strong classification results through lessening the quadratic error.This assists for proposed MPDQDJREBC technique to get better the accuracy for attack detection with reduced time usage.Experimental assessment is carried out with UNSW_NB15 Dataset using different factors such as accuracy,precision,recall,F-measure and attack detection time.The observed results exhibit the MPDQDJREBC technique provides higher accuracy and lesser time consumption than the conventional techniques.

IIoT和云计算在智能制造业中的应用

在过去的数十年里,物联网(internet of things,IoT)已经成为一个全新的发展范式,在无线通信和微电子技术等领域都获得了极大成就,并受到了社会重视。与传统物联网不同,工业产生的大数据随时代的发展呈指数级增长,需要实时智能处理。为提高工业物联网(industrial internet of things,IIoT)系统的实时性与能源效率,引入云计算技术来分担IIoT中过重的计算任务。本文分析了IIoT及云计算对智能制造业发展产生的影响,阐述了智能传感器、设备和应用程序在提高生产力、简化业务操作中的应用,还概述了实现智能制造的安全控制和实践,为物联网和云计算在智能制造领域的发展模式提供思路。

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。

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。

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.

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.

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.

Digital twins and multi-access edge computing for IIoT

Background All recent technological findings can be collectively used to strengthen the industrial Internet of things(IIoT)sector.The novel technology of multi-access edge computing or mobile edge computing(MEC)and digital twins have advanced rapidly in the industry.MEC is the middle layer between mobile devices and the cloud,and it provides scalability,reliability,security,efficient control,and storage of resources.Digital twins form a communication model that enhances the entire system by improving latency,overhead,and energy consumption.Methods The main focus in this study is the biggest challenges that researchers in the field of IIoT have to overcome to obtain a more efficient communication environment in terms of technology integration,efficient energy and data delivery,storage spaces,security,and real-time control and analysis.Thus,a distributed system is established in a local network,in which several functions operate.In addition,an MEC-based framework is proposed to reduce traffic and latency by merging the processing of data generated by IIoT devices at the edge of the network.The critical parts of the proposed IIoT system are evaluated by using emulation software.Results The results show that data delivery and offloading are performed more efficiently,energy consumption and processing are improved,and security,complexity,control,and reliability are enhanced.Conclusions The proposed framework and application provide authentication and integrity to end users and IoT devices.

Blockchain Based Secure Solution for Cloud Storage:A Model for Synchronizing Industry 4.0 and IIoT

Industry 4.0 is one of the hot topic of today’s world where everything in the industry will be data driven and technological advancements will take place accordingly.In the fourth phase of industrial revolution,manufacturers are dependent upon data produced by the consumers to invent,innovate or change anything for the product.Internet of things devices like OBD,RFID,IIoT,Smart devices are the major source of data generation and represents trends in the industry.Since the IoT device are vulnerable to hackers due to its limitation,consumer data security should be tighten up and enhanced.This paper gives an overview of industrial revolutions as well as proposes Blockchain Cloud Computing as a solution to store data for Industry 4.0.