Industrial Internet Of Things Systems Research Articles

Конкурентоспособность системы промышленного Интернета вещей

The article considers a new paradigm of competition, the essence of which is a combination of competition with cooperation within the framework of the interaction of ecosystems of the industrial Internet of Things. The conditions under which this cooperation is most effective are determined. The information and intellectual interaction of industrial Internet of Things ecosystems through the exchange of their services is considered. Important conditions: specialization of services of industrial Internet of Things ecosystems and the intensity of their exchange of services. The result is the generation of many business models, creating an explosion of opportunities to increase the value of the economy of the industrial Internet of Things. The task of selecting the most promising effective business models from the set of obtained business models is not trivial, the use of which ultimately gives a high competitiveness of the ecosystems involved in the exchange of services. However, no one has canceled economical, technically advanced solutions and competitive solutions at all levels of the functioning of the industrial Internet of Things system, that is, at the levels of sensors, gateways, business agents, server programs, databases of dynamic data generated by sensors, executive devices. The article offers competitive solutions for all levels of the industrial Internet of Things system, the architecture of the industrial Internet of Things ecosystem is developed. The possibilities of the system related to the connection of people to the system of the industrial Internet of Things and to the transition to the “Internet of Everything” (Internet of everything or IoE) are considered. The Internet of everything significantly increases the set of optimal business models that increase the value of a particular product.

Machine Learning-Based Time-Series Data Analysis in Edge-Cloud-Assisted Oil Industrial IoT System

With the rapid development of the Industrial Internet of Things (IIoT) and edge computing techniques, in situ intelligent sensors are continuously generating increasing and vast amounts of time-series data. In many industrial applications, particularly highly distributed systems positioned in remote areas, repeated transmission of large amounts of raw data onto the remote server is not possible. This poses a significant challenge to the timely processing of these data in IIoT. Analyzing and processing all the raw data remotely in the cloud server is impractical and has very low efficiency owing to network latency and the limited cloud computing resources. Failure of detecting abnormal data may result in major production safety problems. Therefore, businesses are moving machine learning capabilities to the edge to enable real-time actions in the field. In this study, we present a machine-learning-based edge-cloud framework to solve this problem. First, robust random cut forest and isolation forest algorithms are employed at the edge gateway to the collected data for the detection of anomalously changing data. Subsequently, these preprocessed time-series data are transmitted to cloud services for data trend prediction and missing data completion using the long short-term memory recurrent neural network method feed in conjunction with the original sequence of historical data combined with the first-order forward difference data. The experimental results show that the machine-learning-based edge-cloud-assisted oil production IIoT system can improve substantially the efficiency and accuracy of time-series data analyses.

Certificate-Based Signature Scheme for Industrial Internet of Things Using Hyperelliptic Curve Cryptography

The Industrial Internet of Things (IIoT) is a technology that uses the Internet of Things (IoT) infrastructure to sense, process, and communicate real-time events in the industrial system to cut down on unnecessary operating costs and to speed up industrial automation of internal and external working processes. Since the IIoT system inherits the same cyber-physical vulnerabilities that the IoT system already encounters, it requires additional work to address security concerns owing to its heterogeneous nature. As a result, an efficient security mechanism is essential to protect against various and unknown cyber-attacks. In this article, we propose a certificate-based signature scheme based on hyperelliptic curve cryptography (HECC), with the aim of improving security while reducing computational and communication costs in the IIoT environment. The proposed scheme outperforms existing schemes in terms of both computational and communication costs, as well as offering better security.

Cybersecurity for Industrial Internet of Things: Architecture, Models and Lessons Learned

Modern industrial systems now, more than ever, require secure and efficient ways of communication. The trend of making connected, smart architectures is beginning to show in various fields of the industry such as manufacturing and logistics. The number of IoT (Internet of Things) devices used in such systems is naturally increasing and industry leaders want to define business processes which are reliable, reproducible, and can be effortlessly monitored. With the rise in number of connected industrial systems, the number of used IoT devices also grows and with that some challenges arise. Cybersecurity in these types of systems is crucial for their wide adoption. Without safety in communication and threat detection and prevention techniques, it can be very difficult to use smart, connected systems in the industry setting. In this paper we describe two real-world examples of such systems while focusing on our architectural choices and lessons learned. We demonstrate our vision for implementing a connected industrial system with secure data flow and threat detection and mitigation strategies on real-world data and IoT devices. While our system is not an off-the-shelf product, our architecture design and results show advantages of using technologies such as Deep Learning for threat detection and Blockchain enhanced communication in industrial IoT systems and how these technologies can be implemented. We demonstrate empirical results of various components of our system and also the performance of our system as-a-whole.

Cyber-Security of Industrial Internet of Things in Electric Power Systems

Electric Power Systems (EPSs) are among the most critical infrastructures of any society, since they significantly impact other infrastructures. Recently, there has been a trend toward implementing modern technologies, such as Industrial Internet of Things (IIoT), in EPSs to enhance their real-time monitoring, control, situational awareness, and intelligence. This movement, however, has exposed EPSs to various cyber intrusions that originate from the IIoT ecosystem. Statistics show that 38% of reported attacks have been against power and water infrastructure, and so far at least 91% of power utilities have experienced a cyber-attack. The cyber-security problem is even more severe for IIoT applications in EPSs due to the vulnerabilities and resource limitations of such applications. Thus, based on the above statistics, it is necessary to investigate the vulnerabilities of IIoT-based applications in EPSs, identify probable attacks and their consequences, and develop intrusion prevention and detection approaches to secure IIoT systems. On this basis, this paper first elaborates on the applications of IIoT-based systems in EPSs, and evaluates their security challenges. Afterwards, it comprehensively reviews various cyber-attacks against IIoT-assisted EPSs, with a particular focus on attack entry points and adversarial methods. Finally, efforts to prevent cyber-intrusions against IIoT systems in EPSs are explained, and different attack detection techniques are discussed.

Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of intrusions in network. Further, PPBDL-IIoT technique also involves the design of Chaos Game Optimization (CGO) with Bidirectional Gated Recurrent Neural Network (BiGRNN) technique for both detection and classification of intrusions in the network. Besides, CGO technique is applied to fine tune the hyperparameters in BiGRNN model. CGO algorithm is applied to optimally adjust the learning rate, epoch count, and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model. Moreover, Blockchain enabled Integrity Check (BEIC) scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system. The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were analysed under various measures. The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.

MATRIX FACTORIZATION OF BIG DATA IN THE INDUSTRIAL SYSTEMS

The creation of new technologies and their implementation in various fields necessitated Big Data processing and storage. In industrial systems, modernization means the use of a large number of smart devices that perform specialized functions. Data from such devices are used to control the system and automate production processes. A change in the parameters of individual components of the manufacturing system may indicate the need to adjust the global management strategy. The intelligent industrial systems main characteristics were defined in the paper. The Industrial Internet of Things concept and the relevance of the modernization problem for manufacturing were analyzed. The problems of processing Big Data in Industrial Internet of Things systems were examined in the paper. The use of recommendation systems for quickly finding relationships between users and production services was considered. The advantages of Big Data analysis by recommendation systems, which have a favourable effect on industrial enterprise efficiency were given. The use of SVD and FunkSVD matrix factorization algorithms for processing sparse data matrices was analyzed. The possibility of optimizing arrays of information, choosing the most important, and rejecting redundancy with the help of the above algorithms was determined. The proposed algorithms were simulated. The advantages of FunkSVD for working with sparse data were assigned. It was found that the FunkSVD algorithm processes the data in a shorter time than SVD, but this does not affect the accuracy of the result. The SVD is also more difficult to implement and it requires more computing resources was established. It has been shown that FunkSVD uses a lot of data to determine the relationships between it and make recommendations about the products most likely to be of interest to users. To increase the efficiency of processing large sets of information the FunkSVD algorithm was improved in such a way that it uses fewer data to generate recommendations. Based on the results of the research, the modified method works faster than the non-modified one but retains high calculation accuracy, which is important for work in recommender systems. The possibility of providing recommendations to users of industrial systems in a shorter period, thus improving their relevance, was revealed. It was proposed to continue research for finding the optimal parameters of the FunkSVD algorithm for Big Data processing.

Integration of Hardware Security Modules and Permissioned Blockchain in Industrial IoT Networks

Hardware Security Modules (HSM) serve as a hardware based root of trust that offers physical protection while adding a new security layer in the system architecture. When combined with decentralized access technologies as Blockchain, HSM offers robustness and complete reliability enabling secured end-to-end mechanisms for authenticity, authorization and integrity. This work proposes an efficient integration of HSM and Blockchain technologies focusing on, mainly, public-key cryptography algorithms and standards, that result crucial in order to achieve a successful combination of the mentioned technologies to improve the overall security in Industrial IoT systems. To prove the suitability of the proposal and the interaction of an IoT node and a Blockchain network using HSM a proof of concept is developed. Results of time performance analysis of the prototype reveal how promising the combination of HSMs in Blockchain environments is.

Dynamical Orchestration and Configuration Services in Industrial IoT Systems: An Autonomic Approach

The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the “things” appears to be one of the biggest conceptual and technological challenges in developing the IIoT framework. Typically, collaboration at the field device level is very limited. Instead, the decision-making process is usually propagated to higher levels with substantial computational resources. This centralized architecture has been widely deployed based on global cloud infrastructure. However, sending data over the cloud for analysis may bring about privacy and security threats. Besides, network latency could be another factor that reduces adaptability. In this article, we propose a decentralized approach that applies the concepts of local automation cloud. By using semantic technologies to achieve autonomicity, the approach enables real-time monitoring of the control systems within one local cloud and automates orchestration and configuration locally through adaptation based on semantic policies. The approach is deployed and tested on a chemical production use case in which business-level policies have been used for dynamical planning for suppliers and automatic detection of malfunctioning sensors with subsequent adaptation to continuing supply planning and production as smooth as possible.

An Industrial Cloud-Based IoT System for Real-Time Monitoring and Controlling of Wastewater

Wastewater treatment is considered the most important process for reducing pollutants in wastewater to levels that nature can cope with. At many sewages treatment plants, industrial wastes cause more difficulties in the treatment process than any other single problem where the plant operators have to deal with. These plants may not be designed to handle these types of wastes and the accelerated deterioration of sewage treatment plant structures. In this paper, we propose a new industrial IoT cloud-based model for real-time wastewater monitoring and controlling. The proposed system monitors the power of hydrogen (pH) and temperature parameters from the wastewater inlet that will be treated in the wastewater treatment plant, thereby avoiding impermissible industrial wastewater that the plant cannot handle. The system collects and uploads real-time sensor readings to the cloud via an IIoT Wi-Fi Module. Additionally, it reports observed or identified unexpected industrial wastewater inlets via SMS notifications and alarms and controls the valves of the gates. This is needed to change the path of the water to the industrial wastewater treatment plant that can treat this type of wastes. Experimental work shows the effectiveness of the proposed system compared to related work.

Data protection and threat prediction in industrial IoT cloud environment

There are several cloud computing data protection systems depending on accessibility control, attribute-based encryption (ABE), security and reputations, but still, it is disjointed and lacks a cohesive rationale. I-IoT systems including data protection and threat security metrics are discussed in this paper. I-IoT layered method that is general and extended, with privacy, security features and layers detection is proposed. To protect the user's private data, security procedures and crucial management events are established amongst all the lower, moderate and higher levels. To permit data flow across the levels of the proposed cloud computed powered I-IoT paradigm, we built security certificates. The proposed systematic method eliminates potential security vulnerabilities and is also utilised in conjunction with the optimal security approaches to mitigate the security threats on cloud computed decentralised industrial IoT systems and devices. The assaults toward I-IoT systems are described in this paper, and a detailed examination of remedies to these attacks.

Data security storage mechanism based on blockchain industrial Internet of Things

In the age of Industry 5.0, the industrial Internet of Things (IIoT) system has changed from the original “cyber-physical” system to a complex “human-cyber-physical” system, data security issues become more important. Blockchain technology can be used to ensure the security of IIoT data. However, the traditional blockchain system uses Merkle trees to store data, in which the proof size is large when verifying the integrity and correctness of the data. And it is unable to perform batch verification of the data. Large size proof will bring great pressure to communication, causing end-to-end communication delays, which seriously affect the stability, efficiency, and security of IIoT system. To solve it, in the paper, the incremental aggregator subvector commitment (IASVC) is used to replace Merkle tree, which reduces the size of proof and communication consumption. Each block processes 1000 transactions, the proof size of a single data piece is 15% of the original scheme. Moreover, our scheme can realize the aggregation verification of the proof. In addition, the qualifications of data upload on nodes are set using IASVC, which can reduce the storage pressure of nodes by storing a single commitment instead of the entire qualification list.

Resource Allocation for Latency-Aware Federated Learning in Industrial Internet of Things

Federated learning (FL) has been employed for numerous privacy-sensitive applications, where distributed devices collaboratively train a global model. In industrial Internet of things (IIoT) systems, training latency is the key performance metric as the automated manufacture usually requires timely processing. The existing works increase the number of effective devices to accelerate the training. However, devices in IIoT systems are usually deployed densely; increasing the number of clients can potentially cause serious interference and prolonged training latency. In this article, we propose a resource allocation scheme for FL, namely RaFed. We formulate the problem of reducing training latency as an optimization problem, which is proved to be NP-hard. We propose a heuristic algorithm to select appropriate devices for achieving a good tradeoff between the interference and convergence time. We conduct experiments using an RGB-D dataset in an IIoT system. The results show that RaFed significantly reduces the latency by 29.9%, compared to the state-of-the-art works.

Reliability Aware Medical Resource Allocation for Health Care Industrial Internet of Things (IIoT) Using Tabu Search and Alo Algorithm

The medical data integrating system allows the hospital’s resource constraints to be more effectively utilized. Moreover, by improving the resource management and allocation method, the hospital’s operations may be more organized, and the effectiveness of healthcare can be improved without breaking the medical agreements. Significant catastrophes frequently result in a scarcity of important medical resources, hence resource allocation must be optimized to enhance the performance of relief operations. The two main requirements for healthcare industrial applications are timeliness and reliability. Therefore, in the architecture of a smart healthcare industry these two criteria should be thought carefully. A well-known approach for the security and timeliness in the intelligent healthcare industry is to utilize hybrid IoT and Cloud technologies. Yet it is not enough to protect their hard deadlines for tight time-sensitive applications utilizing cloud. A potential way to cope with efficiency and latency criteria for strict time-sensitive applications is the deployment of intermediate processing layer IoT that can be linked between healthcare industrial plant and cloud. The purpose of this article is to develop a healthcare Industrial IoT system that include a medical resource allocation scheme for dividing a certain amount of workload between those multiple computing layers which are dependable and time consuming. IOT is integration of microprocessors and controller Workload partitioning can give us important design decisions to specify how many computing resources are needed in cooperation with IoT to develop a local private cloud. Ant lion optimization (ALO) and TABU Look for the right route. The simplest method of deciding the distance to a destination is to choose an OLSR routing protocol depending on the meaning or measure it requires. The method proposed in the distribution and data storage of medical resources is very efficient.

Asset Management Method of Industrial IoT Systems for Cyber-Security Countermeasures

Cyber-security countermeasures are important for IIoT (industrial Internet of things) systems in which IT (information technology) and OT (operational technology) are integrated. The appropriate asset management is the key to creating strong security systems to protect from various cyber threats. However, the timely and coherent asset management methods used for conventional IT systems are difficult to be implemented for IIoT systems. This is because these systems are composed of various network protocols, various devices, and open technologies. Besides, it is necessary to guarantee reliable and real-time control and save CPU and memory usage for legacy OT devices. In this study, therefore, (1) we model various asset configurations for IIoT systems and design a data structure based on SCAP (Security Content Automation Protocol). (2) We design the functions to automatically acquire the detailed information from edge devices by “asset configuration management agent”, which ensures a low processing load. (3) We implement the proposed asset management system to real edge devices and evaluate the functions. Our contribution is to automate the asset management method that is valid for the cyber security countermeasures in the IIoT systems.

Energy-Efficient Industrial Internet of Things: Overview and Open Issues

The last few decades have witnessed an explosive growth of the Internet-of-Things (IoT) systems, which provide ubiquitous sensing and computing services. When adopted in industrial and manufacturing environments, IoT is referred to as the industrial IoT (IIoT), which has attracted increasing research attention. Energy efficiency is one of the most important research topics in green IIoT, as 1) the limited resource can significantly affect the lifetime of IIoT systems and 2) massive sensors, devices, machines keep consuming a considerable amount of energy, and increasing the carbon footprint. In this article, we present a comprehensive survey on energy-efficient communications and computation mechanisms in IIoT systems (such as smart grids). We categorize the existing works, review, discuss, and compare the works to explore their pros and cons. We also discuss the open issues and research challenges, considering the recent 5G communications and edge computing trends.

Machine Learning for Automated Industrial IoT Attack Detection: An Efficiency-Complexity Trade-off

Critical city infrastructures that depend on smart Industrial Internet of Things (IoT) devices have been increasingly becoming a target of cyberterrorist or hacker attacks. Although this has led to multiple studies in the recent past, there exists a paucity of literature concerning real-time Industrial IoT attack detection. The goal of this article is to build a machine-learning approach using Industrial IoT sensor readings for accurately tracking down Industrial IoT attacks in real time. We analyze IoT system behavior under a lab-controlled series of attacks on a Secure Water Treatment (SWaT) system. The system is analytically challenging in that it results in sensor readings that resemble waveforms. To that end, we develop a novel early detection method using functional shape analysis (FSA) to extract features from the data that can capture the profile of the waveform. Our results show an efficiency-complexity trade-off between functional and non-functional methods in predicting IoT attacks.

Analysis of Security Issues and Countermeasures for the Industrial Internet of Things

Industrial Internet of Things (IIoT) can be seen as an extension of the Internet of Things (IoT) services and applications to industry with the inclusion of Industry 4.0 that provides automation, reliability, and control in production and manufacturing. IIoT has tremendous potential to accelerate industry automation in many areas, including transportation, manufacturing, automobile, marketing, to name a few places. When the benefits of IIoT are visible, the development of large-scale IIoT systems faces various security challenges resulting in many large-scale cyber-attacks, including fraudulent transactions or damage to critical infrastructure. Moreover, a large number of connected devices over the Internet and resource limitations of the devices (e.g., battery, memory, and processing capability) further pose challenges to the system. The IIoT inherits the insecurities of the traditional communication and networking technologies; however, the IIoT requires further effort to customize the available security solutions with more focus on critical industrial control systems. Several proposals discuss the issue of security, privacy, and trust in IIoT systems, but comprehensive literature considering the several aspects (e.g., users, devices, applications, cascading services, or the emergence of resources) of an IIoT system is missing in the present state of the art IIoT research. In other words, the need for considering a vision for securing an IIoT system with broader security analysis and its potential countermeasures is missing in recent times. To address this issue, in this paper, we provide a comparative analysis of the available security issues present in an IIoT system. We identify a list of security issues comprising logical, technological, and architectural points of view and consider the different IIoT security requirements. We also discuss the available IIoT architectures to examine these security concerns in a systematic way. We show how the functioning of different layers of an IIoT architecture is affected by various security issues and report a list of potential countermeasures against them. This study also presents a list of future research directions towards the development of a large-scale, secure, and trustworthy IIoT system. The study helps understand the various security issues by indicating various threats and attacks present in an IIoT system.

Automated and Controlled Data Collection Using Industrial IoT System for Smart Maintenance

Maintenance 4.0 is a concept that involves the use of IIoT (Industrial Internet of Things) technology to connect maintenance objects, which enables remote data collection, information exchange, analysis and potential improvement in productivity and efficiency, as well as planning maintenance activities. The purpose of this paper is to present the development of the Industrial Internet of Things data collection system, which relies on Tinkerforge IoT modules, that enables automated data collection alongside control of sensor and data collection parameters. To evaluate the ability of the system, an experiment was conducted where two equipment states were simulated using a rotational equipment failure simulator. The experiment determined that the presented IIoT system had successfully gathered information and that there is a clear distinction in acceleration patterns when simulating two different equipment states.

SISTEMA IOT INDUSTRIAL PARA MONITORAMENTO DE VIBRAÇÕES DE MÁQUINAS ROTATIVAS

Industrial IoT systems have been used to collect and analyze data from industrial processes. In this paper, we describe the development of a system containing IoT technologies to perform vibration monitoring of rotating machines for use in predictive maintenance systems. A two-axis vibration attached to a motor is used to generate the signal which is sent to a programmable logic controller using Modbus RTU protocol. Measurement history is stored in a MySQL relational database and presented through a dashboard using Grafana. Connection between controller and database is done by means a flow developed in Node-RED using OPC-UA protocol. Initial analysis were carried out using ranges of values in accordance with technical standards to establish tolerable levels of vibration. The system was successfully deployed and tested in a workbench; the methodology and results are reported in this paper.